Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity

PLoS Comput Biol. 2013 Apr;9(4):e1003037. doi: 10.1371/journal.pcbi.1003037. Epub 2013 Apr 25.


The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other via interneurons, are a common motif of cortical microcircuits. We show through theoretical analysis and computer simulations that Bayesian computation is induced in these network motifs through STDP in combination with activity-dependent changes in the excitability of neurons. The fundamental components of this emergent Bayesian computation are priors that result from adaptation of neuronal excitability and implicit generative models for hidden causes that are created in the synaptic weights through STDP. In fact, a surprising result is that STDP is able to approximate a powerful principle for fitting such implicit generative models to high-dimensional spike inputs: Expectation Maximization. Our results suggest that the experimentally observed spontaneous activity and trial-to-trial variability of cortical neurons are essential features of their information processing capability, since their functional role is to represent probability distributions rather than static neural codes. Furthermore it suggests networks of Bayesian computation modules as a new model for distributed information processing in the cortex.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Action Potentials / physiology*
  • Animals
  • Bayes Theorem
  • Brain / physiology
  • Computational Biology / methods
  • Computer Simulation
  • Humans
  • Models, Neurological
  • Nerve Net / physiology
  • Neuronal Plasticity / physiology*
  • Neurons / physiology
  • Probability
  • Synapses / physiology
  • Synaptic Transmission / physiology

Grants and funding

This work was written under partial support by project #FP7-216593 (SECO), project #FP7-506778 (PASCAL2), project #FP7-243914 (BRAIN-I-NETS) and project #FP7-269921 (BrainScaleS) of the European Union. MP has been supported by the Samsung Advanced Institute of Technology and a Forschungskredit grant of the University of Zurich. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.