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Stochastic variational learning in recurrent spiking networks.

Danilo Jimenez Rezende1, Wulfram Gerstner1

  • 1Laboratory of Cognitive Neuroscience, School of Life Sciences, Brain Mind Institute, Ecole Polytechnique Federale de Lausanne Lausanne, Vaud, Switzerland ; Laboratory of Computational Neuroscience, School of Computer and Communication Sciences, Ecole Polytechnique Federale de Lausanne Lausanne, Vaud, Switzerland.

Frontiers in Computational Neuroscience
|April 29, 2014
PubMed
Summary
This summary is machine-generated.

Researchers developed a novel learning rule for spiking neural networks, enabling them to perform statistical inference and learn patterns. This biologically plausible model advances understanding of neural computation and perception.

Keywords:
action potentialsneural networksspiking neuronssynapsesvariational learning

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Area of Science:

  • Computational neuroscience
  • Machine learning
  • Artificial neural networks

Background:

  • Understanding biological learning mechanisms is crucial for advancing artificial intelligence.
  • Spiking neural networks (SNNs) offer a biologically plausible model for neural computation.
  • Statistical inference in complex neural systems remains a challenge.

Purpose of the Study:

  • To derive and investigate a new learning rule for recurrent spiking networks with hidden neurons.
  • To combine principles from variational learning and reinforcement learning for neural network training.
  • To develop a biologically plausible model for statistical inference in SNNs.

Main Methods:

  • Derivation of a novel learning rule integrating variational and reinforcement learning.
  • Implementation of a generative model for spike train histories in recurrent SNNs.
  • Simulation of the learning rule on stationary and non-stationary spike train data.

Main Results:

  • The derived learning rule functions as a local Spike Timing Dependent Plasticity (STDP) rule.
  • Global neuromodulatory factors convey 'novelty' information within the learning rule.
  • The model successfully learned both stationary and non-stationary spike train patterns.

Conclusions:

  • The proposed learning rule provides a statistically rigorous framework for SNNs.
  • The model demonstrates the potential for SNNs to perform complex statistical inference.
  • An experimental paradigm is proposed to validate the novelty signal in biological systems.