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Related Experiment Video

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Spatio-temporal pattern recognizers using spiking neurons and spike-timing-dependent plasticity.

James Humble¹, Susan Denham, Thomas Wennekers

¹Centre for Robotic and Neural Systems, Cognition Institute, Plymouth University Plymouth, UK.

Frontiers in Computational Neuroscience

|October 23, 2012

Summary

This summary is machine-generated.

This study shows how spike-timing-dependent plasticity (STDP) can train neural networks to recognize complex spatio-temporal patterns. By forming connected neuron chains, the model learns sequences and demonstrates robustness to timing variations.

Keywords:

neural automata sequence learning spike-timing-dependent plasticity spiking neurons synfire chains

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Area of Science:

Computational Neuroscience
Machine Learning

Background:

Spike-timing-dependent plasticity (STDP) enables neurons to adapt to repeating input patterns.
Extending STDP for recognizing longer spatio-temporal signals is a key challenge.

Purpose of the Study:

To demonstrate how STDP can train neural networks to recognize extended spatio-temporal input signals.
To investigate different STDP rules and their impact on sequence learning.

Main Methods:

Utilized mutually connected neurons with plastic synapses and a winner-takes-all mechanism.
Implemented nearest-neighbor STDP and all-to-all STDP for chain formation.
Investigated stitching learned chains to form longer sequence recognizers.

Main Results:

Nearest-neighbor STDP formed sequential 'synfire' chains.
All-to-all STDP created multi-stage connections respecting temporal order.
Chains were stitched to recognize longer, potentially nested, sequences.
Recognition robustness depended on synaptic properties and membrane noise.

Conclusions:

STDP is a viable mechanism for training sequence recognizers.
Different STDP rules yield distinct network architectures and capabilities.
The model exhibits high memory capacity, potentially enhanced by sparse coding.