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Event-driven contrastive divergence for spiking neuromorphic systems.

Emre Neftci1, Srinjoy Das2, Bruno Pedroni3

  • 1Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA.

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|February 28, 2014
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Summary

This study introduces an event-driven training method for Restricted Boltzmann Machines (RBMs) using spiking neurons, enabling efficient learning on neuromorphic hardware. The approach utilizes neural sampling and Spike Time Dependent Plasticity (STDP) for practical applications.

Keywords:
Markov chain monte carlogenerative modelneuromorphic cognitionrecurrent neural networksynaptic plasticity

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Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Neuromorphic Engineering

Background:

  • Restricted Boltzmann Machines (RBMs) and Deep Belief Networks excel in tasks like dimensionality reduction and feature learning.
  • Neuromorphic hardware offers advantages in scalability, power efficiency, and real-time interaction for large-scale neural networks.
  • Traditional RBMs and Contrastive Divergence (CD) training lack direct compatibility with dynamical, spiking neural substrates due to discrete updates.

Purpose of the Study:

  • To develop an event-driven training algorithm for RBMs compatible with neuromorphic hardware constraints.
  • To enable RBMs to be implemented using Integrate & Fire (I&F) spiking neurons.
  • To create a spiking neural network capable of sampling from a target Boltzmann distribution.

Main Methods:

  • An event-driven variation of Contrastive Divergence (CD) was developed for training RBMs with Integrate & Fire (I&F) neurons.
  • Neural sampling was employed, where the recurrent activity of the spiking network replaces discrete CD steps.
  • Spike Time Dependent Plasticity (STDP) was used for online, asynchronous weight updates.

Main Results:

  • A Restricted Boltzmann Machine (RBM) composed of leaky I&F neurons trained with STDP successfully learned a generative model of the MNIST handwritten digit dataset.
  • The trained RBM demonstrated capabilities in recognition, generation, and cue integration tasks.
  • The approach successfully synthesized a spiking neural network for practical, high-level machine learning functionalities.

Conclusions:

  • The proposed event-driven CD training method is effective for RBMs implemented on neuromorphic hardware using spiking neurons.
  • This work bridges the gap between traditional machine learning algorithms and biologically plausible neural computation.
  • The findings support a machine learning-driven approach for creating functional spiking neural networks for real-world applications.