The Computational Modelling Laboratory
A specialist Brain Research & Imaging Centre (BRIC) laboratory that will enhance our research into computational cognitive neuroscience
The Computational Modelling Laboratory in the
Brain Research & Imaging Centre (BRIC)
provides Plymouth’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.
The human brain is perhaps 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, those models help us visualise and analyse the massive raw data from neuroscience experiments in more revealing ways. Other times, our models are designed to mimic or replicate aspects of brain function. These models can help develop a better understanding of mental health disorders, help make human learning more efficient, and they can inform the development of the next generation of smart machines.
For enquiries or further information please contact: bric@plymouth.ac.uk
Investigating learning and memory
The Computational Modelling Laboratory Lead,
Professor Andy Wills
, works on the computational modelling of learning and memory.
He has worked on computer models of amnesia (O’Connell et al., 2016), decision-making (Sambrook et al., 2018), and maintains the catlearn software package – a popular modelling tool with over 14,000 downloads to date (Wills et al., 2017).
He also works on a project concerning the relation between errors and learning, funded by the Economic and Social Research Council.
Other members of the lab, including visiting professor Roman Borisyuk, build detailed neural models to predict human behaviour (Kazanovich & Borisyuk, 2016).
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 ¶¶Òõ¶ÌÊÓƵ.
A collaboration with
Professor Antonio Rago
and colleagues in the Faculty of Science and Engineering, supports the processing of complex computational routines for empirical human neuroimaging data analysis and In Silico neural, cognitive and behavioural models.
Research expertise
Lab lead:
Professor Andy Wills
, Professor in Psychology
Other research in this laboratory is carried out by
Professor Elsa Fouragnan
and
Professor Roman Borisyuk
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.
