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Bayesian spiking neurons II: learning.

Sophie Deneve1

  • 1Département d'Etudes Cognitives, Ecole Normale Supérieure, College de France 75005 Paris, France. sophie.deneve@ens.fr

Neural Computation
|November 30, 2007
PubMed
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This study presents Bayesian learning in spiking neural networks, enabling neurons to recognize temporal input dynamics and build hierarchical causal models. This approach optimizes information transfer while minimizing energy-intensive spikes.

Area of Science:

  • Computational neuroscience
  • Machine learning
  • Artificial neural networks

Background:

  • Spiking neural dynamics can be viewed as Bayesian integration, accumulating evidence over time.
  • Previous work established Bayesian inference in spiking neurons.

Purpose of the Study:

  • To develop a theory of Bayesian learning in spiking neural networks.
  • To enable neurons to learn temporal dynamics of synaptic inputs.
  • To establish hierarchical causal models for sensory input in successive neural layers.

Main Methods:

  • Developing a theory of Bayesian learning for spiking neural networks.
  • Implementing a learning rule that is local, spike-time dependent, and nonlinear.
  • Analyzing rules that maximize information transfer between neural layers.

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Main Results:

  • Neurons learn to recognize temporal dynamics of synaptic inputs.
  • Successive layers of neurons learn hierarchical causal models for sensory input.
  • The learning rule is local, spike-time dependent, and highly nonlinear.

Conclusions:

  • The proposed approach offers a principled description of spiking and plasticity rules.
  • Maximizes information transfer between successive neural layers.
  • Efficiently limits the number of costly spikes.