neuroscientist & programmer


I was born in London in November 1990. After receiving a BSc in Psychology from the University of Exeter in 2013, I completed a PhD in Experimental Psychology at the University of Oxford (2014-2018).
During this doctoral research, I focused on human electrophysiology and oscillations in brain activity. Specifically, I investigated the ways in which alpha oscillations (7-13 Hz), recorded over visual regions of the brain, contribute to visual processing. I did this by delivering sinusoidal electrical currents over posterior regions of cortex at varying frequencies, and observing the effects of this stimulation on visual attention task performance. The results of this research have been published in the Journal of Experimental Psychology: General, and Frontiers in Neuroscience.
After submitting my PhD thesis, in order to learn new skills and gain experience of research outside of academia, I took a position at Cambridge Cognition (a software company that specialises in providing cognitive assessment software for clinical research). Here, I learned about software development in a commercial environment (e.g. version control, development life cycles, DevOps), as well as how to run cognitive psychology experiments using online platforms (i. e. Mechanical Turk and Prolific Academic). More recently, I have also worked as a consultant data scientist on a project with Unit 9 and Ford, analysing various types of data (e.g. video, EEG, GPS) to help monitor driver cognition during competitive racing.



The primary focus of my doctoral research was to better understand why oscillations are so prevalent in human brain activity. One of the strongest examples of such oscillations are occipitoparietal alpha rhythms, which emerge strongly when people close their eyes. While alpha rhythms are commonly associated with reductions in arousal and attention, my research sought to explore how alpha rhythms can also contribute positively to cognitive functioning. Examples of such work include theoretical papers describing how neural oscillations can facilitate long-range communication in the brain (see review in Trends in Cognitive Sciences), as well as fine-grained temporal control of perceptual processes ( see review in the European Journal of Neuroscience).


In most of my experiments, I have used electroencephalography (EEG) to measure human brain activity. However, benefiting from the increasing number of open datasets in neuroscience, I have also worked on analysing invasive electrophysiology, optical imaging, and laminar fMRI data. One example of such work is my analysis of Neuropixels recordings (from the Allen Institute; see link). In recent years, I have also become increasingly interested in the nervous systems of insects. In particular, I have focused on analyses of connectomic (see link) and in-vivo, whole brain imaging data in Drosophila (see link).


In addition to psychology and neuroscience, I have a strong interest in programming languages. During my PhD, I used MATLAB extensively for conducting experiments, and for analysing both behavioural and electrophysiological data. I have commonly used SPSS, but have more recently transferred entirely to R for my statistical analyses.
From my previous work in developing cognitive tasks for online experiments, I am also proficient in web development languages (i.e. HTML, CSS, Javascript). During my time at Cambridge Cognition, I used these languages to develop both front- and back-end applications for monitoring cognitive health in pharmaceutical trials. In addition, I also learned how to use technologies like Git for version control, Amazon Web Services for cloud computing, Docker and Kubernetes for machine virtualisation, and Python for machine learning.


An ideal intersection of my interests in neuroscience and software engineering is computational neuroscience. Using libraries like NEURON, NEST, and Brian 2, I have worked to create realistic simulations of neural networks. Examples of such work include models of neural oscillation generation in the thalamus (see link), and wave propagation across 2D neural sheets (see link). I have also focused on using convolutional neural networks to model the emergence of receptive fields in visual cortex (see link). Further examples of my projects in computational neuroscience can be found in this Github repository.


The roles of cortical oscillations in sustained attention

Michael S. Clayton, Nick Yeung, Roi Cohen Kadosh

Trends in Cognitive Sciences 19 (4), 188-195

We rely on sustained attention to protect task performance against fatigue and distraction. Time-related variations in attention correlate with amplitude changes of specific cortical oscillations. However, the ways in which these oscillations might support sustained attention, how these oscillations are controlled, and the extent to which they influence one another remain unclear. We address this issue by proposing an oscillatory model of sustained attention. Within this framework, sustained attention relies on frontomedial theta oscillations, inter-areal communication via low-frequency phase synchronisation, and selective excitation and inhibition of cognitive processing through gamma and alpha oscillations, respectively. Sustained attention also relies on interactions between these oscillations across attention-related brain networks.
Mapping the mechanisms of transcranial alternating current stimulation: a pathway from network effects to cognition

Ruairidh Battleday, Timothy Muller, Michael S. Clayton, Roi Cohen Kadosh

Frontiers in Psychiatry 5, 162

In recent decades, our appreciation of the complexity of the brain has deepened immensely, as has our understanding of how it performs key functions. In the face of such complexity, and given the rising cost of neuropsychiatric illness (1), an intriguing question is whether we can promote further understanding, and in some cases enhancement, of the typical and atypical brain by targeted modulation of its activity. Notably, transcranial alternating current stimulation (tACS)–which involves transcranial application of weak sinusoidal electrical currents (2)–seems ideally suited to address this question, as it has been demonstrated to modulate endogenous oscillatory electrical activity (3), enhance cognitive functions (4–7), and provide support in neurological disease (8, 9). However, a complete mechanistic pathway between the neuronal and cognitive effects of tACS remains in need of explication, precluding both significant theoretical contribution by tACS studies, and the development of more adaptive neuroenhancement regimes. Therefore, in this Opinion article, we briefly review the role of oscillatory neuronal activity in cognition, before outlining one potential pathway by which the interaction between tACS and endogenous oscillations at a network level may be reconciled with its effects on broader cognitive functions.
The many characters of visual alpha oscillations

Michael S. Clayton, Nick Yeung, Roi Cohen Kadosh

European Journal of Neuroscience

A central feature of human brain activity is the alpha rhythm: a 7–13 Hz oscillation observed most notably over occipitoparietal brain regions during periods of eyes‐closed rest. Alpha oscillations covary with changes in visual processing and have been associated with a broad range of neurocognitive functions. In this article, we review these associations and suggest that alpha oscillations can be thought to exhibit at least five distinct ‘characters’: those of the inhibitor, perceiver, predictor, communicator and stabiliser. In short, while alpha oscillations are strongly associated with reductions in visual attention, they also appear to play important roles in regulating the timing and temporal resolution of perception. Furthermore, alpha oscillations are strongly associated with top‐down control and may facilitate transmission of predictions to visual cortex. This is in addition to promoting communication between frontal and posterior brain regions more generally, as well as maintaining ongoing perceptual states. We discuss why alpha oscillations might associate with such a broad range of cognitive functions and suggest ways in which these diverse associations can be studied experimentally.
Electrical stimulation of alpha oscillations stabilises performance on visual attention tasks

Michael S. Clayton, Nick Yeung, Roi Cohen Kadosh

Journal of Experimental Psychology: General

Neural oscillations in the alpha band (7–13 Hz) have long been associated with reductions in attention. However, recent studies have suggested a more nuanced perspective in which alpha oscillations also facilitate processes of cognitive control and perceptual stability. Transcranial alternating current stimulation (tACS) over occipitoparietal cortex at 10 Hz (alpha-tACS) can selectively enhance EEG alpha power. To assess the contribution of alpha oscillations to attention, we delivered alpha-tACS across four experiments while 178 participants performed sustained attention tasks. Poor performance on all visual tasks was previously associated with increased EEG alpha power. We therefore predicted initially that alpha-tACS would consistently impair visual task performance. However, alpha-tACS was instead found to prevent deteriorations in visual performance that otherwise occurred during sham- and 50 Hz-tACS. This finding was observed in two experiments, using different sustained attention tasks. In a separate experiment, we also found that alpha-tACS limited improvements on a visual task where learning was otherwise observed. Consequently, alpha-tACS appeared to exert a consistently stabilizing effect on visual attention. Such effects were not seen in an auditory control task, indicating specificity to the visual domain. We suggest that these results are most consistent with the view that alpha oscillations facilitate processes of top-down control and attentional stability
The effects of 10 Hz transcranial alternating current stimulation on audiovisual task switching

Michael S. Clayton, Nick Yeung, Roi Cohen Kadosh

Frontiers in Neuroscience 12, 67

Neural oscillations in the alpha band (7–13 Hz) are commonly associated with disengagement of visual attention. However, recent studies have also associated alpha with processes of attentional control and stability. We addressed this issue in previous experiments by delivering transcranial alternating current stimulation at 10 Hz over posterior cortex during visual tasks (alpha tACS). As this stimulation can induce reliable increases in EEG alpha power, and given that performance on each of our visual tasks was negatively associated with alpha power, we assumed that alpha tACS would reliably impair visual performance. However, alpha tACS was instead found to prevent both deteriorations and improvements in visual performance that otherwise occurred during sham & 50 Hz tACS. Alpha tACS therefore appeared to exert a stabilizing effect on visual attention. This hypothesis was tested in the current, pre-registered experiment by delivering alpha tACS during a task that required rapid switching of attention between motion, color, and auditory subtasks. We assumed that, if alpha tACS stabilizes visual attention, this stimulation should make it harder for people to switch between visual tasks, but should have little influence on transitions between auditory and visual subtasks. However, in contrast to this prediction, we observed no evidence of impairments in visuovisual vs. audiovisual switching during alpha vs. control tACS. Instead, we observed a trend-level reduction in visuoauditory switching accuracy during alpha tACS. Post-hoc analyses showed no effects of alpha tACS in response time variability, diffusion model parameters, or on performance of repeat trials. EEG analyses also showed no effects of alpha tACS on endogenous or stimulus-evoked alpha power. We discuss possible explanations for these results, as well as their broader implications for current efforts to study the roles of neural oscillations in cognition using tACS.


PhD in Experimental Psychology

January 2014 - May 2018

Jesus College, University of Oxford

  • Funded by the UK Defence Science and Technology Laboratory
  • Supervised by Roi Cohen Kadosh and Nick Yeung
  • Focused on understanding the roles played by alpha oscillations (7 - 13 Hz) over occipitoparietal cortex in sustained, visual attention. Research involved the modulation of brain activity using transcranial alternating current stimulation, and the measurement of these effects using behavioural analyses and electroencephalography
  • Collected data from more than 250 participants (both single and multi-session experiments)
  • Click here to download my thesis

BSc Psychology - First Class Honours

October 2010 - July 2013

University of Exeter

  • Highest mark in dissertation (81%), and third highest overall mark in degree cohort (74.75%)
  • SAGE Prize for the Best Undergraduate Dissertation in Psychology
  • Worked as an intern in the Cognitive Research Group, helping doctoral students with cognitive testing, electroencephalography, and eye-tracking

Epsom College

September 2004 - June 2009
  • A Levels: A, A, B (English, Politics, Biology)
  • GCSE: 1 A*, 6 As, 1 B, 2 Cs


Unit 9 & Ford

January 2019 - present

Consultant Data Scientist

  • Led the analysis of EEG, video, and GPS data on a project with Ford, focused on tracking the mental states of drivers during competitive racing
  • Collaborated with scientific and machine learning teams to create automated systems for classifying driving errors and their associated mental states
  • Click here to visit the Unit 9 website for the project.

Cambridge Cognition

April 2018 - May 2019

Science Intern and Research Engineer

  • Worked on designing platforms to record and analyse voice data for detection of pain using machine learning techniques (see link for project description).
  • Led the process of deploying this software on Amazon Web Services, using Docker and Kubernetes. Also worked on hardware projects using Raspberry Pi devices.
  • Designed and developed human cognition tests for online studies (using front-end web technologies).
  • Used Mechanical Turk and Prolific Academic to study these tests rapidly and at scale (i.e. n>100 study samples collected within a few hours), and to determine which tests were most suitable for online use.
  • Learned about software development cycles, version control (i.e. Git), and the digital health industry.