Rafal Bogacz graduated in computer science at Wroclaw University of Technology in Poland. Afterwards he did a PhD in computational neuroscience at the University of Bristol, and worked as a postdoctoral researcher at Princeton University, USA, jointly in the Departments of Applied Mathematics and Psychology. In 2004 he came back to Bristol where he worked as a Lecturer and then a Reader. Rafal moved to the University of Oxford in 2013.
Awards Training and Qualifications
1998 – MEng, Wroclaw University of Technology, Poland
2001 – PhD, University of Bristol
Professor of Computational Neuroscience
My research is in the area of computational neuroscience, which uses mathematical models to understand how computations in neural circuits give rise to human and animal behaviour. My work focuses on the computational models of brain circuits underlying action selection and decision making. These circuits include a set of subcortical nuclei known as the basal ganglia, which has been intensively studied because it is affected by Parkinson’s disease.
My research concerns models of brain decision networks in both health and disease. My group investigates how the cortico-basal-ganglia network selects actions and learns from their outcomes. We also employ mathematical models to study how to best control deep brain stimulation in order to minimize the excessive oscillations in activity are generated in Parkinson’s disease.
Audio and video links
Sources of funding
Medical Research Council
Biotechnology and Biological Sciences Research Council
Theories of error back-propagation in the brain
BOGACZ R. and Whittington JCR., Trends in Cognitive Sciences
An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity
Whittington JCR. and Bogacz R., (2017), Neural Computation, 29, 1229 - 1262
Learning Reward Uncertainty in the Basal Ganglia
Mikhael JG. and Bogacz R., (2016), PLOS Computational Biology, 12, e1005062 - e1005062
Properties of Neurons in External Globus Pallidus Can Support Optimal Action Selection
Bogacz R. et al, (2016), PLOS Computational Biology, 12, e1005004 - e1005004
Computational Models Describing Possible Mechanisms for Generation of Excessive Beta Oscillations in Parkinson’s Disease
Pavlides A. et al, (2015), PLOS Computational Biology, 11, e1004609 - e1004609
Predicting the effects of deep brain stimulation using a reduced coupled oscillator model.
Weerasinghe G. et al, (2019), PLoS Comput Biol, 15
Predicting beta bursts from local field potentials to improve closed-loop DBS paradigms in Parkinson’s patients
Moraud EM. et al, (2018), 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Deep Brain Stimulation of the Subthalamic Nucleus Does Not Affect the Decrease of Decision Threshold during the Choice Process When There Is No Conflict, Time Pressure, or Reward
Leimbach F. et al, (2018), Journal of Cognitive Neuroscience, 30, 876 - 884
Time-varying decision boundaries: insights from optimality analysis
Malhotra G. et al, (2018), Psychonomic Bulletin & Review, 25, 971 - 996
Dendritic Integration of Sensory Evidence in Perceptual Decision-Making
Groschner LN. et al, (2018), Cell, 173, 894 - 905.e13