Sander Bohte

Full Name
Prof.dr. S.M. Bohte
+31 20 592 4074
Scientific Staff Member, Group leader, Coordinator Partnerships & Transfer
Sander Bohte


I develop computational models to understand information processing in neural networks. I strongly believe that neural networks and computational neuroscience models should "compute"; the challenge is to develop insights from neuroscience into usefully computing neural networks, and to bring machine learning insights into models of how neurons in the brain compute. I particularly focused on continuous-time information processing, where time is an explicit dimension of the problem domain. This includes networks of spiking neurons, predictive coding, interactive neural cognition, supervised neural learning, and (biologically plausible) deep reinforcement learning methods. I am also a part-time full professor of Computational Neuroscience at the University of Amsterdam, with the Swammerdam Institute of Life Sciences, and an honorary full-professor of Bio-inspired Neural Networks at the Rijksuniversiteit Groningen.


  • A key research interest is work on neural adaptation and predictive coding for optimal spiking information processing. An example is the notion of multiplicative adaptation for adaptive spike coding, which allows spiking neurons to efficiently encode analog signals over vastly different and rapidly changing dynamic ranges. Current work focuses framing such adaptation in terms of predictive coding, and applying this paradigm to standard learning theory.
  • We also work on biologically plausible policy gradient reinforcement learning of working memory, for so called Semi-Markov Decision Processes. In AuGMent, we show how synaptic tags combined with integrating neurons allow neural networks to learn sequences of tasks, closely mimicking the way monkeys learn these tasks. Recent work has shown how we can formulate learning and processing in RNNs like AuGMent in continuous-time, and implement this in spiking neural networks.
  • Other research relates to neural models of early vision and audition, where the dynamic properties of real neurons are crucial to understanding the relationship between spiking and neural information processing.
  • Active collaborations involve Matthias Brucklacher, Kwangjun Lee and Cyriel Pennartz at SILS, UvA; Lieke Ceton, Sami Mollard, Paolo Papale and Pieter Roelfsema, at NIN Amsterdam; and Lynn Soerensen and Steven Scholte at B&C, UvA.
  • In more applied machine learning efforts, we work on AI-based physics with Nikolaj Mucke, Ruud Henkes and Kees Oosterlee.

Talented students are always welcome to come and do their MSc-thesis work at CWI on Bio-inspired Deep Learning, either based on their own proposed ideas, or based some ready-made potential MSc thesis projects. In particular for projects that link (spiking) neural networks to biology, and also on work that explores (probabilistic) learning in spiking neural networks (SNNs), for example in connection with reservoir computing, or in projects on efficiently simulating large-scale SNNs, with for example GPUs. Feel free to contact me for more information.


All publications

Professional activities

  • Organizer: Systems Neuroscience Modeling Symposium - [SNMS]
  • Professor: Honorary professor of Bio-inspired Neural Networks, Rijksuniversiteit Groningen.
  • Editor: IEEE Trans. on Neural Networks and Learning Systems
  • Editor: モFrontiers in Neuroscience and Robotics and AIヤ, Frontiers
  • Professor: Cognitive Computational Neuroscience, University of Amsterdam
  • Professor: Bio-inspired Neural Networks, Rijksuniversiteit Groningen


  • Deputy task leader Human Brain Project (HBP) SGA3 WP3, T3.7 (2020)
  • Coordinator NWA ORC "Safety Solutions for Autonomous Vehicles" (ACT) (2020)
  • Research leader NWO TTW Perspectief programma "Efficient Deep Learning" (EDL) (2018)
  • NWO NAI ᅠ(PI) grant for appointment of PhD-student on Deep Spiking Vision (), 2014. (2014)
  • NWO NAI ᅠ(co-PI) grant for appointment of PhD student on Reward-basedlearning of Subroutines by Neural networks (2014)
  • NWO NAI ᅠ(advisor) grant for appointment of PhD student on Deep Learning for Robust Robot Control (2014)
  • NWO NAI ᅠ(advisor) grant for appointment of PhD student on Decentralized UAV Control (2014)
  • UvA ABC ᅠ(co-PI) grant for appointment of PhD-student on Plausible (2014)
  • ERCIM - ERCIM ᅠPlausible models of continuous time neural reinforcement (2013)
  • NWO IDEAS ᅠ - NWO IDEAS programme proposal Flexible Robot Brains selected for (2013)
  • Veni Innovational Research Grant NWO - Scalable Reinforcement Learning in Spiking Neural Networks (2004)

Current projects with external funding

  • Perceptive acting under uncertainty: safety solutions for autonomous systems (None)