School of Psychology

Computational Neuroscience

The computational neuroscience group uses neural computation to describe the processes in the brain.

The University of Nottingham’s computational neuroscience research group, led by Mark van Rossum, Mark Humphries and Stephen Coombes, uniquely bridges psychology and mathematics.

Our research covers all facets of computational research on the brain, from the changes at a single synapse, through the dynamics of large populations, to the bases of learning and movement dysfunction in neural disorders. We draw on techniques from across the scientific spectrum, including artificial intelligence, machine learning, dynamical systems theory, network science, and more.

Our work is funded by the MRC, the Leverhulme Trust, and the EPSRC.

Research areas

  • Action selection and decision making: how we choose what action to do next, and how we make our minds up
  • Energy as a constraint on neural processing: how optimising energy use places hard constraints on neural function and wiring
  • Movement disorders: how dysfunction of the basal ganglia leads to the symptoms of Parkinson’s disease, Huntington’s disease, dystonia, and more
  • Neural data science: developing machine learning approaches to the analysis of neural activity 
  • Population coding: how the joint activity of groups of neurons codes and computes
  • Synaptic plasticity: how and when the connections between neurons change, to drive learning

Study with us

MSc Computational Neuroscience, Cognition and AI

This interdisciplinary course combines aspects of psychology, mathematics and computer science to help you understand brain function, develop better analysis tools for neural data and to inspire artificial intelligence algorithms.  

Course information

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Recent projects 

Impaired learning in dystonia patients

Mark Humphries with Tom Gilbertson and Douglas Steele, University of Dundee. 

The movement disorder dystonia has been linked to changes in synaptic plasticity in the striatum. However, plasticity here is also considered key to learning from feedback.

In this project, we showed for the first time that dystonia patients are impaired only at reversing an already-learnt association between stimuli and reward.

By fitting computational models to the behaviour of both patients and a control group, we found that this learning deficit could be explained by a selective problem with the D2-type receptor in a sub-set of striatal neurons. Using learning deficits to uncover specific receptor problems is a promising approach for finding new targets for drug treatments.

Network science analysis of population activity

Across a range of papers, Mark Humphries and collaborators have pioneered the idea of using the tools of network theory to analyse large-scale recordings of activity from hundreds or thousands of neurons.

These have included solutions for the unsupervised detection of cell assemblies, and tracking the evolution of correlated activity over time and learning. From this work, we have published a range of open-source code, including:


Discover more about our research

In an episode of the Brain-Inspired podcast, Mark Humphries talks about his work and about the new field of neural data science.

Listen now

The BMJ’s Journal of Neurology, Neurosurgery and Psychiatry podcast interviews Mark Humphries about using computational models to understand Parkinson’s disease.

Listen now


Group members


Mark Humphries

Our research interrogates how the joint activity of many neurons encodes the past, present, and future in order to guide behaviour.

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Two questions particularly intrigue us: how do populations of neurons learn?  And is the joint activity of many neurons really a simple dynamical system? As neurons across the brain must work together to make bodies function in the world, we’ve attempted to make sense of the neural activity in the basal ganglia, brainstem, sensory and prefrontal cortex, and the sea-slug locomotion system. Irritatingly, dopamine keeps popping up everywhere – even in the sea slugs. I also find the simple pleasures of network theory to be a welcome distraction from the unfathomable complexities of the brain. 

You can find more about our work at  Humphries Lab.

I write about (systems) neuroscience for a broad audience at The Spike.

Matias Ison
Matias J. Ison
Assistant Professor

My research programme focuses on two fundamental questions in neuroscience: memory formation and visual search. For this, I study neural activity at different scales using a variety of experimental approaches and computational techniques.

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In collaboration with international institutions (e.g. UCLA Medical Center, King’s College Hospital), we study the simultaneous activity of single neurons recorded from microelectrodes implanted in the brain of epileptic patients for possible curative surgery. This approach led us to show for the first time that individual neurons in the human brain change their firing to link associations when a new memory is formed. We have recently started to implement neural network models, including short-term plasticity and STDP, to further our understanding of memory formation.

To uncover the neuronal underpinnings of visual search we are developing novel data analysis techniques to bridge the gap between eye movements and non-invasive (EEG/MEG) brain recordings. This research has recently led us to propose a novel data-driven framework, providing the link between local processing (from individual fixations) and the underlying mental program.

Chris Madan
Christopher Madan
Assistant Professor

I study memory using a combination of cognitive psychology, neuroimaging, and computational modelling methods. I am particularly interested in what factors makes some experiences more memorable than others (such as emotion, reward, and motor processing) and how these influences can manifest in future behaviour, such as decision making.

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I also specialise in characterising inter-individual differences in brain morphology, particularly with respect to aging, dementia, and cognitive abilities.

I conduct research across a variety of topics, including emotional memory, risky decision-making, and embodied cognition. I study these topics using behavioural paradigms, as well as fMRI, EEG, and structural MRI. Additionally, some studies involve computational modelling--either in the form of advanced statistical methods and machine learning, or through the development of specific models designed to distinguish between particular theoretical hypotheses.

Mark van Rossum

I started my computational neuroscience research after obtaining a PhD in theoretical physics. I have since worked across a wide number of topics in neuroscience, including, retinal function, synaptic plasticity and homoeostasis, memory formation, noise in neurons and sensory coding. 

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In addition, I have developed a number of data analysis methods for neuroscience, including the 'Van Rossum' spike distance measure. I have taught and organised various courses in the field of computational neuroscience. I have directed multiple PhD training programmes in Edinburgh, and I was nominated for a FENS supervision award. I am very excited about the new programmes that I have set up in Nottingham, as for the first time in the UK these will directly integrate computational neuroscience with psychology and mathematics.

To support students who have less exposure to computational neuroscience research, I have lectured many times in so-called summer schools and tutorials. My lecture notes have been used worldwide. For my research I mainly use computer simulations and theoretical analysis, and of course extensive conversations with fellow scientists. I am always interested in setting up new collaborations. Current research includes the study of schema and rule extraction from memory events, and the role of metabolic constraints on neural computation. The most up-to-date list of publications can be found on Google Scholar.


Postdoctoral scientists

Mathew Evans
Mathew Evans
Research Fellow

I'm a Computational Neuroscience Postdoc (in both subject and methodology) in the lab of Prof Mark Humphries.

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I am interested in the computational principles underlying simple cognitive processes such as sensation, action and decision making. I believe that computational and statistical methods can provide step-changes in our ability to understand the brain - in particular the automation of data processing tasks with machine learning. I also believe in studying simple systems with as much experimental precision as possible. For these reasons my current research is focussed on understanding the rodent whisker system with a range of computational tools (in collaboration with Rasmus Petersen, Manchester, UK).

My PhD and early postdoc years were earned with Prof. Tony Prescott in Sheffield, where I developed models of whisker based tactile perception, and robot technologies based on the rodent whisker system. See for more information.

Ho Ling Li
Ho Ling Li

Research Fellow

My research interest is synaptic plasticity and memory formation by using computational modelling

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With this approach, I hope it is achievable to obtain important insights into the biology in the brain. Currently, my primary research focus is on the impacts of metabolic cost on plasticity and network circuitry. Besides that, I am building a novel neural network model that describes the temporal dependence of reconsolidation and extinction of fear memory.

Silvia Maggi
Silvia Maggi
Research Fellow

The main focus of my research is to understand network dynamics underlying learning and memory processes.

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Learning from experience requires the combined integration of sensory stimuli and past events to drive future decision. This adaptive behaviour relies on complex dynamics of large-scale brain circuitry.  Understanding of these complex processes represents an important goal towards the comprehension of network abnormalities observed in learning disabilities and other psychiatric disorders.  My approach to address this challenge is to analyse large neural recordings of animals learning new tasks.  By using network theory and machine learning techniques I aim to understand cortico-hippocampal contribution when learning a spatial-navigation task.  




School of Psychology

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