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Oliver
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PhD Research Methods & Statistics London Tutor - King's College PhD Researcher in Computational Neuroscience - All Levels: A Level (AQA/OCR), IB, GCSE & University (BSc - PhD): Python, SPSS, R, MatLab
- Math
- Statistics
- SVT
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About Oliver
I'm Oliver, an Academic Excellence Consultant at Pareto Path and a PhD researcher in Computational Neuroscience at King's College London, and I help students master Statistics, Research Methods and the mathematics behind them, alongside Neuroscience, Psychology, Computer Science and Artificial Intelligence and Machine Learning, with particular depth in SPSS, R, Python and Jamovi, experimental design and data analysis. I teach at university level at King's College London and tutor students from GCSE and A Level through to undergraduate, postgraduate and PhD, online and in person in Central London.
Where most students struggle is not the theory but the research methods, statistics and the mathematics behind them, and that is exactly where I do my best work. As a published researcher who uses statistics, R, Python and machine learning in real research every day, I teach these topics from genuine practice rather than from a textbook, which is what makes them finally click...
My Credentials:
✓ PhD researcher in Computational Neuroscience at King's College London, funded by a competitive MRC studentship
✓ MSc Basic & Clinical Neuroscience, King's College London (passed with Distinction)
✓ MRes Neuroimaging, King's College London, MRC-DTP (passed with Distinction)
✓ BSc (Hons) Biomedical Science, St George's, University of London (having started out in Medicine)
✓ Published first-author researcher: DySCo, a framework for modelling dynamic functional connectivity networks in the brain, and AutoNeuro, a real-time, closed-loop fMRI system (currently under review)
✓ Lecturer and teaching assistant at King's College London in Machine Learning, Computational Neuroscience, and Computing for Brain & Cognitive Scientists
✓ Former Research Assistant in Forensic & Neurodevelopmental Sciences at King's College London, using deep learning to analyse conduct problems in young people
✓ Machine Learning Lead at a boutique AI consultancy, building the AI behind their flagship product
✓ Years of hands-on teaching and coaching experience with students of all ages and abilities
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 far more enjoyable. Because I am an active researcher who works with statistics, R, Python and data every day, I can show you how statistics and research methods actually work in practice, not just how to repeat them in an exam. That is what turns a nervous student into a confident one.
I specialise in:
✓ Research Methods, Statistics & Data Analysis: SPSS, R, Python and Jamovi, experimental design, data analysis and reporting, from A Level through to undergraduate, postgraduate and PhD. This is my core teaching specialism.
✓ The Mathematics Behind Statistics: probability, distributions, linear algebra, calculus and the maths that underpins every test, taught from the ground up.
✓ Statistical Modelling: the general linear model, regression, ANOVA, linear mixed models and Bayesian methods, with hands-on coding in R.
✓ Artificial Intelligence & Machine Learning: Python, supervised and unsupervised models, neural networks and applied machine learning for data analysis.
✓ Neuroscience & Computational Neuroscience: the statistics and computational methods behind neuroimaging, neuronal dynamics modelling and brain data. This is my core research specialism.
✓ Statistics and Maths at A Level, IB and GCSE (AQA and the major exam boards): the research methods, maths and statistics content that students find hardest.
✓ Dissertation & Project Support: research design, statistical analysis, data handling, coding and write-up for undergraduate, postgraduate and doctoral projects.
Who I Help:
✓ GCSE, IB and A Level students aiming for top grades in the research methods, maths and statistics content
✓ University students struggling with statistics, research methods or data analysis
✓ Undergraduates and postgraduates needing data analysis, coding or dissertation support
✓ Researchers and PhD students who want to master SPSS, R, Python and advanced statistical modelling
Real Results:
✓ I lecture and teach quantitative and computational methods at King's College London
✓ I am a published first-author researcher who uses statistics and machine learning every day
✓ I specialise in making statistics and research methods, where most students struggle, genuinely make sense
✓ I have years of experience teaching and coaching students of all ages and abilities, online and in person
My Background:
I started out studying Medicine at St George's, University of London, before completing a BSc (Hons) in Biomedical Science. I then moved to King's College London for an MSc in Basic & Clinical Neuroscience, which I passed with Distinction, followed by an MRes in Neuroimaging, also with Distinction, as the funded research year of my doctoral programme. I am now completing a PhD in Computational Neuroscience at King's College London, funded by a competitive MRC studentship.
My research models dynamic functional connectivity in epilepsy, including the seizure onset zone and how task modulation relates to epileptogenicity, and it focuses on building and testing real-time, closed-loop fMRI systems that adapt to a person's brain activity as it is being measured. Along the way I worked as a Research Assistant in Forensic & Neurodevelopmental Sciences, using MRI deep learning to study conduct problems in young people, and as Machine Learning Lead at a boutique AI consultancy. I also spent six years teaching climbing in London, coaching children's squads, school groups and adults, which is where I learned how to make difficult things feel achievable for anyone.
I am based in Central London and teach both in person and online, so I have the privilege of working with students across London, the UK and internationally. I offer a free, no-pressure introductory call so we can make sure I am the right fit before you commit.
What to Expect:
✓ Personalised lessons tailored to your specific exam board, course or research project
✓ Clear explanations grounded in real research (no jargon unless you want it)
✓ Statistics, coding and data analysis made genuinely understandable (SPSS, R, Python, Jamovi)
✓ Essay feedback, structure support and exam technique
✓ A patient, encouraging approach for students of every level
✓ Flexible sessions in person in Central London or online across the UK and internationally
Course Coverage
Course coverage is walked through below, 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.
Research Methods and Statistics with R (Undergraduate to PhD)
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.
✓ Measurement and distributions: variables and measurement error, the properties of distributions, the normal distribution, z-scores and standardisation, and setting up R for scripting
✓ Sampling and inference: sampling distributions, standard error, confidence intervals and effect sizes, and the logic of the null hypothesis and the p-value
✓ Design and comparison of means: between-subjects and within-subjects designs, randomisation, order effects, and the t-test understood as a general linear model
✓ Correlation and regression: linear and partial correlation, simple and multiple regression, dummy coding, and measures of model fit
✓ Analysis of variance: one-way, factorial, repeated-measures and mixed ANOVA, planned and post-hoc contrasts, sphericity, and non-parametric alternatives
✓ Linear mixed models: fixed and random effects, random intercepts and slopes, and mixed models applied to brain and behavioural data
✓ Bayesian methods: frequentist versus Bayesian approaches, priors, Bayes factors, and Bayesian regression and ANOVA
✓ Power, reproducibility and reporting: power analysis and choosing N, the reproducibility crisis, questionable research practices, and how to report statistics for a publishable report
Mathematics and Programming Foundations (R, Python and MATLAB)
The mathematical and computational toolkit modern science and data analysis demand, taught from the ground up.
✓ Core mathematics: arithmetic and number systems, logs and exponents, linear and quadratic equations, inequalities, and trigonometric and Fourier methods
✓ Calculus and linear algebra: introductory calculus, vectors and matrices, transposition, inversion and the identity matrix, and matrix operations for data analysis
✓ Probability: probability and conditional probability, randomness, Monte Carlo simulation and resampling
✓ Programming for analysis: scripting and data wrangling in R, Python and MATLAB, regular expressions, and reproducible workflows
✓ Time-series and multivariate data: processing one-dimensional and two-dimensional datasets such as fMRI, EEG and electrophysiological recordings
Machine Learning in Neuroscience (Python)
A modern and highly employable strand, from first principles through to deep and reinforcement learning, applied to brain and behavioural data.
✓ Supervised learning: regression for continuous outcomes and classification for categorical outcomes, using linear and logistic regression, support vector machines and decision trees
✓ Unsupervised learning: dimensionality reduction and clustering through PCA, ICA and k-means
✓ Model evaluation: cross-validation, overfitting and underfitting, and ROC curves, precision and recall
✓ Deep learning: multi-layer perceptrons, activation functions and backpropagation, convolutional networks for imaging and recurrent networks for sequence data
✓ Reinforcement learning: reward-based learning, exploration and exploitation, Q-learning, policy gradients and Markov decision processes
✓ Ensembles and Auto-ML: bagging, boosting and stacking, and automated model selection, with applications such as predicting disease status from neuroimaging
Computational Neuroscience (Python)
A hands-on modelling strand that builds from brain networks up to detailed models of single neurons, coded in Python.
✓ Modelling and brain connectivity: network science, the adjacency matrix, and graph-theoretical measures of network organisation
✓ Whole-brain dynamics: the Kuramoto model of coupled oscillators and the Wilson-Cowan model of excitatory and inhibitory dynamics, simulated with tools such as Neurolib
✓ Generative and probabilistic models: how generative models capture the likelihood of connections forming
✓ Models of neuronal dynamics: spiking neuron models, synaptic plasticity, and reservoir computing with recurrent and echo state networks
✓ The Hodgkin-Huxley model: the classic model of the action potential, ion-channel dynamics and neuronal excitability
Advanced Neuroimaging and Connectomics (MSc and PhD)
My own research territory, the postgraduate methods that define modern human neuroscience.
✓ Structural and diffusion MRI: T1 and T2 imaging, voxel-based morphometry, DTI and tractography, and white-matter network reconstruction
✓ Functional MRI: the BOLD signal, preprocessing pipelines such as fMRIPrep, the general linear model, and first-level and second-level analysis
✓ Network neuroscience and connectomics: the adjacency matrix, graph-theoretical measures, structural and functional connectivity, and the human connectome
✓ Dynamic functional connectivity: time-resolved connectivity and brain-state dynamics, the focus of my framework DySCo for modelling dynamic functional connectivity networks in the brain
✓ Real-time and closed-loop fMRI: neurofeedback and real-time systems that adapt to a person's brain activity as it is measured, the focus of my system AutoNeuro
✓ Multivariate and machine-learning analysis: MVPA, representational similarity analysis, decoding, and deep-learning clustering and subtyping of clinical populations from MRI
Computer Science and Computing for Scientists
University-level computer science taught by an active practitioner who codes every day.
✓ Programming foundations: Python programming, control flow, functions and object-oriented design
✓ Algorithms and data structures: core data structures, algorithm design and analysis, and computational complexity
✓ Scientific computing: numerical methods, simulation, and computational modelling in Python and MATLAB
✓ Software for research: version control, testing, reproducible pipelines and good engineering practice
✓ Applied AI engineering: building and deploying machine-learning systems, drawing on my work leading machine learning at an AI consultancy
Foundations of Brain and Behaviour
We build from single cells to whole systems and on to clinical conditions, the core of how the brain gives rise to mind and behaviour.
✓ The nervous system: the organisation of the central and peripheral nervous systems, brain development from the neural tube, brain anatomy, neurons, glial cells and synapses, and where psychology meets neuroscience
✓ Cells and signalling: the structure and function of neurons and glial cells, the action potential, synaptic transmission, and the neurotransmitter lifecycle from synthesis and vesicular release to receptor binding and breakdown
✓ Sensation and perception: the difference between sensation and perception, transduction in the visual and auditory systems, sensory coding, and what sensory deficits reveal
✓ Investigating the brain: 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, memory and language: selective and sustained attention, the fractionation of memory across short-term, working and long-term systems, the hippocampus and amygdala, and the neural foundations of language in Broca's and Wernicke's areas
✓ Neurodegeneration and psychiatric conditions: the pathology and treatment of Alzheimer's and Parkinson's disease, and the biological basis of schizophrenia and the affective disorders
Brain Form and Function, including Neuropharmacology
The cellular and chemical machinery of the nervous system, including how drugs act on it.
✓ Brain cells and their function: cell types, the structure and organisation of the neuron, and what keeps neurons alive
✓ Neural communication: electrical signalling and the action potential, chemical synapses and the full neurotransmitter lifecycle, and the major receptors including ligand-gated ion channels and G-protein-coupled receptors
✓ Major neurotransmitter systems in health and disease: the dopaminergic, noradrenergic, cholinergic and serotonergic pathways and their role in neurological disorders
✓ Neuroactive drugs: the principles of drug action, delivery and clearance, agonism and antagonism, tolerance and dependence, and the classification and mechanisms of psychotropic drugs
✓ Neuroplasticity: gene-environment interactions, epigenetic modification, habituation and sensitisation, and the cellular basis of learning and memory
✓ Immunity and the brain: innate and adaptive immunity, microglia, neuroinflammation and the effects of chronic immune activation
Memory, Perception and the Cognitive Brain
How the brain organises itself, and just how unreliable, and fascinating, the mind can be.
✓ Memory and its distortions: episodic and autobiographical memory, the reconstructive nature of remembering, and the neural systems behind it
✓ Perception and its distortions: visual illusions, visual agnosia and optic ataxia, the modularity of vision, and the progression from visual input to conscious perception
✓ Emotion, the self and the social world: how emotion shapes cognition, self-related processing, social perception, and the biases that colour memory and behaviour
✓ Modularity and networks: the modularity versus equipotentiality debate, the binding problem, and how brain networks and neural connections are mapped
✓ Consciousness: the neural correlates of consciousness, split-brain findings, implicit processing, and the leading scientific theories
✓ Functional neurological disorder and language: the clinical features and models of FND, and the neural network for speech perception, reading and developmental dyslexia
The Making of a Brain: Neuroanatomy and Development
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, anatomical terminology, the major brain regions, the functional organisation of the cortex, and the motor and sensory maps
✓ From neurons to behaviour: neuronal polarity, action potentials, chemical synapses, and excitatory and inhibitory neurons
✓ Grey and white matter: astrocytes and oligodendrocytes, the meninges, and the organisation of grey and white matter and the spinal cord
✓ Tract-tracing and connectivity: anterograde and retrograde tracing, axon tracts, and Diffusion Tensor Imaging
✓ Neurodevelopment: neural tube patterning and neurulation, the signalling gradients that guide brain development, axon guidance and the growth cone, and topographic mapping
✓ Comparative neuroanatomy: evolutionary homology, neuroevolution and primate brain evolution, and open data platforms such as the Allen Institute Mouse Connectivity Atlas
Molecular and Cellular Neuroscience
The brain at its smallest scale, the cell biology that everything else rests on.
✓ Cellular structure and function: the organelles and their jobs, from the nucleus and mitochondria to the endoplasmic reticulum, Golgi apparatus, ribosomes, cytoskeleton and lysosomes
✓ Gene transcription and protein translation: promoters and the regulation of expression, epigenetic modification, splicing, the genetic code, and post-translational modification
✓ Axonal transport and protein management: trafficking by kinesin and dynein along microtubules, the ubiquitin-proteasome system, autophagy and the unfolded protein response
✓ Energy and signalling: mitochondrial function and ER-mitochondrial signalling, second messenger systems, and the role of calcium, phosphorylation and kinases
✓ Environmental interactions: the cellular stress response and how epigenetics links the environment to the cell
The Electrophysiological Brain
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: synaptic events and action potentials, brain oscillations and brain states, and how these link to cognition and behaviour
✓ Recording techniques: intracellular and extracellular recording, single-neuron recording, optogenetics, and EEG and event-related potentials
✓ Applications in cognitive neuroscience: how these methods answer real cognitive questions, from single neurons to whole-scalp recordings
✓ Data analysis and design: advanced analysis of electrophysiological data, and how to formulate research questions and design experiments
Psychology across Development, the Individual and Society
The broader psychology syllabus, kept in full so your whole course is covered.
✓ Developmental psychology: nature and nurture, genetic and epigenetic influences, attachment, cognitive development through Piaget and Vygotsky, and developmental psychopathology including autism and ADHD
✓ Individual differences: personality and intelligence, their measurement and genetic basis, learning theory and behaviourism, and classical and operant conditioning
✓ Social psychology: the self, attribution errors and cognitive biases, attitudes and persuasion, conformity, obedience and social influence, group processes, and prosocial and antisocial behaviour
✓ The origins of individual differences: human and quantitative genetics, genome-wide association studies and polygenic scores, and the social and cultural origins of mental health and disorder
Specialist and Conceptual Modules
The more conceptual and applied options, covered for students who take them.
✓ Decision-making under uncertainty: rationality, free will and volition, preference measurement, prospect theory, and the psychology of addiction and gambling
✓ Philosophy of mind: the mind-body problem, dualism, identity theory, functionalism and anomalous monism, and the metaphysics of perception
✓ The interdisciplinary study of consciousness: neural correlates, higher-order and information-processing theories, and conscious versus non-conscious processing
✓ Applied performance psychology: psychological skills training, CBT, REBT and ACT, performance and mental health, injury and career transitions, and resilience, grit and mental toughness
Dissertation, Research Skills and Publishing (MSc and PhD)
The research craft that turns a strong student into a published one.
✓ Project design: formulating research questions, choosing designs, and writing a strong proposal
✓ Writing up: structuring Methods, Results and Discussion, reporting statistics, and the elements of effective scientific writing
✓ Reproducible research: open data, pre-registration, version control and reproducible analysis pipelines
✓ Publishing and dissemination: the structure of a research paper, journal metrics, the peer-review process, presentations and building an e-portfolio
If it appears in your 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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As an Academic Excellence Consultant at Pareto Path I help students from all backgrounds, across Statistics, Research Methods and the mathematics behind them, alongside Neuroscience, Psychology, Computer Science and Artificial Intelligence and Machine Learning, and especially SPSS, R, Python and data analysis, to achieve the satisfaction of work they are truly proud of, and the boost to their grades that comes with that, through personalised one-to-one lessons in Central London or online.
Whether you're:
✓ A GCSE, A Level or IB student aiming for top grades in the research methods, maths and statistics content
✓ A university student struggling with statistics, research methods or data analysis
✓ An undergraduate, postgraduate or PhD student needing data analysis, coding or dissertation support
✓ A researcher who wants to master SPSS, R, Python and advanced statistical modelling
...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, in person in Central London or online, with no generic lectures
✓ Research Methods & Statistics Mastery - SPSS, R, Python and Jamovi explained clearly (this is my speciality), including experimental design, assumption testing, analysis and reporting
✓ The Maths Behind the Methods - probability, distributions, linear algebra and the maths that make statistics finally make sense
✓ Statistical Modelling & Coding - regression, ANOVA, linear mixed models and Bayesian methods, with hands-on R and Python
✓ Essay, Report & Exam Technique - structure, critical analysis and proven strategies to maximise marks
✓ Dissertation & Data Analysis Support - design, analysis, coding and write-up for research projects
How It Works:
✓ Step 1: Free Introductory 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: Regular 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 Statistics and Research Methods and the mathematics behind them across all levels:
✓ A Level, IB & GCSE Research Methods, Maths and Statistics (AQA and the major boards)
✓ Descriptive and inferential statistics, distributions and probability
✓ Experimental design, validity and sampling
✓ The maths behind the methods, from algebra to the normal distribution
Undergraduate & Postgraduate Statistics & Data Analysis:
✓ Statistics in SPSS, R, Python and Jamovi
✓ The general linear model: t-tests, correlation and regression
✓ ANOVA, factorial, repeated-measures and mixed designs
✓ Linear mixed models and Bayesian statistics
✓ Assumption testing, effect sizes, power analysis and reporting
PhD, Research & Applied Methods:
✓ Reproducible research, pre-registration and open data
✓ Statistics and machine learning for neuroimaging and brain data
✓ Multivariate methods and dimensionality reduction
✓ Dissertation and thesis design, analysis and write-up
Full Topic List Available: I cover a wide range of statistics, research methods, mathematics, AI and neuroscience topics, with particular depth in SPSS, R, Python, statistical modelling and the maths behind the methods. If it's in your Statistics or Research Methods course, there is a good chance I can help, so do ask.
Pricing:
✓ Prices start at £99 per hour for all Statistics, Research Methods, Neuroscience and AI/Machine Learning tutoring
✓ First contact: a FREE introductory 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 genuinely understand
✓ I make statistics and research methods, the hardest part for most students, actually make sense
✓ I'm an active PhD researcher and published author, so you learn from someone who does this work every day
✓ I teach at university level at King's College London in quantitative and computational methods
✓ Patient, encouraging and experienced with students of all ages and abilities
✓ Flexible sessions in person in Central London or online across the UK and internationally
Ready to Get Started?
Click "Contact Oliver" to book your free introductory call and I will always do my best to reply within the hour.
Course Coverage
Course coverage is walked through below, 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.
Research Methods and Statistics with R (Undergraduate to PhD)
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.
✓ Measurement and distributions: variables and measurement error, the properties of distributions, the normal distribution, z-scores and standardisation, and setting up R for scripting
✓ Sampling and inference: sampling distributions, standard error, confidence intervals and effect sizes, and the logic of the null hypothesis and the p-value
✓ Design and comparison of means: between-subjects and within-subjects designs, randomisation, order effects, and the t-test understood as a general linear model
✓ Correlation and regression: linear and partial correlation, simple and multiple regression, dummy coding, and measures of model fit
✓ Analysis of variance: one-way, factorial, repeated-measures and mixed ANOVA, planned and post-hoc contrasts, sphericity, and non-parametric alternatives
✓ Linear mixed models: fixed and random effects, random intercepts and slopes, and mixed models applied to brain and behavioural data
✓ Bayesian methods: frequentist versus Bayesian approaches, priors, Bayes factors, and Bayesian regression and ANOVA
✓ Power, reproducibility and reporting: power analysis and choosing N, the reproducibility crisis, questionable research practices, and how to report statistics for a publishable report
Mathematics and Programming Foundations (R, Python and MATLAB)
The mathematical and computational toolkit modern science and data analysis demand, taught from the ground up.
✓ Core mathematics: arithmetic and number systems, logs and exponents, linear and quadratic equations, inequalities, and trigonometric and Fourier methods
✓ Calculus and linear algebra: introductory calculus, vectors and matrices, transposition, inversion and the identity matrix, and matrix operations for data analysis
✓ Probability: probability and conditional probability, randomness, Monte Carlo simulation and resampling
✓ Programming for analysis: scripting and data wrangling in R, Python and MATLAB, regular expressions, and reproducible workflows
✓ Time-series and multivariate data: processing one-dimensional and two-dimensional datasets such as fMRI, EEG and electrophysiological recordings
Machine Learning in Neuroscience (Python)
A modern and highly employable strand, from first principles through to deep and reinforcement learning, applied to brain and behavioural data.
✓ Supervised learning: regression for continuous outcomes and classification for categorical outcomes, using linear and logistic regression, support vector machines and decision trees
✓ Unsupervised learning: dimensionality reduction and clustering through PCA, ICA and k-means
✓ Model evaluation: cross-validation, overfitting and underfitting, and ROC curves, precision and recall
✓ Deep learning: multi-layer perceptrons, activation functions and backpropagation, convolutional networks for imaging and recurrent networks for sequence data
✓ Reinforcement learning: reward-based learning, exploration and exploitation, Q-learning, policy gradients and Markov decision processes
✓ Ensembles and Auto-ML: bagging, boosting and stacking, and automated model selection, with applications such as predicting disease status from neuroimaging
Computational Neuroscience (Python)
A hands-on modelling strand that builds from brain networks up to detailed models of single neurons, coded in Python.
✓ Modelling and brain connectivity: network science, the adjacency matrix, and graph-theoretical measures of network organisation
✓ Whole-brain dynamics: the Kuramoto model of coupled oscillators and the Wilson-Cowan model of excitatory and inhibitory dynamics, simulated with tools such as Neurolib
✓ Generative and probabilistic models: how generative models capture the likelihood of connections forming
✓ Models of neuronal dynamics: spiking neuron models, synaptic plasticity, and reservoir computing with recurrent and echo state networks
✓ The Hodgkin-Huxley model: the classic model of the action potential, ion-channel dynamics and neuronal excitability
Advanced Neuroimaging and Connectomics (MSc and PhD)
My own research territory, the postgraduate methods that define modern human neuroscience.
✓ Structural and diffusion MRI: T1 and T2 imaging, voxel-based morphometry, DTI and tractography, and white-matter network reconstruction
✓ Functional MRI: the BOLD signal, preprocessing pipelines such as fMRIPrep, the general linear model, and first-level and second-level analysis
✓ Network neuroscience and connectomics: the adjacency matrix, graph-theoretical measures, structural and functional connectivity, and the human connectome
✓ Dynamic functional connectivity: time-resolved connectivity and brain-state dynamics, the focus of my framework DySCo for modelling dynamic functional connectivity networks in the brain
✓ Real-time and closed-loop fMRI: neurofeedback and real-time systems that adapt to a person's brain activity as it is measured, the focus of my system AutoNeuro
✓ Multivariate and machine-learning analysis: MVPA, representational similarity analysis, decoding, and deep-learning clustering and subtyping of clinical populations from MRI
Computer Science and Computing for Scientists
University-level computer science taught by an active practitioner who codes every day.
✓ Programming foundations: Python programming, control flow, functions and object-oriented design
✓ Algorithms and data structures: core data structures, algorithm design and analysis, and computational complexity
✓ Scientific computing: numerical methods, simulation, and computational modelling in Python and MATLAB
✓ Software for research: version control, testing, reproducible pipelines and good engineering practice
✓ Applied AI engineering: building and deploying machine-learning systems, drawing on my work leading machine learning at an AI consultancy
Foundations of Brain and Behaviour
We build from single cells to whole systems and on to clinical conditions, the core of how the brain gives rise to mind and behaviour.
✓ The nervous system: the organisation of the central and peripheral nervous systems, brain development from the neural tube, brain anatomy, neurons, glial cells and synapses, and where psychology meets neuroscience
✓ Cells and signalling: the structure and function of neurons and glial cells, the action potential, synaptic transmission, and the neurotransmitter lifecycle from synthesis and vesicular release to receptor binding and breakdown
✓ Sensation and perception: the difference between sensation and perception, transduction in the visual and auditory systems, sensory coding, and what sensory deficits reveal
✓ Investigating the brain: 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, memory and language: selective and sustained attention, the fractionation of memory across short-term, working and long-term systems, the hippocampus and amygdala, and the neural foundations of language in Broca's and Wernicke's areas
✓ Neurodegeneration and psychiatric conditions: the pathology and treatment of Alzheimer's and Parkinson's disease, and the biological basis of schizophrenia and the affective disorders
Brain Form and Function, including Neuropharmacology
The cellular and chemical machinery of the nervous system, including how drugs act on it.
✓ Brain cells and their function: cell types, the structure and organisation of the neuron, and what keeps neurons alive
✓ Neural communication: electrical signalling and the action potential, chemical synapses and the full neurotransmitter lifecycle, and the major receptors including ligand-gated ion channels and G-protein-coupled receptors
✓ Major neurotransmitter systems in health and disease: the dopaminergic, noradrenergic, cholinergic and serotonergic pathways and their role in neurological disorders
✓ Neuroactive drugs: the principles of drug action, delivery and clearance, agonism and antagonism, tolerance and dependence, and the classification and mechanisms of psychotropic drugs
✓ Neuroplasticity: gene-environment interactions, epigenetic modification, habituation and sensitisation, and the cellular basis of learning and memory
✓ Immunity and the brain: innate and adaptive immunity, microglia, neuroinflammation and the effects of chronic immune activation
Memory, Perception and the Cognitive Brain
How the brain organises itself, and just how unreliable, and fascinating, the mind can be.
✓ Memory and its distortions: episodic and autobiographical memory, the reconstructive nature of remembering, and the neural systems behind it
✓ Perception and its distortions: visual illusions, visual agnosia and optic ataxia, the modularity of vision, and the progression from visual input to conscious perception
✓ Emotion, the self and the social world: how emotion shapes cognition, self-related processing, social perception, and the biases that colour memory and behaviour
✓ Modularity and networks: the modularity versus equipotentiality debate, the binding problem, and how brain networks and neural connections are mapped
✓ Consciousness: the neural correlates of consciousness, split-brain findings, implicit processing, and the leading scientific theories
✓ Functional neurological disorder and language: the clinical features and models of FND, and the neural network for speech perception, reading and developmental dyslexia
The Making of a Brain: Neuroanatomy and Development
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, anatomical terminology, the major brain regions, the functional organisation of the cortex, and the motor and sensory maps
✓ From neurons to behaviour: neuronal polarity, action potentials, chemical synapses, and excitatory and inhibitory neurons
✓ Grey and white matter: astrocytes and oligodendrocytes, the meninges, and the organisation of grey and white matter and the spinal cord
✓ Tract-tracing and connectivity: anterograde and retrograde tracing, axon tracts, and Diffusion Tensor Imaging
✓ Neurodevelopment: neural tube patterning and neurulation, the signalling gradients that guide brain development, axon guidance and the growth cone, and topographic mapping
✓ Comparative neuroanatomy: evolutionary homology, neuroevolution and primate brain evolution, and open data platforms such as the Allen Institute Mouse Connectivity Atlas
Molecular and Cellular Neuroscience
The brain at its smallest scale, the cell biology that everything else rests on.
✓ Cellular structure and function: the organelles and their jobs, from the nucleus and mitochondria to the endoplasmic reticulum, Golgi apparatus, ribosomes, cytoskeleton and lysosomes
✓ Gene transcription and protein translation: promoters and the regulation of expression, epigenetic modification, splicing, the genetic code, and post-translational modification
✓ Axonal transport and protein management: trafficking by kinesin and dynein along microtubules, the ubiquitin-proteasome system, autophagy and the unfolded protein response
✓ Energy and signalling: mitochondrial function and ER-mitochondrial signalling, second messenger systems, and the role of calcium, phosphorylation and kinases
✓ Environmental interactions: the cellular stress response and how epigenetics links the environment to the cell
The Electrophysiological Brain
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: synaptic events and action potentials, brain oscillations and brain states, and how these link to cognition and behaviour
✓ Recording techniques: intracellular and extracellular recording, single-neuron recording, optogenetics, and EEG and event-related potentials
✓ Applications in cognitive neuroscience: how these methods answer real cognitive questions, from single neurons to whole-scalp recordings
✓ Data analysis and design: advanced analysis of electrophysiological data, and how to formulate research questions and design experiments
Psychology across Development, the Individual and Society
The broader psychology syllabus, kept in full so your whole course is covered.
✓ Developmental psychology: nature and nurture, genetic and epigenetic influences, attachment, cognitive development through Piaget and Vygotsky, and developmental psychopathology including autism and ADHD
✓ Individual differences: personality and intelligence, their measurement and genetic basis, learning theory and behaviourism, and classical and operant conditioning
✓ Social psychology: the self, attribution errors and cognitive biases, attitudes and persuasion, conformity, obedience and social influence, group processes, and prosocial and antisocial behaviour
✓ The origins of individual differences: human and quantitative genetics, genome-wide association studies and polygenic scores, and the social and cultural origins of mental health and disorder
Specialist and Conceptual Modules
The more conceptual and applied options, covered for students who take them.
✓ Decision-making under uncertainty: rationality, free will and volition, preference measurement, prospect theory, and the psychology of addiction and gambling
✓ Philosophy of mind: the mind-body problem, dualism, identity theory, functionalism and anomalous monism, and the metaphysics of perception
✓ The interdisciplinary study of consciousness: neural correlates, higher-order and information-processing theories, and conscious versus non-conscious processing
✓ Applied performance psychology: psychological skills training, CBT, REBT and ACT, performance and mental health, injury and career transitions, and resilience, grit and mental toughness
Dissertation, Research Skills and Publishing (MSc and PhD)
The research craft that turns a strong student into a published one.
✓ Project design: formulating research questions, choosing designs, and writing a strong proposal
✓ Writing up: structuring Methods, Results and Discussion, reporting statistics, and the elements of effective scientific writing
✓ Reproducible research: open data, pre-registration, version control and reproducible analysis pipelines
✓ Publishing and dissemination: the structure of a research paper, journal metrics, the peer-review process, presentations and building an e-portfolio
If it appears in your 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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Rates
Rate
- $186
Pack rates
- 5 h: $928
- 10 h: $1857
online
- $186/h
free lessons
This first lesson offered with Oliver will allow you to get to know each other and clearly specify your needs for your next lessons.
- 30min
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