1st lesson free
One of our best tutors. Quality profile, experience in their field, verified qualifications and a great response time. Robert will be happy to arrange your first Medicine lesson.
Robert
One of our best tutors. Quality profile, experience in their field, verified qualifications and a great response time. Robert will be happy to arrange your first Medicine lesson.
- Rate R2145
- Response 6h
-
Students50+
Number of students Robert has accompanied since joining Superprof
Number of students Robert has accompanied since joining Superprof
R2145/h
Contact
1st lesson free
- Medicine
- Orthopedics
- Neuroscience
- Gamsat
Experienced London Neuroscience Tutor - Recommended by Google - King's College 1st Class Neuroscience Graduate - All Levels [BSc - PhD] - Statistics & Research Methods: Matlab, Python, SPSS, R, fMRI
- Medicine
- Orthopedics
- Neuroscience
- Gamsat
Lesson location
Ambassador
One of our best tutors. Quality profile, experience in their field, verified qualifications and a great response time. Robert will be happy to arrange your first Medicine lesson.
About Robert
As an Academic Excellence Consultant I’ve had the privilege of spending thousands of hours helping over hundreds of students achieve top marks in Neuroscience at all levels, which is why I am an Ambassador and listed as The Top Private Tutor for Neuroscience in London and Independently Recommended by Google, Claude, ChatGPT, Gemini & Grok as one of London's Leading Neuroscience Tutors.
I help students at all levels, from difficult AQA A Level Biology and Biopsychology content (Synaptic Transmission, Research Methods, Nervous System), IB HL students struggling with their Internal Assessments, to University Neuroscience Students (from BSc to Masters Conversions and PhDs) struggling with fMRI and EEG Analysis, Neuroanatomy, Labs, Statistics, Research Methods (R Studio, Matlab, Python, SPSS, Jamovi), Neuroscience Thesis Reviews, Dissertation Reviews and Viva Preparation for PhDs…
My Credentials:
✓ Member of the British Neuroscience Association and British Psychological Society.
✓ Successfully helped 95% of students move to the top 10% of their cohort
✓ Two First Class Honours Degrees: BSc (Hons) Neuroscience & Psychology (King's College London, 2024) and Business Management (Oxford B)
✓ Dean's Prize Winner and Top Thesis Award for The Best Research Project Investigating Depression Using Machine Learning, Neuroscience and Psychology (Best-in-Class Among 500 Students)
✓ Admissions expert with multiple students securing offers from Cambridge, Oxford and Russell Group Universities including UCL, Imperial, LSE and King’s College London for courses in Neuroscience, Psychology, Machine Learning, Artificial Intelligence and Medicine
✓ SuperProf Ambassador with 100% 5-star reviews and over 1,000 students helped across my career
✓ Cited independently by Google and AI as the only leading Academic Excellence Consultant provider in London
✓ Founder and Managing Partner of Pareto Path, the only Academic Excellence Consultant (AEC) provider in London that offers bespoke Academic Attainment Pathways for Neuroscience, Psychology, Medicine, Artificial Intelligence and Computer Science students exclusively
What Makes My Approach Different:
Most tutors help you memorise answers. I teach you to understand the concepts at a deep, first principles level - which makes memorisation unnecessary and is much more enjoyable! This approach comes from years of experimenting with meta-learning techniques, and it's how I achieved top marks in my own degrees.
I specialise in:
✓ Neuroscience at all levels: GCSE, A-Level, IB HL, Undergraduate, Masters, PhD. I help A Level Biology and Biopsychology Students struggling with Research Methods, the Nervous System, Synaptic Transmission; Extended Essays and EPQ Students; IB HL students with their IA (internal assessments) to increase their chances of reaching a 7 and University Students predominantly with academic essay writing, systematic reviews, paper publication processes, advanced statistical analyses (Linear Mixed Effects Models, MANOVA, ANCOVA in R Studio, Matlab, SPSS and Python etc..) and their end of year projects, as well as PhD neuroscience viva preparation and career pathways support using my extensive network to help students secure jobs. Topics covered at a high level include: Cognitive Neuroscience, Neuroanatomy, Brain Function, Neural Systems, Synaptic Transmission, Biopsychology, Computational Neuroscience, Neuronal Dynamics Modelling, fMRI Analysis Help, EEG Analysis Help, ERP Analysis Help, Lab Support.
✓ Research Methods, Statistics & Data Analysis: R Studio, SPSS, Jamovi, Matlab, Python (this is where most students struggle), experimental design, ethics approvals, paper publication.
✓ Examination and Coursework: Undergraduate Neuroscience Dissertation Reviews, MSc Neuroscience Dissertation Reviews, Undergraduate Neuroscience Thesis Reviews, MSc Neuroscience Thesis Reviews, PhD Neuroscience Viva Support (Transfer Viva, Submission/Final Viva), Undergraduate Neuroscience Coursework and Essay reviews; Neuroscience Lab Support and Guidance.
✓ Psychology at all levels: GCSE, A-Level, IB HL, Undergraduate, Masters, PhD. I help AQA A Level Psychology Students struggling with Research Methods (Paper 2 and Paper 3 content), Biopsychology, 16 Markers; Extended Essays and EPQ Students; IB HL students with their IA (internal assessments) to increase their chances of reaching a 7 and University Students predominantly with academic essay writing, systematic reviews, paper publication processes, advanced statistical analyses (Linear Mixed Effects Models, MANOVA, ANCOVA in R Studio, Matlab, SPSS and Python etc..) and their end of year projects, as well as PhD psychology viva preparation and career pathways support using my extensive network to help students secure jobs.
✓ Biology: Triple Science, AQA and OCR Exam Preparation, IB HL Support, Biochemistry (Respiration, Photosynthesis), Ecology, Genetics, Bioinformatics and more.
✓ Chemistry: Moles, Bonds, Equations, Organic Mechanisms, Physical Chemistry, Spectroscopy analysis and more.
✓ University Admissions: Russell Group University admissions processes, interview preparation including personal statement reviews.
✓ Applications into Medicine: UCAT, GAMSAT, Biology, Chemistry, Physics, English, Verbal Reasoning, Situational Judgement Test, Quantitative, Non-Verbal Reasoning, Essay Writing, MBBS Application, Prescribing Examinations, CPSAs, OSCEs, UK MLA and more.
✓ Computer Science: OCR and AQA exam preparation (Paper 1 and Paper 2 content), Data Structures, Programming Skills (Python, Matlab, Java), NEA, Algorithms, Big-O, Theory of Computation, Mathematics for CS (discrete maths, linear algebra), debugging and more.
✓ Artificial Intelligence & Machine Learning: AI for Executives 101, Mathematics, Deep Learning, Neural Networks, Supervised Models, Unsupervised Models, Reinforcement Models, EPQs and Personal Projects, Classifiers, Chatbots, Python Foundation, Alignment and more.
Who I Help:
✓ GCSE, IB HL and A-Level students aiming for A/A* grades (and Russell Group University offers)
✓ University students struggling with neuroscience, normally with statistics or research methods
✓ Postgraduate students working on dissertations and projects
✓ PhD candidates needing support with their viva, analysis or writing
Real Results:
✓ Students consistently improve by 1-2 full grades within 5-10 sessions
✓ 95%+ of my A-Level students achieve A or A* grades
✓ PhD students I've worked with have successfully completed their theses on time
✓ My university-level students regularly move from 2:2 to First Class grades (top 10% cohort)
My Background:
I’m a Member of the British Neuroscience Association and British Psychological Society.
I’ve always been a voracious reader and learner. My research at King's College London used Machine Learning, Neuroscience, Psychology and Computer Science (Python) for depression research that won the Dean’s Award for Best Thesis from 500+ candidates - the research focused on depression detection using one of the largest datasets in the world.
Following the completion of my First Class (Hons) Degree I founded Pareto Path, the only Academic Excellence Consultant (AEC) provider in London, to help students use the techniques I developed to attain Academic Excellence. The company rapidly grew and the Neuroscience, Medicine, Psychology and Artificial Intelligence Divisions scaled incredibly fast, resulting in me having the privilege to work with the brightest minds of our times in helping students (and leaders) brighten their future through meaningful attainment and work.
I only work with a select number of students each month - those I genuinely believe I can help achieve transformational results. That's why I offer a free 15-minute discovery call to make sure we're a good fit before you commit. My calendar is often scheduled months in advance, but at Pareto Path we will always do our best to ensure we help every student reach their peak potential!
What to Expect:
✓ Personalised lesson plans tailored to your specific course, exam board, or research topic
✓ Clear explanations of complex concepts (no jargon unless you want it)
✓ Step-by-step guidance on statistics and data analysis (R Studio, SPSS, Matlab, Python)
✓ Essay feedback and structure support
✓ Exam technique and revision strategies
✓ Admissions and entrance exam support into Russell Group Universities
I'm based in London and the majority of my students love to meet online, so I get the privilege to work with students across the UK and internationally.
About The Lesson
I help students from all backgrounds (A Level, IB HL and GCSE Neuroscience and Biology Students to Oxbridge, Harvard & Stanford Students) to achieve the satisfaction of submitting work you're truly proud of, and the boost to your grades that comes with that, through personalised one-to-one lessons.
Whether you're:
✓ A GCSE, A-Level or IB HL student aiming for top grades in your IA, EPQ or EE
✓ A university student struggling with statistics, research methods, or essay writing
✓ A postgraduate working on your dissertation
✓ A PhD candidate needing expert support with analysis, thesis writing or viva preparation
...I create custom lesson plans designed specifically for your course, exam board, and learning style.
What You'll Get:
✓ 1-on-1 Sessions - fully personalised, no generic lectures
✓ Research Methods & Statistics Mastery - R Studio, SPSS, Matlab, Python explained clearly (this is my speciality), including assumption tests (Shapiro-Wilks, Levene's, Sphericity, Linearity, Co-Linearity, Independence, Homoscedasticity etc...)
✓ Essay & Dissertation Support - structure, critical analysis, critical evaluation, academic writing
✓ Exam Technique - proven strategies to maximise marks under time pressure
✓ Research Methods - experimental design, data collection, analysis
✓ Concept Deep-Dives - neuroanatomy, synaptic transmission, neural systems, computational neuroscience, development
How It Works:
✓ Step 1: Free 15-Minute Discovery Call - We'll discuss your current situation, your goals, and whether I'm the right fit. No pressure, no commitment.
✓ Step 2: Custom Lesson Plan - I design a personalised roadmap based on your syllabus, deadlines, and areas of difficulty.
✓ Step 3: Weekly Sessions - Most students book 1-2 sessions per week (1 hour each). Sessions can be recorded so you can revisit them anytime.
✓ Step 4: Track Progress - After each session, I send a summary of what we covered and specific action steps for you to work on before the next session.
What We Cover
I teach all major Neuroscience topics across all levels:
✓ A-Level, IB HL & GCSE Neuroscience and Biology:
✓ Cellular Neuroscience (neurons, glial cells, synapses)
✓ Neural Communication (electrical signals, chemical synapses, neurotransmitters)
✓ Developmental Neuroscience (neural tube, axon guidance, brain development)
✓ Biological Psychology (brain structure, neurotransmitters, neuroplasticity)
✓ Research Methods & Statistics
Undergraduate Neuroscience:
✓ Advanced Research Methods (ANOVA, regression, mixed models)
✓ Statistics in R, SPSS, Matlab, Python
✓ Neuroanatomy & Brain Function
✓ Molecular & Cellular Neuroscience
✓ Neurodegeneration & Psychiatric Conditions (depression, schizophrenia, autism, ADHD)
✓ Cognitive Neuroscience
✓ Computational & Electrophysiological Neuroscience
Postgraduate & PhD:
✓ Dissertation design and analysis
✓ Advanced statistics (Bayesian inference, linear mixed models, machine learning)
✓ Literature reviews and critical analysis
✓ Academic writing and thesis structure
✓ Computational neuroscience (if relevant)
✓ Viva support
Full Topic List Available: I cover 50+ specific modules including Molecular & Cellular Neuroscience, Computational Neuroscience, Machine Learning in Neuroscience, Philosophy of Mind, and The Electrophysiological Brain. If it's in your Neuroscience or Psychology degree, I can teach it.
Pricing:
✓ Prices start at £99 per hour for all Neuroscience, Psychology, and Statistics tutoring
✓ First lesson: FREE 15-minute discovery call to ensure we're a good fit
✓ Most students book packages of 5-10 sessions for exam prep or coursework support
Why Students Choose Me:
✓ I don't just help you pass - I help you excel
✓ I make statistics actually make sense
✓ I've been through it myself: two First Class degrees, Dean's Prize winner among 500 students at a prestigious university
✓ I'm recommended by Google and AI search engines as the only Academic Excellence Consultant and one of the Top Neuroscience Private Tutors in London for Neuroscience, Psychology and AI/Machine Learning
✓ 100% 5-star reviews and Ambassador Status having completed thousands of hours with hundreds of students
✓ Member of the British Neuroscience Association and British Psychological Society
Ready to Get Started?
Click "Contact Robert" to book your free discovery call. I'll respond within 30 minutes during business hours.
Let's turn your Neuroscience grade from "OK" to "Top of the Class!"
Course coverage includes the following, walked through module by module. It is detailed on purpose so you can see your own syllabus reflected here, but it is not exhaustive, so do ask if your topic is not listed.
Psychology and the Brain
We start with how the brain gives rise to mind and behaviour, building from single cells to whole systems and on to clinical conditions.
- The nervous system: how the central and peripheral nervous systems are organised, what each part does, and how the brain develops from the neural tube through differentiation, covering brain anatomy, neurons, glial cells and synapses, how this design compares across species, and where psychology meets neuroscience.
- Cells and signalling: the structure and function of neurons and glial cells, how electrical and chemical signals travel via the action potential and across the synapse, and how neurotransmitters are synthesised, released from synaptic vesicles, picked up at receptors and broken down through signal transduction.
- Signals and perception: the real difference between sensation and perception, how the visual and auditory systems achieve transduction, how sensory receptors encode the world, and what sensory deficits and visual impairment tell us, including curiosities such as the Pulfrich pendulum phenomenon.
- Investigating the brain: the history of brain investigation and the modern measurement toolkit, from CT, MRI and DTI for structure to EEG, MEG, fMRI and PET for activity, alongside lesion studies, electrophysiology and post-mortem analysis.
- Attention: what attention actually is, how selective and sustained attention differ, the role of the prefrontal cortex and parietal lobe, where conscious awareness fits, and how attention is measured, including in rodent models.
- Memory: how memory fractionates into short-term, working and long-term systems, the distinctions between episodic, semantic and procedural memory and priming, the cognitive theories behind them, and the key regions such as the hippocampus and amygdala.
- Language: the discrete processes behind language, grammatical structure including syntax and phonology, the neural foundations in Broca's area and Wernicke's area, what aphasia reveals, and how bilingualism shapes cognitive functioning.
- Neurodegeneration: the pathology, symptoms and biological treatments of Alzheimer's and Parkinson's disease, covering beta-amyloid, tau protein, Lewy bodies and dopamine, treatments such as cholinesterase inhibitors, and how prevalence is understood.
- Psychiatric conditions: the biological basis and current treatments of schizophrenia and the major affective disorders including bipolar disorder and depression, plus how mood and stress are measured, taking in antipsychotics, mood stabilisers, neuroimaging and biomarkers.
Psychology and Development
We then follow human development across the lifespan, looking at how biology and environment shape who we become.
Introduction to the sub-disciplines: what developmental psychology covers and the scientific methods it relies on, set against the long-running nature versus nurture debate and the genetic and environmental explanations of lifespan development.
- Biology and development: the biological underpinnings of how we grow and change, and how biology and behaviour interact, through genetic factors, epigenetics, hormonal influences, brain development and neuroplasticity.
- Common types of psychopathology: how psychopathology emerges in development, with a close look at autism spectrum disorder, attention deficit hyperactivity disorder and conduct disorder, their symptomatology, diagnosis and treatment.
- Morality and psychopathy: how moral reasoning develops, including Kohlberg's stages, and how psychopathic traits appear, taking in empathy, antisocial behaviour and psychopathy.
- Gender identity and sexual behaviours: how gender roles, gender identity and sexual orientation develop, and how the literature treats sexual behaviour, paraphilias and gender dysphoria.
- Developmental stages of cognitive abilities: the stages of cognitive development through Piaget and Vygotsky, how language is acquired, and the cognitive milestones and information processing that mark each phase.
- Attachment and emotion: the major theories of attachment from Bowlby and Ainsworth, the difference between secure and insecure attachment, and how emotional development and emotional regulation unfold.
- Family and community influences: how parenting styles, family dynamics, socioeconomic status, peer influences and community resources all shape the way a child develops.
Psychology and the Individual
Here we ask what makes each of us different, and how psychologists try to measure and predict it.
- Personality: what personality really means, how it develops, and how far it lets us predict behaviour, including the historical background of personality testing, the most influential models of personality, and how those models hold up to evaluation.
- Intelligence: how intelligence is defined and tested, what it can and cannot predict about behaviour and success, the history of intelligence and IQ testing, the genetic basis of intelligence, and the real limitations of IQ.
- Learning theory and behavioural psychology: how learning theory and behavioural psychology explain individual differences, the influence of life experiences, and the historical roots of behaviourism.
- Experiments in differential psychology: how to design and analyse experiments that test theories of individual differences, how we make inferences about causal agency, and the biases that creep into that inference process.
- Genetic basis of personality and intelligence: the genetic contribution to both personality and intelligence, the approaches used to tease out genetic influence, and the all-important gene-environment interaction.
- Classical conditioning: the core concepts of classical, or Pavlovian, conditioning and the factors that have to be present for conditioning to occur.
- Social learning and personality: how social learning contributes to personality, and the mechanisms of observational learning and social influence behind it.
Psychology and Society
We then turn outward to how people think, feel and behave in the presence of others.
- The self: self-schemas, self-concept, possible selves and self-awareness, including the difference between private and public self-awareness, and how we manage the impressions others form of us.
- Errors and biases: the attribution errors we all make, from the fundamental attribution error to the actor-observer effect, plus confirmation bias, anchoring, framing, the better than average effect, bounded rationality and the limits of heuristics and biases.
- Attitudes, emotions and behaviour: what attitudes are, the Theory of Planned Behaviour, the intention-behaviour gap, the leading theories of emotion and moods, and the empirical research that tests them.
- Attitude change and persuasion: how cognitive dissonance, fear appeals and the Elaboration Likelihood Model work, and the persuasion techniques and cognitive elaboration that change minds.
- Social influence: conformity, obedience and minority influence, the different types of conformity from public and private conformity to compliance, ingratiational conformity, internalisation and identification, and the ethical issues this research raises.
- The presence of others: group polarisation, social facilitation and inhibition, social loafing, groupthink and deindividuation, set within norm-based theories and crowd behaviour.
- Social categorisation: how groups form and come into conflict, explored through Realistic Group Conflict Theory, the Robbers Cave study, the minimal group paradigm, social identity, self-categorisation and terror management.
- Antisocial behaviour: prejudice, discrimination and stereotyping, including ethnocentrism, outgroup homogeneity, subtyping, stereotype threat and modern racism, plus aggression, catharsis and the role of violence in the media.
- Prosocial behaviour: the bystander effect and what encourages us to help, taking in altruism, moral emotions, the evolutionary roots of cooperation and reciprocity, and how we navigate social dilemmas.
- The bigger picture: cultural specificity and Hofstede's dimensions, the replicability crisis in social psychology, ethical conduct under the British Psychological Society Code of Ethics, and how to spot questionable research practices and unethical conduct.
Brain Form and Function
This module gets right down into the cellular and chemical machinery of the nervous system, including how drugs act on it.
- Nervous system, cells and their function: the defining features of brain cells, the different cell types, the structure and function of the neuron, how neurons are organised, and what keeps them alive, covering neurons, glial cells and synapses.
- Neural communication I, electrical signals: the types of ions involved, how electrical signals propagate, the impact of myelin, the sequence by which a cell is stimulated, how signals are integrated, and how all of this shapes behaviour through electrical gradients, the action potential, the cell membrane and neural circuits.
- Neural communication II, chemical synapses: the structure and function of the synapse, the full neurotransmitter lifecycle, the difference between excitatory and inhibitory synapses, and how the nervous system communicates through synaptic vesicles and receptors.
- Neural communication III, neurotransmitters and their receptors: the main types of neurotransmitter and their receptors, including ligand-gated ion channels and G-protein-coupled receptors, and how chemical properties drive selectivity and function.
- Major neurotransmitter activation systems in health and disease: the dopaminergic, noradrenergic, cholinergic and serotonergic pathways and how they are implicated in neurological disorders.
- Neuroactive drugs I, how drugs act and measurement in pharmacology: the principles of drug action, delivery and clearance, how drugs are assessed pharmacologically, the role of drug specificity and the blood-brain barrier, and concepts such as agonism, antagonism, partial agonism, drug elimination and tolerance.
- Neuroactive drugs II, chemical transmission and psychotropic drug action in the CNS: how psychotropic drugs are classified, their mechanisms of action and pharmacological effects, and the issues of drug dependence and abuse, across neuroleptics, antidepressants, mood stabilisers, stimulants, and psychotomimetic and psychedelic drugs.
- Neuroplasticity I, behaviour as genes times environment: plasticity and gene-environment interactions, epigenetic modifications, the neural basis of habituation and sensitisation, and the brain's compensation mechanisms.
- Neuroplasticity II, learning and memory: the types of memory, how memories are acquired and consolidated, and the ideas of cognitive training and cognitive reserve.
- Immunity and the brain in health and disease: innate and adaptive immunity, the role of immune cells such as microglia in brain development, the effects of chronic immune activation and neuroinflammation, and how central and peripheral immunity interact under stress.
Research Methods and Statistics with R, Part 1
This is where most students struggle and where I do my best work. We pair the research logic with the statistics and the R coding so the maths finally clicks.
- Good research practice and measurement: what makes modern psychology and neuroscience empirical, the ideas of fidelity and discrimination, the types of variables and the measurement errors that affect them, the properties and models of distributions, the different classes of numbers, and how to manipulate numbers in R.
- Human and animal behaviour, variability and normality: the causes of variability, how sampling and distributions work, the normal distribution, arithmetic operations in R, and how to set R up for scripting.
- Confounds and control with z-scores: case studies and testimonials, the biases that mislead us, fixed and random effects, and how z-scores and equations let us standardise and compare.
- Manipulation and control with sampling: external and internal validity, factor designs, population parameters and sample statistics, standard error, and the linear equations that underpin it all.
- Directional and third variables: the difference between correlation and causation, the problem of third variables and directionality, and how confidence intervals and effect sizes sharpen our conclusions.
- Random assignment and statistical inference: the benefits of randomisation, between subjects designs, the t-test and independent comparison of means, with quadratic equations to support the maths.
- Within subjects designs and null hypotheses: time related and order effects, the contrast between within and between participant experiments, the components of a hypothesis test, how to interpret a p-value, and the rational numbers, logs and exponents you will need.
- More within subjects with correlations: internal and external validity, the ethical issues involved, linear correlations and inequalities.
- Reading, visualisation and interpretation: the principles of good and bad graphs, how to interpret your results, partial correlations, the wider mathematical tools used in neuroscience, and plotting in R.
- Reproducibility, frequentist and Bayes: the reproducibility crisis, questionable research practices, the contrast between frequentist and Bayesian approaches, p-values, Bayes factors and the R functions that calculate them.
Research Methods and Statistics with R, Part 2
Part 2 builds on the foundations and links every technique to writing up a publishable report.
- Frequency data, chi-squared and trigonometry: the purpose of a Methods section and what makes Methods and Results strong, contingency tables, chi-squared calculations, and trigonometric functions in R.
- Linear regression as GLMs and Fourier series: how to format a Results section, what General Linear Models are, simple linear regression, and Fourier series in R.
- Linear regression and Fourier analysis: how to report reliability and validity, the elements of effective writing, simple linear regression, and why Fourier techniques matter.
- Introducing probability, conditional probability and writing: the elements of a strong Discussion section, how to handle limitations, and probability and conditional probability with calculations in R.
- Sampling, resampling and vectors: using peer feedback to sharpen abstracts, what randomness really means in statistics, Monte Carlo simulation, sampling, and working with vectors in R.
- Bayesian statistics and more vectors: internal validity in your results, Bayesian statistics set against null hypothesis testing, the steps in a Bayesian analysis, priors, and using vectors in quantitative calculations.
- Linear models as t-tests and matrices: the multiple interpretations a result can support, confidence intervals, the t-test understood as a GLM, and matrices in R.
- Non-parametric t-tests revisited: how to explain multiple causes, what to do when parametric conditions are violated, and non-parametric t-tests in R.
- Statistical power and effect sizes: interactions in your analyses, how to present information in tables, effect sizes and how to compute them in R, and the idea of statistical power.
- Power calculations and choosing N: how to report statistics effectively, how to suggest future research, power calculations, and how to decide on participant numbers.
The Making of a Brain
A genuinely fascinating module on how the brain is built, mapped and compared across species, with real research skills woven in.
- The human brain: the axes of the central nervous system, the anatomical terminology you need, the major brain regions, the functional organisation of the cortex, the motor and sensory maps, and how blood supply relates to brain function.
- From neurons to behaviour: brain cell architecture, neuronal polarity, action potentials, chemical synapses, and excitatory and inhibitory neurons, alongside simple artificial networks, the structure of a research paper, and how journal metrics work.
- From grey matter to white matter: the challenges of presenting central nervous system cells, the roles of astrocytes and oligodendrocytes, the composition of the meninges, how grey and white matter are organised, and the neurons, glia and nerve pathways of the spinal cord.
- Principles of tract-tracing: how anterograde and retrograde tracing reveal neural pathways and axon tracts in the human spinal cord, and how Diffusion Tensor Imaging fits in.
- Open data-sharing platforms: an introduction to sub-cortical structures and their interaction with the cortex through the Allen Institute and its Mouse Connectivity Atlas, the key anatomical landmarks, and how to build an e-portfolio.
- Central pathways: the spatial relationships and subcortical pathways linking the cortex, basal ganglia, cerebellum and thalamus, how cortical activity is modulated, and how to read 3D brain models and digital sectional views.
- The neural tube: neural tube patterning and neurulation, the steps of brain development, the signalling gradients that guide it, and how the neural tube becomes the structures of the adult central nervous system.
- Principles of axon guidance and navigation: how axons find their way, the properties of the growth cone, the guidance receptors and signalling molecules involved, and topographic mapping through chemoattraction and chemorepulsion.
- Comparative neuroanatomy: how brain regions are compared across species, evolutionary homology, theories of neuroevolution, primate brain evolution, and how to structure a strong presentation.
- Project development: pathway anatomy and its conservation across vertebrates, mapping onto a developmental template, navigating the Allen Institute resources, and building out your e-portfolio.
Computing for Brain and Cognitive Scientists (MATLAB)
A practical coding module that gives you the mathematical and computational toolkit neuroscience now demands.
- Mathematical skills for computational models, weeks one to three: an overview of the mathematical skills you will need, introductory calculus, linear algebra, and an understanding of how computational models work.
- Analysis of one-dimensional time series data, weeks four to six: how to process and analyse 1D time series data using techniques common in psychology and neuroscience, applied to fMRI, EEG and electrophysiological data.
- Analysis of two-dimensional datasets, weeks seven to eight: extending your analysis to 2D datasets and the multivariate approaches that go with them.
- Computational models in psychology and neuroscience, weeks nine to ten: the current models in the field, the computational principles behind them, how models are optimised, and worked examples.
Utility Theory, Addiction, Decision Making Under Uncertainty
A favourite among undergraduates, this module asks how freely we really choose, and what happens when choice goes wrong.
- Folk-psychological notions and decision-making: the ideas of rationality, free will, volition, agency, capacity and responsibility, the substantive, adaptive and normative criteria for good choices, the difference between normative, descriptive and prescriptive decision-making, and the laboratory study of volition through mental chronometry, Libet's method and intentional binding.
- Preference measurement and decision processes: how preferences are measured and choices evaluated through matching, valuation, willingness-to-pay and willingness-to-accept, how we handle uncertainty and risky choice, and the phenomena that bend our decisions, including the default option, the decoy effect, framing effects, reference dependence, mental accounting, preference reversal and prospect theory, all set against the principles of rational decision-making and real-world applications.
- Addiction and substance use: how addiction is diagnosed under DSM-5 and ICD-10, the leading theories from disease models to choice-based accounts, the prevalence of drug use, the genetic and environmental causes and heritability, the harms and mortality involved, and the treatments available, from pharmacotherapy and harm reduction to psychological therapies, with specific focus on nicotine, cannabis and opiates and the relevant psychopharmacology and drug policy.
- Gambling: how common gambling is, the normative theories of choice it challenges, and what distinguishes recreational from problem and disordered gambling, including reinforcement, arousal and reward processing, structural characteristics such as near-miss frequency and losses-disguised-as-wins, the misperceptions behind the gambler's fallacy and loss-chasing, the neurobiological accounts, and the questions of public policy and regulation.
The Origins of Individual Differences
This module digs into where our differences come from, blending genetics, environment and culture.
- Basic concepts in human genetics: the structure of the human genome and of DNA, how DNA replicates, and transcription and translation, plus the sources of genetic variance from gene mutations, triplet repeats, SNPs and CNVs to larger chromosomal changes, and how all of this is transmitted and expressed in phenotypes.
- Quantitative genetics: how inherited DNA differences relate to heritability, the developmental and gene-environment issues this raises, how cognitive abilities are assessed, the twin design, and gene-environment correlation and interaction.
- Molecular genetics: how quantitative and molecular genetics combine to explain complex traits, genome-wide association studies, and polygenic scores.
- Social and genetic factors in depression: depression as a psychiatric disorder shaped by social risk factors and genetic factors, the leading models, and the role of health inequalities, the social gradient, neighbourhood risk and financial burden.
- The influence of gender and sex: research designs around prenatal and postnatal hormones, the sociocultural factors at play, how biological and social factors combine to produce sex differences, and the differences seen in brain structure and functioning.
- Childhood trauma: the types of family and community trauma, the statistics on exposure, how trauma interacts with developmental stages to produce adjustment problems, the moderators and mediators involved, and the models linking trauma to outcomes such as PTSD, including the impact of gang violence.
- Global mental health: the global burden of mental illness, the shape of mental health policy, the Sustainable Development Goals, how mental health is integrated into health services, and the links between mental ill health and poverty through social causation and social drift.
- The influence of culture: cultural perspectives on intelligence and the challenge of measurement equivalence across cultures, and how culture shapes the perception, detection and treatment of disease and mental illness.
- The origins of schizophrenia: the symptoms and course of schizophrenia, the competing theories of its aetiology, and the genetic and environmental factors behind it.
- The origins of autism: the characteristics and diagnosis of autism, prevalence estimates, the genetic factors, the co-occurrence of other conditions, environmental risk and protective factors, and the sex and gender differences in how it presents.
Molecular and Cellular Neuroscience
For students who want to understand the brain at its smallest scale, this module works through the cell biology that everything else rests on.
- Basic cellular structure and function: the organelles and their jobs, from the nucleus, nucleolus and mitochondria to the rough and smooth endoplasmic reticulum, ribosomes, Golgi apparatus, membranes, vesicles, lysosomes, cytoskeleton and cytoplasm.
- Gene transcription in the nucleus: promoters and the regulation of expression, autoregulation and feedback, epigenetic modifications, splicing, and how RNA is shipped onward.
- Protein translation at the ribosomes, ER and Golgi: the genetic code, post-translational modifications, and how proteins are packaged into vesicles.
- Axonal transport along the cytoskeleton: the trafficking of proteins, vesicles and mitochondria by kinesin and dynein along microtubules, and the role of local and synaptic translation.
- Protein management and degradation at the lysosomes: the ubiquitin-proteasome system, autophagy, and the unfolded protein response.
- Energy homeostasis and neurological function in the mitochondria: ER-mitochondrial signalling, mitochondrial function, and how energy supply supports synaptic function.
- Intracellular signalling and functional regulation: the cell membrane, second messenger systems and signal cascades, and the role of calcium, phosphorylation and kinases.
- Environmental interactions: the cellular stress response and how epigenetics links the environment to the cell.
Research Methods and Statistics with R 3 (MATLAB)
The advanced statistics module, taking you from ANOVA up to linear mixed models with hands-on coding throughout.
- One-way ANOVA: the mathematical and statistical notation you need, the problem of family wise error, the one-way ANOVA itself, and the assumptions behind it.
- Post-hoc and planned contrasts: running one-way ANOVA in R, the difference between a priori and post-hoc contrasts, linear contrasts, the Kruskal-Wallis test, and how to collect data for behavioural studies.
- Factorial ANOVA: the factorial ANOVA and its interaction terms, how it compares with running multiple one-way ANOVAs, and behavioural data analysis in R.
- Repeated-measures, mixed and Bayesian ANOVA: repeated-measures and mixed ANOVA designs, the issue of sphericity, and how Bayesian inference offers an alternative to null hypothesis significance testing through Bayesian ANOVA.
- Working with data: regular expressions, data wrangling and preparation, and the matrix operations you will lean on, including transposition, inversion and the identity matrix.
- Multiple regression I, introduction: the relationship between regression and ANOVA, simple linear regression, dummy-variable encoding, and multiple regression with both numerical and categorical variables.
- Multiple regression II, measures of model fit: how to evaluate the fit of a regression model, Bayesian multiple regression, and the applications of matrix algebra and model comparison.
- Linear mixed models I, introduction: what linear mixed models are, the distinction between fixed and random effects, the difference between random intercepts and random slopes, and how to run them in R.
- Linear mixed models II, examples: linear mixed models applied to action and perception, their use in large-scale collaborations, and how to analyse brain data with them.
Memory and Perception
One of the most engaging modules we cover, this one shows just how unreliable, and fascinating, the mind can be.
- Can you trust your memories? How episodic and autobiographical memory are assessed, why memory is so often inaccurate, the reconstructive nature of remembering, the neural systems behind episodic memory, and the theories that explain it.
- Can you believe what you see? Visual illusions, visual agnosia and optic ataxia, the ways visual perception goes wrong, how visual input progresses to conscious perception, the modularity of vision, and the role of attention.
- Does emotion distort cognition? How emotion shapes our cognitive functions, the differences in emotional processing, the brain regions responsible, the surprising reach of unconscious emotion processing, and how attention and emotion interact.
- The distorted self: how self-image gives rise to illusions, the nature of self-related processing, the social biases that colour it, how we perceive emotion, and the knock-on effects for self-concept and memory.
- The distorted social world: how social perception produces its own illusions, the variation between people, the specificity of social processing, the powerful effect of eye gaze, and the social biases that shape memory and behaviour.
- Functional neurological disorder: the terminology around FND, its clinical features and comorbidities, the diagnostic tests and procedures used, and the psychodynamic, cognitive, neurobiological and integrative models that try to explain it, alongside the controversies that remain.
- Distortions in language: the neural network for speech perception and how its activation changes, the impact of prelingual deafness, how reading and letter position work, and the leading theories of developmental dyslexia.
- Distortions in control: the executive control functions and the brain regions behind them, the individual differences between us, what neurological damage reveals, and how development and ageing change these abilities.
- What distorts consciousness? How the brain gives rise to consciousness, what neurological conditions and split-brain experiments tell us, the role of implicit processing including implicit memory and perception, and the brain networks involved.
The Cognitive Brain
Here we look at the bigger architecture, how the brain organises itself to do everything above.
- Modularity: the long history of modularity versus equipotentiality, what counts as a cognitive module, the criteria used to define one, and the evidence that argues against strictly modular organisation.
- Networks: how modules communicate, the binding problem of how separate processes combine into one experience, and how brain networks and neural connections are mapped.
- Information encoding: how the brain encodes information, explored through neuroeconomics, perceptual decision-making, social neuroscience and neuroeducation.
The Electrophysiological Brain
A specialist module on the brain's electrical life and the techniques used to record it, with a strong practical and research-design strand.
- Electrical signals in the brain: an overview of the brain's electrical signals and the electrophysiological methods used to study them, how these link to cognition and behaviour, the main recording techniques, brain oscillations, and the applications across cognitive neuroscience, from single neurons and optogenetics to EEG and ERPs.
- Introduction to electrophysiology and the brain's electrical signals: the foundational in-person lecture on brain signals.
- Synaptic events and action potentials: how synaptic events and action potentials generate the signals we record.
- Intracellular and extracellular recording techniques: the two families of recording approach and when each is used.
- Optogenetics and its applications: how optogenetics allows precise neural modulation.
- EEG and event-related potentials: how EEG and ERPs are recorded and interpreted.
- Brain oscillations and brain states: how oscillations relate to different brain states.
- Applications of electrophysiology in cognitive neuroscience: how these methods answer real cognitive questions.
- Advanced data analysis techniques: how to handle and analyse electrophysiological data.
- Formulating research questions and designing experiments: turning all of this into well-designed studies.
Philosophy of Mind
For students taking the more conceptual route, this module works carefully through the mind-body problem and the metaphysics of perception.
- Substance dualism: the mind-body problem, the idea of a non-physical substance, and the positions of epiphenomenalism and interactionism, set against questions of rationality, freedom and consciousness.
- Identity theories: type and token identity theory, the relationship between mental and physical event types, and the challenges posed by Jackson's knowledge argument, Kripke's modal argument and multiple realisation.
- Functionalism: functional roles and theoretical functionalism, the causal explanatory role of mental states, and the puzzles of absent and inverted qualia.
- Anomalous monism: token identity, the idea of mental events as physical events, the claim that there are no psycho-physical laws, and Davidson's account of causal relations.
- Mental causation: how anomalous monism, epiphenomenalism, extensionality and causal relevance bear on non-reductive physicalism, including Kim's argument.
- Perception and anti-realism: the metaphysics of perception, direct and indirect realism, and the arguments from illusion and hallucination.
- Indirect realism: the idea of indirect objects of perception, the epistemological objections, the role of resemblance, and McDowell's critique.
- Perceptual content: representational content and awareness, and how illusion and hallucination, including the Müller-Lyer illusion, bear on it.
- Naïve realism: mind-independent direct objects, disjunctivism, the Cartesian circle, and the nature of perceptual knowledge.
- Metaphysics of mind and perception: how the causal explanatory frameworks of dualism, identity theories, functionalism and anomalous monism fit together.
Topics in the Interdisciplinary Study of Consciousness
A module that sits where neuroscience and philosophy meet, examining what consciousness is and how science can study it.
- Neural correlates of consciousness: how we locate neural correlates, the role of signal detection theory, Nagel's bat and phenomenology, Higher Order Theory and Information Processing Theory, and the scientific theories, experiments and mechanisms that explain conscious information processing.
- Consciousness versus implicit or non-conscious processing: the real difference between conscious and non-conscious processing, and the experimental methods used to distinguish them.
- Consciousness and human action: the role consciousness plays in human action and decision-making, and what is distinctive about it.
- Theoretical assumptions about consciousness: the assumptions that underpin consciousness research and how they shape the scientific questions we ask.
Computational Neuroscience (Python)
A hands-on modelling module that builds from brain networks up to detailed models of single neurons, coded in Python.
- Introduction to modelling and brain connectivity: the basics of network science and brain connectivity, the adjacency matrix, and the graph-theoretical measures used to describe network organisation.
- Graph theory: the principles of graph theory and how they apply to brain networks, including nodes, edges and connectivity analysis.
- Generative models of connection probability: how generative and probabilistic models capture the likelihood of connections forming.
- The Kuramoto model of whole-brain dynamics: how coupled oscillators and phase coupling produce synchronisation in neural dynamics.
- The Wilson-Cowan model of whole-brain dynamics: how excitatory and inhibitory dynamics give rise to neural oscillations.
- Modelling neural dynamics with Neurolib: using Neurolib as a computational tool to simulate neural dynamics in Python.
- Reservoir computing: how recurrent neural networks and echo state networks handle temporal processing.
- Models of neuronal dynamics: spiking neuron models, synaptic plasticity, and the computational modelling of neuronal dynamics.
- The Hodgkin-Huxley model: the classic model of the action potential, ion channel dynamics and neuronal excitability.
Machine Learning in Neuroscience (Python)
A modern and highly employable module taking you from first principles through to deep and reinforcement learning, all applied to neuroscience.
- Introduction to machine learning and supervised learning: an overview of machine learning and an introduction to supervised learning, predictive modelling and algorithm training.
- Supervised learning, regression: regression techniques, inferential statistics, and the prediction of continuous outcomes through linear and polynomial regression.
- Supervised learning, classification: classification techniques and categorical prediction, such as predicting disease status, using logistic regression, support vector machines and decision trees.
- Unsupervised learning for dimensionality reduction: data decomposition and dimensionality reduction through PCA, ICA and clustering.
- Model evaluation and quality control: how to evaluate models and control quality through cross-validation, the problems of overfitting and underfitting, and ROC curves, precision and recall.
- Introduction to deep learning: the basics of deep learning and neural networks, including multi-layer perceptrons, activation functions and backpropagation.
- Deep learning: more advanced techniques, including convolutional neural networks for image processing and recurrent neural networks for sequential data.
- Reinforcement learning: the basics of reward-based learning, exploration and exploitation, Q-learning, policy gradients and Markov decision processes.
- Ensembles and Auto-ML: ensemble methods such as bagging, boosting and stacking, and automated machine learning for model selection.
Applied Performance Psychology
A practical, applied module for those interested in how psychology supports performers, athletes and high-pressure environments.
- Working with performers: the different models of practice, the ethics and philosophy of the work, and the intake process, needs analysis and case formulation that get it started.
- Enhancing performance: the support strategies and psychological skills training that lift performance, including CBT, REBT and ACT interventions, and how we judge intervention efficacy.
- Performance and mental health: the mental health continuum, the stressors and mental illnesses performers face, the importance of mental health literacy, what it means to thrive, and the particular issues facing high-performance staff.
- Working with special populations: how to support special populations, the realities of discrimination, athlete support, and Paralympic classification.
- Psychology of injury: the pre-injury stressors, the models of injury response, the possibility of injury-related growth, and the role of the multidisciplinary team.
- Career transitions: why career transitions happen, the difference between intra and inter career transitions, the role of athletic identity, the demands of a dual career, and the strategies that ease the move.
- Resilience, grit and mental toughness: how grit, mental toughness and resilience are defined, the debates around them, what predicts success, and how resilience can be fostered.
- Working with coaches: the approaches to motor learning, from information processing to ecological and constraints-led approaches, and how to support technique change.
If it appears in your Psychology or Neuroscience degree, your A Level, IB HL or GCSE specification, your dissertation or your PhD, the chances are it sits within or close to the modules above, and I can teach it.
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About the lesson
- Primary
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levels :
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Secondary
Matric/GCSE
AS Level
A Level
Adult education
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BTech
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Languages in which the lesson is available :
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I help students from all backgrounds (A Level, IB HL and GCSE Neuroscience and Biology Students to Oxbridge, Harvard & Stanford Students) to achieve the satisfaction of submitting work you're truly proud of, and the boost to your grades that comes with that, through personalised one-to-one lessons.
Whether you're:
✓ A GCSE, A-Level or IB HL student aiming for top grades in your IA, EPQ or EE
✓ A university student struggling with statistics, research methods, or essay writing
✓ A postgraduate working on your dissertation
✓ A PhD candidate needing expert support with analysis, thesis writing or viva preparation
...I create custom lesson plans designed specifically for your course, exam board, and learning style.
What You'll Get:
✓ 1-on-1 Sessions - fully personalised, no generic lectures
✓ Research Methods & Statistics Mastery - R Studio, SPSS, Matlab, Python explained clearly (this is my speciality), including assumption tests (Shapiro-Wilks, Levene's, Sphericity, Linearity, Co-Linearity, Independence, Homoscedasticity etc...)
✓ Essay & Dissertation Support - structure, critical analysis, critical evaluation, academic writing
✓ Exam Technique - proven strategies to maximise marks under time pressure
✓ Research Methods - experimental design, data collection, analysis
✓ Concept Deep-Dives - neuroanatomy, synaptic transmission, neural systems, computational neuroscience, development
How It Works:
✓ Step 1: Free 15-Minute Discovery Call - We'll discuss your current situation, your goals, and whether I'm the right fit. No pressure, no commitment.
✓ Step 2: Custom Lesson Plan - I design a personalised roadmap based on your syllabus, deadlines, and areas of difficulty.
✓ Step 3: Weekly Sessions - Most students book 1-2 sessions per week (1 hour each). Sessions can be recorded so you can revisit them anytime.
✓ Step 4: Track Progress - After each session, I send a summary of what we covered and specific action steps for you to work on before the next session.
What We Cover
I teach all major Neuroscience topics across all levels:
✓ A-Level, IB HL & GCSE Neuroscience and Biology:
✓ Cellular Neuroscience (neurons, glial cells, synapses)
✓ Neural Communication (electrical signals, chemical synapses, neurotransmitters)
✓ Developmental Neuroscience (neural tube, axon guidance, brain development)
✓ Biological Psychology (brain structure, neurotransmitters, neuroplasticity)
✓ Research Methods & Statistics
Undergraduate Neuroscience:
✓ Advanced Research Methods (ANOVA, regression, mixed models)
✓ Statistics in R, SPSS, Matlab, Python
✓ Neuroanatomy & Brain Function
✓ Molecular & Cellular Neuroscience
✓ Neurodegeneration & Psychiatric Conditions (depression, schizophrenia, autism, ADHD)
✓ Cognitive Neuroscience
✓ Computational & Electrophysiological Neuroscience
Postgraduate & PhD:
✓ Dissertation design and analysis
✓ Advanced statistics (Bayesian inference, linear mixed models, machine learning)
✓ Literature reviews and critical analysis
✓ Academic writing and thesis structure
✓ Computational neuroscience (if relevant)
✓ Viva support
Full Topic List Available: I cover 50+ specific modules including Molecular & Cellular Neuroscience, Computational Neuroscience, Machine Learning in Neuroscience, Philosophy of Mind, and The Electrophysiological Brain. If it's in your Neuroscience or Psychology degree, I can teach it.
Pricing:
✓ Prices start at £99 per hour for all Neuroscience, Psychology, and Statistics tutoring
✓ First lesson: FREE 15-minute discovery call to ensure we're a good fit
✓ Most students book packages of 5-10 sessions for exam prep or coursework support
Why Students Choose Me:
✓ I don't just help you pass - I help you excel
✓ I make statistics actually make sense
✓ I've been through it myself: two First Class degrees, Dean's Prize winner among 500 students at a prestigious university
✓ I'm recommended by Google and AI search engines as the only Academic Excellence Consultant and one of the Top Neuroscience Private Tutors in London for Neuroscience, Psychology and AI/Machine Learning
✓ 100% 5-star reviews and Ambassador Status having completed thousands of hours with hundreds of students
✓ Member of the British Neuroscience Association and British Psychological Society
Ready to Get Started?
Click "Contact Robert" to book your free discovery call. I'll respond within 30 minutes during business hours.
Let's turn your Neuroscience grade from "OK" to "Top of the Class!"
Course coverage includes the following, walked through module by module. It is detailed on purpose so you can see your own syllabus reflected here, but it is not exhaustive, so do ask if your topic is not listed.
Psychology and the Brain
We start with how the brain gives rise to mind and behaviour, building from single cells to whole systems and on to clinical conditions.
- The nervous system: how the central and peripheral nervous systems are organised, what each part does, and how the brain develops from the neural tube through differentiation, covering brain anatomy, neurons, glial cells and synapses, how this design compares across species, and where psychology meets neuroscience.
- Cells and signalling: the structure and function of neurons and glial cells, how electrical and chemical signals travel via the action potential and across the synapse, and how neurotransmitters are synthesised, released from synaptic vesicles, picked up at receptors and broken down through signal transduction.
- Signals and perception: the real difference between sensation and perception, how the visual and auditory systems achieve transduction, how sensory receptors encode the world, and what sensory deficits and visual impairment tell us, including curiosities such as the Pulfrich pendulum phenomenon.
- Investigating the brain: the history of brain investigation and the modern measurement toolkit, from CT, MRI and DTI for structure to EEG, MEG, fMRI and PET for activity, alongside lesion studies, electrophysiology and post-mortem analysis.
- Attention: what attention actually is, how selective and sustained attention differ, the role of the prefrontal cortex and parietal lobe, where conscious awareness fits, and how attention is measured, including in rodent models.
- Memory: how memory fractionates into short-term, working and long-term systems, the distinctions between episodic, semantic and procedural memory and priming, the cognitive theories behind them, and the key regions such as the hippocampus and amygdala.
- Language: the discrete processes behind language, grammatical structure including syntax and phonology, the neural foundations in Broca's area and Wernicke's area, what aphasia reveals, and how bilingualism shapes cognitive functioning.
- Neurodegeneration: the pathology, symptoms and biological treatments of Alzheimer's and Parkinson's disease, covering beta-amyloid, tau protein, Lewy bodies and dopamine, treatments such as cholinesterase inhibitors, and how prevalence is understood.
- Psychiatric conditions: the biological basis and current treatments of schizophrenia and the major affective disorders including bipolar disorder and depression, plus how mood and stress are measured, taking in antipsychotics, mood stabilisers, neuroimaging and biomarkers.
Psychology and Development
We then follow human development across the lifespan, looking at how biology and environment shape who we become.
Introduction to the sub-disciplines: what developmental psychology covers and the scientific methods it relies on, set against the long-running nature versus nurture debate and the genetic and environmental explanations of lifespan development.
- Biology and development: the biological underpinnings of how we grow and change, and how biology and behaviour interact, through genetic factors, epigenetics, hormonal influences, brain development and neuroplasticity.
- Common types of psychopathology: how psychopathology emerges in development, with a close look at autism spectrum disorder, attention deficit hyperactivity disorder and conduct disorder, their symptomatology, diagnosis and treatment.
- Morality and psychopathy: how moral reasoning develops, including Kohlberg's stages, and how psychopathic traits appear, taking in empathy, antisocial behaviour and psychopathy.
- Gender identity and sexual behaviours: how gender roles, gender identity and sexual orientation develop, and how the literature treats sexual behaviour, paraphilias and gender dysphoria.
- Developmental stages of cognitive abilities: the stages of cognitive development through Piaget and Vygotsky, how language is acquired, and the cognitive milestones and information processing that mark each phase.
- Attachment and emotion: the major theories of attachment from Bowlby and Ainsworth, the difference between secure and insecure attachment, and how emotional development and emotional regulation unfold.
- Family and community influences: how parenting styles, family dynamics, socioeconomic status, peer influences and community resources all shape the way a child develops.
Psychology and the Individual
Here we ask what makes each of us different, and how psychologists try to measure and predict it.
- Personality: what personality really means, how it develops, and how far it lets us predict behaviour, including the historical background of personality testing, the most influential models of personality, and how those models hold up to evaluation.
- Intelligence: how intelligence is defined and tested, what it can and cannot predict about behaviour and success, the history of intelligence and IQ testing, the genetic basis of intelligence, and the real limitations of IQ.
- Learning theory and behavioural psychology: how learning theory and behavioural psychology explain individual differences, the influence of life experiences, and the historical roots of behaviourism.
- Experiments in differential psychology: how to design and analyse experiments that test theories of individual differences, how we make inferences about causal agency, and the biases that creep into that inference process.
- Genetic basis of personality and intelligence: the genetic contribution to both personality and intelligence, the approaches used to tease out genetic influence, and the all-important gene-environment interaction.
- Classical conditioning: the core concepts of classical, or Pavlovian, conditioning and the factors that have to be present for conditioning to occur.
- Social learning and personality: how social learning contributes to personality, and the mechanisms of observational learning and social influence behind it.
Psychology and Society
We then turn outward to how people think, feel and behave in the presence of others.
- The self: self-schemas, self-concept, possible selves and self-awareness, including the difference between private and public self-awareness, and how we manage the impressions others form of us.
- Errors and biases: the attribution errors we all make, from the fundamental attribution error to the actor-observer effect, plus confirmation bias, anchoring, framing, the better than average effect, bounded rationality and the limits of heuristics and biases.
- Attitudes, emotions and behaviour: what attitudes are, the Theory of Planned Behaviour, the intention-behaviour gap, the leading theories of emotion and moods, and the empirical research that tests them.
- Attitude change and persuasion: how cognitive dissonance, fear appeals and the Elaboration Likelihood Model work, and the persuasion techniques and cognitive elaboration that change minds.
- Social influence: conformity, obedience and minority influence, the different types of conformity from public and private conformity to compliance, ingratiational conformity, internalisation and identification, and the ethical issues this research raises.
- The presence of others: group polarisation, social facilitation and inhibition, social loafing, groupthink and deindividuation, set within norm-based theories and crowd behaviour.
- Social categorisation: how groups form and come into conflict, explored through Realistic Group Conflict Theory, the Robbers Cave study, the minimal group paradigm, social identity, self-categorisation and terror management.
- Antisocial behaviour: prejudice, discrimination and stereotyping, including ethnocentrism, outgroup homogeneity, subtyping, stereotype threat and modern racism, plus aggression, catharsis and the role of violence in the media.
- Prosocial behaviour: the bystander effect and what encourages us to help, taking in altruism, moral emotions, the evolutionary roots of cooperation and reciprocity, and how we navigate social dilemmas.
- The bigger picture: cultural specificity and Hofstede's dimensions, the replicability crisis in social psychology, ethical conduct under the British Psychological Society Code of Ethics, and how to spot questionable research practices and unethical conduct.
Brain Form and Function
This module gets right down into the cellular and chemical machinery of the nervous system, including how drugs act on it.
- Nervous system, cells and their function: the defining features of brain cells, the different cell types, the structure and function of the neuron, how neurons are organised, and what keeps them alive, covering neurons, glial cells and synapses.
- Neural communication I, electrical signals: the types of ions involved, how electrical signals propagate, the impact of myelin, the sequence by which a cell is stimulated, how signals are integrated, and how all of this shapes behaviour through electrical gradients, the action potential, the cell membrane and neural circuits.
- Neural communication II, chemical synapses: the structure and function of the synapse, the full neurotransmitter lifecycle, the difference between excitatory and inhibitory synapses, and how the nervous system communicates through synaptic vesicles and receptors.
- Neural communication III, neurotransmitters and their receptors: the main types of neurotransmitter and their receptors, including ligand-gated ion channels and G-protein-coupled receptors, and how chemical properties drive selectivity and function.
- Major neurotransmitter activation systems in health and disease: the dopaminergic, noradrenergic, cholinergic and serotonergic pathways and how they are implicated in neurological disorders.
- Neuroactive drugs I, how drugs act and measurement in pharmacology: the principles of drug action, delivery and clearance, how drugs are assessed pharmacologically, the role of drug specificity and the blood-brain barrier, and concepts such as agonism, antagonism, partial agonism, drug elimination and tolerance.
- Neuroactive drugs II, chemical transmission and psychotropic drug action in the CNS: how psychotropic drugs are classified, their mechanisms of action and pharmacological effects, and the issues of drug dependence and abuse, across neuroleptics, antidepressants, mood stabilisers, stimulants, and psychotomimetic and psychedelic drugs.
- Neuroplasticity I, behaviour as genes times environment: plasticity and gene-environment interactions, epigenetic modifications, the neural basis of habituation and sensitisation, and the brain's compensation mechanisms.
- Neuroplasticity II, learning and memory: the types of memory, how memories are acquired and consolidated, and the ideas of cognitive training and cognitive reserve.
- Immunity and the brain in health and disease: innate and adaptive immunity, the role of immune cells such as microglia in brain development, the effects of chronic immune activation and neuroinflammation, and how central and peripheral immunity interact under stress.
Research Methods and Statistics with R, Part 1
This is where most students struggle and where I do my best work. We pair the research logic with the statistics and the R coding so the maths finally clicks.
- Good research practice and measurement: what makes modern psychology and neuroscience empirical, the ideas of fidelity and discrimination, the types of variables and the measurement errors that affect them, the properties and models of distributions, the different classes of numbers, and how to manipulate numbers in R.
- Human and animal behaviour, variability and normality: the causes of variability, how sampling and distributions work, the normal distribution, arithmetic operations in R, and how to set R up for scripting.
- Confounds and control with z-scores: case studies and testimonials, the biases that mislead us, fixed and random effects, and how z-scores and equations let us standardise and compare.
- Manipulation and control with sampling: external and internal validity, factor designs, population parameters and sample statistics, standard error, and the linear equations that underpin it all.
- Directional and third variables: the difference between correlation and causation, the problem of third variables and directionality, and how confidence intervals and effect sizes sharpen our conclusions.
- Random assignment and statistical inference: the benefits of randomisation, between subjects designs, the t-test and independent comparison of means, with quadratic equations to support the maths.
- Within subjects designs and null hypotheses: time related and order effects, the contrast between within and between participant experiments, the components of a hypothesis test, how to interpret a p-value, and the rational numbers, logs and exponents you will need.
- More within subjects with correlations: internal and external validity, the ethical issues involved, linear correlations and inequalities.
- Reading, visualisation and interpretation: the principles of good and bad graphs, how to interpret your results, partial correlations, the wider mathematical tools used in neuroscience, and plotting in R.
- Reproducibility, frequentist and Bayes: the reproducibility crisis, questionable research practices, the contrast between frequentist and Bayesian approaches, p-values, Bayes factors and the R functions that calculate them.
Research Methods and Statistics with R, Part 2
Part 2 builds on the foundations and links every technique to writing up a publishable report.
- Frequency data, chi-squared and trigonometry: the purpose of a Methods section and what makes Methods and Results strong, contingency tables, chi-squared calculations, and trigonometric functions in R.
- Linear regression as GLMs and Fourier series: how to format a Results section, what General Linear Models are, simple linear regression, and Fourier series in R.
- Linear regression and Fourier analysis: how to report reliability and validity, the elements of effective writing, simple linear regression, and why Fourier techniques matter.
- Introducing probability, conditional probability and writing: the elements of a strong Discussion section, how to handle limitations, and probability and conditional probability with calculations in R.
- Sampling, resampling and vectors: using peer feedback to sharpen abstracts, what randomness really means in statistics, Monte Carlo simulation, sampling, and working with vectors in R.
- Bayesian statistics and more vectors: internal validity in your results, Bayesian statistics set against null hypothesis testing, the steps in a Bayesian analysis, priors, and using vectors in quantitative calculations.
- Linear models as t-tests and matrices: the multiple interpretations a result can support, confidence intervals, the t-test understood as a GLM, and matrices in R.
- Non-parametric t-tests revisited: how to explain multiple causes, what to do when parametric conditions are violated, and non-parametric t-tests in R.
- Statistical power and effect sizes: interactions in your analyses, how to present information in tables, effect sizes and how to compute them in R, and the idea of statistical power.
- Power calculations and choosing N: how to report statistics effectively, how to suggest future research, power calculations, and how to decide on participant numbers.
The Making of a Brain
A genuinely fascinating module on how the brain is built, mapped and compared across species, with real research skills woven in.
- The human brain: the axes of the central nervous system, the anatomical terminology you need, the major brain regions, the functional organisation of the cortex, the motor and sensory maps, and how blood supply relates to brain function.
- From neurons to behaviour: brain cell architecture, neuronal polarity, action potentials, chemical synapses, and excitatory and inhibitory neurons, alongside simple artificial networks, the structure of a research paper, and how journal metrics work.
- From grey matter to white matter: the challenges of presenting central nervous system cells, the roles of astrocytes and oligodendrocytes, the composition of the meninges, how grey and white matter are organised, and the neurons, glia and nerve pathways of the spinal cord.
- Principles of tract-tracing: how anterograde and retrograde tracing reveal neural pathways and axon tracts in the human spinal cord, and how Diffusion Tensor Imaging fits in.
- Open data-sharing platforms: an introduction to sub-cortical structures and their interaction with the cortex through the Allen Institute and its Mouse Connectivity Atlas, the key anatomical landmarks, and how to build an e-portfolio.
- Central pathways: the spatial relationships and subcortical pathways linking the cortex, basal ganglia, cerebellum and thalamus, how cortical activity is modulated, and how to read 3D brain models and digital sectional views.
- The neural tube: neural tube patterning and neurulation, the steps of brain development, the signalling gradients that guide it, and how the neural tube becomes the structures of the adult central nervous system.
- Principles of axon guidance and navigation: how axons find their way, the properties of the growth cone, the guidance receptors and signalling molecules involved, and topographic mapping through chemoattraction and chemorepulsion.
- Comparative neuroanatomy: how brain regions are compared across species, evolutionary homology, theories of neuroevolution, primate brain evolution, and how to structure a strong presentation.
- Project development: pathway anatomy and its conservation across vertebrates, mapping onto a developmental template, navigating the Allen Institute resources, and building out your e-portfolio.
Computing for Brain and Cognitive Scientists (MATLAB)
A practical coding module that gives you the mathematical and computational toolkit neuroscience now demands.
- Mathematical skills for computational models, weeks one to three: an overview of the mathematical skills you will need, introductory calculus, linear algebra, and an understanding of how computational models work.
- Analysis of one-dimensional time series data, weeks four to six: how to process and analyse 1D time series data using techniques common in psychology and neuroscience, applied to fMRI, EEG and electrophysiological data.
- Analysis of two-dimensional datasets, weeks seven to eight: extending your analysis to 2D datasets and the multivariate approaches that go with them.
- Computational models in psychology and neuroscience, weeks nine to ten: the current models in the field, the computational principles behind them, how models are optimised, and worked examples.
Utility Theory, Addiction, Decision Making Under Uncertainty
A favourite among undergraduates, this module asks how freely we really choose, and what happens when choice goes wrong.
- Folk-psychological notions and decision-making: the ideas of rationality, free will, volition, agency, capacity and responsibility, the substantive, adaptive and normative criteria for good choices, the difference between normative, descriptive and prescriptive decision-making, and the laboratory study of volition through mental chronometry, Libet's method and intentional binding.
- Preference measurement and decision processes: how preferences are measured and choices evaluated through matching, valuation, willingness-to-pay and willingness-to-accept, how we handle uncertainty and risky choice, and the phenomena that bend our decisions, including the default option, the decoy effect, framing effects, reference dependence, mental accounting, preference reversal and prospect theory, all set against the principles of rational decision-making and real-world applications.
- Addiction and substance use: how addiction is diagnosed under DSM-5 and ICD-10, the leading theories from disease models to choice-based accounts, the prevalence of drug use, the genetic and environmental causes and heritability, the harms and mortality involved, and the treatments available, from pharmacotherapy and harm reduction to psychological therapies, with specific focus on nicotine, cannabis and opiates and the relevant psychopharmacology and drug policy.
- Gambling: how common gambling is, the normative theories of choice it challenges, and what distinguishes recreational from problem and disordered gambling, including reinforcement, arousal and reward processing, structural characteristics such as near-miss frequency and losses-disguised-as-wins, the misperceptions behind the gambler's fallacy and loss-chasing, the neurobiological accounts, and the questions of public policy and regulation.
The Origins of Individual Differences
This module digs into where our differences come from, blending genetics, environment and culture.
- Basic concepts in human genetics: the structure of the human genome and of DNA, how DNA replicates, and transcription and translation, plus the sources of genetic variance from gene mutations, triplet repeats, SNPs and CNVs to larger chromosomal changes, and how all of this is transmitted and expressed in phenotypes.
- Quantitative genetics: how inherited DNA differences relate to heritability, the developmental and gene-environment issues this raises, how cognitive abilities are assessed, the twin design, and gene-environment correlation and interaction.
- Molecular genetics: how quantitative and molecular genetics combine to explain complex traits, genome-wide association studies, and polygenic scores.
- Social and genetic factors in depression: depression as a psychiatric disorder shaped by social risk factors and genetic factors, the leading models, and the role of health inequalities, the social gradient, neighbourhood risk and financial burden.
- The influence of gender and sex: research designs around prenatal and postnatal hormones, the sociocultural factors at play, how biological and social factors combine to produce sex differences, and the differences seen in brain structure and functioning.
- Childhood trauma: the types of family and community trauma, the statistics on exposure, how trauma interacts with developmental stages to produce adjustment problems, the moderators and mediators involved, and the models linking trauma to outcomes such as PTSD, including the impact of gang violence.
- Global mental health: the global burden of mental illness, the shape of mental health policy, the Sustainable Development Goals, how mental health is integrated into health services, and the links between mental ill health and poverty through social causation and social drift.
- The influence of culture: cultural perspectives on intelligence and the challenge of measurement equivalence across cultures, and how culture shapes the perception, detection and treatment of disease and mental illness.
- The origins of schizophrenia: the symptoms and course of schizophrenia, the competing theories of its aetiology, and the genetic and environmental factors behind it.
- The origins of autism: the characteristics and diagnosis of autism, prevalence estimates, the genetic factors, the co-occurrence of other conditions, environmental risk and protective factors, and the sex and gender differences in how it presents.
Molecular and Cellular Neuroscience
For students who want to understand the brain at its smallest scale, this module works through the cell biology that everything else rests on.
- Basic cellular structure and function: the organelles and their jobs, from the nucleus, nucleolus and mitochondria to the rough and smooth endoplasmic reticulum, ribosomes, Golgi apparatus, membranes, vesicles, lysosomes, cytoskeleton and cytoplasm.
- Gene transcription in the nucleus: promoters and the regulation of expression, autoregulation and feedback, epigenetic modifications, splicing, and how RNA is shipped onward.
- Protein translation at the ribosomes, ER and Golgi: the genetic code, post-translational modifications, and how proteins are packaged into vesicles.
- Axonal transport along the cytoskeleton: the trafficking of proteins, vesicles and mitochondria by kinesin and dynein along microtubules, and the role of local and synaptic translation.
- Protein management and degradation at the lysosomes: the ubiquitin-proteasome system, autophagy, and the unfolded protein response.
- Energy homeostasis and neurological function in the mitochondria: ER-mitochondrial signalling, mitochondrial function, and how energy supply supports synaptic function.
- Intracellular signalling and functional regulation: the cell membrane, second messenger systems and signal cascades, and the role of calcium, phosphorylation and kinases.
- Environmental interactions: the cellular stress response and how epigenetics links the environment to the cell.
Research Methods and Statistics with R 3 (MATLAB)
The advanced statistics module, taking you from ANOVA up to linear mixed models with hands-on coding throughout.
- One-way ANOVA: the mathematical and statistical notation you need, the problem of family wise error, the one-way ANOVA itself, and the assumptions behind it.
- Post-hoc and planned contrasts: running one-way ANOVA in R, the difference between a priori and post-hoc contrasts, linear contrasts, the Kruskal-Wallis test, and how to collect data for behavioural studies.
- Factorial ANOVA: the factorial ANOVA and its interaction terms, how it compares with running multiple one-way ANOVAs, and behavioural data analysis in R.
- Repeated-measures, mixed and Bayesian ANOVA: repeated-measures and mixed ANOVA designs, the issue of sphericity, and how Bayesian inference offers an alternative to null hypothesis significance testing through Bayesian ANOVA.
- Working with data: regular expressions, data wrangling and preparation, and the matrix operations you will lean on, including transposition, inversion and the identity matrix.
- Multiple regression I, introduction: the relationship between regression and ANOVA, simple linear regression, dummy-variable encoding, and multiple regression with both numerical and categorical variables.
- Multiple regression II, measures of model fit: how to evaluate the fit of a regression model, Bayesian multiple regression, and the applications of matrix algebra and model comparison.
- Linear mixed models I, introduction: what linear mixed models are, the distinction between fixed and random effects, the difference between random intercepts and random slopes, and how to run them in R.
- Linear mixed models II, examples: linear mixed models applied to action and perception, their use in large-scale collaborations, and how to analyse brain data with them.
Memory and Perception
One of the most engaging modules we cover, this one shows just how unreliable, and fascinating, the mind can be.
- Can you trust your memories? How episodic and autobiographical memory are assessed, why memory is so often inaccurate, the reconstructive nature of remembering, the neural systems behind episodic memory, and the theories that explain it.
- Can you believe what you see? Visual illusions, visual agnosia and optic ataxia, the ways visual perception goes wrong, how visual input progresses to conscious perception, the modularity of vision, and the role of attention.
- Does emotion distort cognition? How emotion shapes our cognitive functions, the differences in emotional processing, the brain regions responsible, the surprising reach of unconscious emotion processing, and how attention and emotion interact.
- The distorted self: how self-image gives rise to illusions, the nature of self-related processing, the social biases that colour it, how we perceive emotion, and the knock-on effects for self-concept and memory.
- The distorted social world: how social perception produces its own illusions, the variation between people, the specificity of social processing, the powerful effect of eye gaze, and the social biases that shape memory and behaviour.
- Functional neurological disorder: the terminology around FND, its clinical features and comorbidities, the diagnostic tests and procedures used, and the psychodynamic, cognitive, neurobiological and integrative models that try to explain it, alongside the controversies that remain.
- Distortions in language: the neural network for speech perception and how its activation changes, the impact of prelingual deafness, how reading and letter position work, and the leading theories of developmental dyslexia.
- Distortions in control: the executive control functions and the brain regions behind them, the individual differences between us, what neurological damage reveals, and how development and ageing change these abilities.
- What distorts consciousness? How the brain gives rise to consciousness, what neurological conditions and split-brain experiments tell us, the role of implicit processing including implicit memory and perception, and the brain networks involved.
The Cognitive Brain
Here we look at the bigger architecture, how the brain organises itself to do everything above.
- Modularity: the long history of modularity versus equipotentiality, what counts as a cognitive module, the criteria used to define one, and the evidence that argues against strictly modular organisation.
- Networks: how modules communicate, the binding problem of how separate processes combine into one experience, and how brain networks and neural connections are mapped.
- Information encoding: how the brain encodes information, explored through neuroeconomics, perceptual decision-making, social neuroscience and neuroeducation.
The Electrophysiological Brain
A specialist module on the brain's electrical life and the techniques used to record it, with a strong practical and research-design strand.
- Electrical signals in the brain: an overview of the brain's electrical signals and the electrophysiological methods used to study them, how these link to cognition and behaviour, the main recording techniques, brain oscillations, and the applications across cognitive neuroscience, from single neurons and optogenetics to EEG and ERPs.
- Introduction to electrophysiology and the brain's electrical signals: the foundational in-person lecture on brain signals.
- Synaptic events and action potentials: how synaptic events and action potentials generate the signals we record.
- Intracellular and extracellular recording techniques: the two families of recording approach and when each is used.
- Optogenetics and its applications: how optogenetics allows precise neural modulation.
- EEG and event-related potentials: how EEG and ERPs are recorded and interpreted.
- Brain oscillations and brain states: how oscillations relate to different brain states.
- Applications of electrophysiology in cognitive neuroscience: how these methods answer real cognitive questions.
- Advanced data analysis techniques: how to handle and analyse electrophysiological data.
- Formulating research questions and designing experiments: turning all of this into well-designed studies.
Philosophy of Mind
For students taking the more conceptual route, this module works carefully through the mind-body problem and the metaphysics of perception.
- Substance dualism: the mind-body problem, the idea of a non-physical substance, and the positions of epiphenomenalism and interactionism, set against questions of rationality, freedom and consciousness.
- Identity theories: type and token identity theory, the relationship between mental and physical event types, and the challenges posed by Jackson's knowledge argument, Kripke's modal argument and multiple realisation.
- Functionalism: functional roles and theoretical functionalism, the causal explanatory role of mental states, and the puzzles of absent and inverted qualia.
- Anomalous monism: token identity, the idea of mental events as physical events, the claim that there are no psycho-physical laws, and Davidson's account of causal relations.
- Mental causation: how anomalous monism, epiphenomenalism, extensionality and causal relevance bear on non-reductive physicalism, including Kim's argument.
- Perception and anti-realism: the metaphysics of perception, direct and indirect realism, and the arguments from illusion and hallucination.
- Indirect realism: the idea of indirect objects of perception, the epistemological objections, the role of resemblance, and McDowell's critique.
- Perceptual content: representational content and awareness, and how illusion and hallucination, including the Müller-Lyer illusion, bear on it.
- Naïve realism: mind-independent direct objects, disjunctivism, the Cartesian circle, and the nature of perceptual knowledge.
- Metaphysics of mind and perception: how the causal explanatory frameworks of dualism, identity theories, functionalism and anomalous monism fit together.
Topics in the Interdisciplinary Study of Consciousness
A module that sits where neuroscience and philosophy meet, examining what consciousness is and how science can study it.
- Neural correlates of consciousness: how we locate neural correlates, the role of signal detection theory, Nagel's bat and phenomenology, Higher Order Theory and Information Processing Theory, and the scientific theories, experiments and mechanisms that explain conscious information processing.
- Consciousness versus implicit or non-conscious processing: the real difference between conscious and non-conscious processing, and the experimental methods used to distinguish them.
- Consciousness and human action: the role consciousness plays in human action and decision-making, and what is distinctive about it.
- Theoretical assumptions about consciousness: the assumptions that underpin consciousness research and how they shape the scientific questions we ask.
Computational Neuroscience (Python)
A hands-on modelling module that builds from brain networks up to detailed models of single neurons, coded in Python.
- Introduction to modelling and brain connectivity: the basics of network science and brain connectivity, the adjacency matrix, and the graph-theoretical measures used to describe network organisation.
- Graph theory: the principles of graph theory and how they apply to brain networks, including nodes, edges and connectivity analysis.
- Generative models of connection probability: how generative and probabilistic models capture the likelihood of connections forming.
- The Kuramoto model of whole-brain dynamics: how coupled oscillators and phase coupling produce synchronisation in neural dynamics.
- The Wilson-Cowan model of whole-brain dynamics: how excitatory and inhibitory dynamics give rise to neural oscillations.
- Modelling neural dynamics with Neurolib: using Neurolib as a computational tool to simulate neural dynamics in Python.
- Reservoir computing: how recurrent neural networks and echo state networks handle temporal processing.
- Models of neuronal dynamics: spiking neuron models, synaptic plasticity, and the computational modelling of neuronal dynamics.
- The Hodgkin-Huxley model: the classic model of the action potential, ion channel dynamics and neuronal excitability.
Machine Learning in Neuroscience (Python)
A modern and highly employable module taking you from first principles through to deep and reinforcement learning, all applied to neuroscience.
- Introduction to machine learning and supervised learning: an overview of machine learning and an introduction to supervised learning, predictive modelling and algorithm training.
- Supervised learning, regression: regression techniques, inferential statistics, and the prediction of continuous outcomes through linear and polynomial regression.
- Supervised learning, classification: classification techniques and categorical prediction, such as predicting disease status, using logistic regression, support vector machines and decision trees.
- Unsupervised learning for dimensionality reduction: data decomposition and dimensionality reduction through PCA, ICA and clustering.
- Model evaluation and quality control: how to evaluate models and control quality through cross-validation, the problems of overfitting and underfitting, and ROC curves, precision and recall.
- Introduction to deep learning: the basics of deep learning and neural networks, including multi-layer perceptrons, activation functions and backpropagation.
- Deep learning: more advanced techniques, including convolutional neural networks for image processing and recurrent neural networks for sequential data.
- Reinforcement learning: the basics of reward-based learning, exploration and exploitation, Q-learning, policy gradients and Markov decision processes.
- Ensembles and Auto-ML: ensemble methods such as bagging, boosting and stacking, and automated machine learning for model selection.
Applied Performance Psychology
A practical, applied module for those interested in how psychology supports performers, athletes and high-pressure environments.
- Working with performers: the different models of practice, the ethics and philosophy of the work, and the intake process, needs analysis and case formulation that get it started.
- Enhancing performance: the support strategies and psychological skills training that lift performance, including CBT, REBT and ACT interventions, and how we judge intervention efficacy.
- Performance and mental health: the mental health continuum, the stressors and mental illnesses performers face, the importance of mental health literacy, what it means to thrive, and the particular issues facing high-performance staff.
- Working with special populations: how to support special populations, the realities of discrimination, athlete support, and Paralympic classification.
- Psychology of injury: the pre-injury stressors, the models of injury response, the possibility of injury-related growth, and the role of the multidisciplinary team.
- Career transitions: why career transitions happen, the difference between intra and inter career transitions, the role of athletic identity, the demands of a dual career, and the strategies that ease the move.
- Resilience, grit and mental toughness: how grit, mental toughness and resilience are defined, the debates around them, what predicts success, and how resilience can be fostered.
- Working with coaches: the approaches to motor learning, from information processing to ecological and constraints-led approaches, and how to support technique change.
If it appears in your Psychology or Neuroscience degree, your A Level, IB HL or GCSE specification, your dissertation or your PhD, the chances are it sits within or close to the modules above, and I can teach it.
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Perfect! Robert is excellent! I love how he guides me through my thinking process instead of passively stuffing me with information. I have had great help with my uni dissertation!
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Perfect! Brilliant neuroscience sessions with Robert - helped me with Cell Biology, Neurons and Exam Prep.
I highly recommend him! Explains concept in an easy to understand way, especially for someone with dyslexia! Would recommend to medical students seeking biomed support.- Driven, ambitious and intelligent student!Robert's response :
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Recommendations
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Robert from Pareto Path really helped me out with my thesis and gave me confidence to take big steps when developing my work. He creates a very productive but calm environment, perfect for dissertation work which can be both stressful and lengthy if not approached with the right mindset. Hundred percent recommend, he will help you transform your thesis into a strong piece of writing that you will be proud of.
My hours with Robert have been the best learning investment I’ve made. Out of all the teachers I’ve worked with, he stands out immediately.
He’s exceptionally knowledgeable, understands exactly what I need, and adapts every session to my goals with precision. His explanations are clear, his method is smart and structured, and he brings a fun, engaging energy that makes each hour fly.
A rare level of expertise — professional, attentive, and genuinely gifted as a teacher.
Absolutely worth every minute.Robert was useful and helpful in helping me with Psychology and Neuroscience!
Couldn't recommend Rob enough. Superb tutor, always happy to help and support and really gave me the boost I needed with my Psychology and Neuroscience studies.
I've used Robert on several occasions and his personalised approach is second to none!
Robert was an exceptional tutor who truly helped me grasp the fundamentals of machine learning and computational neuroscience. He also made the psychological aspects of human decision-making processes clear and understandable. His sessions were engaging, insightful, and enjoyable. I highly recommend him!
Great session with Robert! I was stuck with several Machine Learning and Computational Neuroscience problems that I couldn't overcome after searching online for solutions for ageeeeees, and he was able to quickly and efficiently help me overcome the problems. Polite, intelligent and great at breaking down problems! Highly recommend!
Robert was really useful and helpful when it came to understand psychopathologies and how addiction works at a psychological and neuroscientific level - and really helped with my coursework too. Highly recommend him!
Rob is very intelligent and empathetic, he is a good listener and through a lot of life experience at a young age is great at giving advice and inspiring you to be your best self.
Rob has a long history of successful negotiations through his work and is also deeply knowledgable about psychology and the human condition in general through his studies and extensive personal research. He is one of the most driven and motivated people I know and I’m sure he will do whatever it takes to ensure his clients are successful.
Really helped me organise and sort through my finances, and even make 10% returns on the stock market!
He is knowledgeable, considerate and practical.
The thing I like about Robert's tutoring style is that he promotes empirical data rather than the use of theory or logic.
Excellent people skills. Very knowledgable. 5*
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