The Computational Modelling Laboratory
A specialist Brain Research & Imaging Centre (BRIC) laboratory that works to enhance our research into the brain computations
The Computational Modelling Laboratory in the
Brain Research & Imaging Centre (BRIC) provides °µÍø½âÃÜ’s neuroscientists with access to high-performance computing and data storage facilities. It also offers a common working space for researchers and postgraduate students with interests in computational neuroscience and computational modelling of brain functions.
The human brain is arguably the most complex machine in the universe. In the Computational Modelling Laboratory, we develop computer models of the brain that help us to understand this complexity.
Sometimes, these models help us visualise and analyse the massive raw data from neuroscience experiments in more revealing ways. Other times, our models are designed to represent and explain aspects of brain function. These models can help develop a better understanding of mental health disorders and healthy behaviour. We support the development of new computational models of brain functions and behaviours of experiments performed here, involving learning, decision making, and social interactions.
For enquiries or further information please contact: bric@plymouth.ac.uk
Investigating decision-making and the psychology of AI
The Computational Modelling Laboratory Lead,
Dr Andrea Pisauro , works on the computational modelling of decision-making and the psychology of AI.
Andrea has worked on computational modelling of brain activity (Pisauro et al., 2016) and cognitive processes relating to decision-making, including evidence accumulation (Pisauro et al., 2017), evaluation (Matthews et al., 2023) and social interactions (Pisauro et al., 2022), and teaches a module on the psychology of artificial intelligence.
BRIC Director
Professor Elsa Fouragnan , Lecturer in Psychology, also works on the relationship between errors and learning, using sophisticated computer models in her analysis of imaging data on this topic (Fouragnan et al., 2018).
Other members of the lab, include Dr Amir Aly, Lecturer in Artificial Intelligence & Robotics. His research applies deep learning and explainable AI to neuroimaging and multimodal brain data (e.g., structural MRI and resting-state fMRI) to characterise neurodivergence and predict clinically relevant outcomes, including autism, ADHD and related neurodevelopmental conditions.
Dr Craig McNeile, lecturer in theoretical physics, coordinates the computational modelling of the
human myopia research laboratory . Their research uses MRI to extract the three-dimensional, high-resolution anatomy of the eye in conjunction with computational modelling to investigate how ocular shape and biomechanics dictate eye growth.
BRIC neuroscience and high performance computing
Researchers at BRIC are able to process large and complex datasets through the high-speed links to
the Lovelace System HPC at the °µÍø½âÃÜ.
Research expertise
Lab lead:
Dr Andrea Pisauro , Lecturuer in Psychology.
Other research in this laboratory is carried out by
Professor Elsa Fouragnan ,
Dr Nadège Bault ,
Professor Andy Wills ,
Dr Amir Aly , and
Dr Craig McNeile .
In this video, Professor Andy Wills, Professor in Psychology, explains the Computational Modelling Lab in more detail.
Key publications
Sambrook T, Wills AJ, Hardwick B & Goslin J. (2018). 'Model-free and model-based reward prediction errors in EEG', NeuroImage,
Seabrooke T, Hollins T, Kent C, Wills A & Mitchell C. (2018). 'Learning from failure: Errorful generation improves memory for items, not associations', Journal of Memory and Language 104, 70–82,
Wills AJ, O'Connell G, Edmunds CER & Inkster AB. (2017). 'Progress in modelling through distributed collaboration: Concepts, tools, and category-learning examples', Psychology of Learning and Motivation,
Fouragnan E, Retzler C & Philiastides MG. (2018). 'Separate neural representations of prediction error valence and surprise: Evidence from an fMRI meta-analysis', Human Brain Mapping, ,
Kazanovich Y & Borisyuk R. (2016). 'Reaction times in visual search can be explained by a simple model of neural synchronization', Neural Networks 87, 1–7,
O'Connell G, Myers CE, Hopkins RO, McLaren RP, Gluck MA & Wills AJ. (2016). 'Amnesic Patients Show Superior Generalization in Category Learning', Neuropsychology, .
Prokic EJ, Weston C, Yamawaki N, Hall SD, Jones RS, Stanford IM, Ladds G, Woodhall GL. (2015). 'Cortical oscillatory dynamics and benzodiazepine-site modulation of tonic inhibition in fast-spiking interneurons', Neuropharmacology, 20; 95:192-205.
Lacey MG, Gooding-Williams G, Prokic EJ, Yamawaki N, Hall SD, Stanford IM, Woodhall GL.(2014). 'Spike Firing and IPSPs in Layer V Pyramidal Neurons during Beta Oscillations in RatPrimary Motor Cortex (M1) InVitro'. PLoS ONE, 9(1):e85109.
Ronnqvist KC, McAllister CJ, Woodhall GL, Stanford & Hall SD. (2013). 'A multimodal perspective on the composition of cortical oscillations'. Frontiers in Human Neuroscience. 7, 132.
Yamawaki N, Magill PJ, Woodhall GL, Hall, SD., & Stanford, IM. (2012). 'Frequency selectivity and dopamine dependence of plasticity at cortico-subthalamic synapses'. Neuroscience. 17;203:1-11.
Pirttimaki T, Hall SD & Parri HR. (2011). 'Sustained neuronal activity generated by glial plasticity'. Journal of Neuroscience. 31(21): 7637-47.
Brookes M, Gibson, A, Hall SD, Furlong PL, Barnes GR, Hillebrand, A, Francis S & Morris P. (2005). 'GLM-beamformer method demonstrates stationary field, alpha ERD and gamma ERS co-localisation with fMRI BOLD response in visual cortex'. NeuroImage, 26(1): 302-8.
Brookes M, Gibson A, Hall SD. Furlong PL, Barnes GR, Hillebrand A, Francis, S & Morris P. (2004). 'A general linear model for MEG beamformer imaging'. Neuroimage, 23(3): 936-46.
