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
- Senior Research Fellow in Computational Neuroscience at the Nuffield Department of Clinical Neurosciences
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. Although many questions remain open, the anatomy and the neural activity in the basal ganglia has been characterized to the extent that allows formulating a formal mathematical theory describing how it selects actions in the healthy brain, and how the pathological patterns of activity observed in Parkinson’s disease are generated.
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.
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 Comput, 29, 1229 - 1262
Learning Reward Uncertainty in the Basal Ganglia.
Mikhael JG. and Bogacz R., (2016), PLoS Comput Biol, 12
Properties of Neurons in External Globus Pallidus Can Support Optimal Action Selection.
Bogacz R. et al, (2016), PLoS Comput Biol, 12
Computational Models Describing Possible Mechanisms for Generation of Excessive Beta Oscillations in Parkinson's Disease.
Pavlides A. et al, (2015), PLoS Comput Biol, 11
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), J Cogn Neurosci, 30, 876 - 884
Time-varying decision boundaries: insights from optimality analysis.
Malhotra G. et al, (2018), Psychon Bull Rev, 25, 971 - 996
Dendritic Integration of Sensory Evidence in Perceptual Decision-Making.
Groschner LN. et al, (2018), Cell, 173, 894 - 905.e13
Mechanisms Underlying Decision-Making as Revealed by Deep-Brain Stimulation in Patients with Parkinson's Disease.
Herz DM. et al, (2018), Curr Biol, 28, 1169 - 1178.e6