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Sparse distributed memory using rank-order neural codes.

Stephen B Furber1, Gavin Brown, Joy Bose

  • 1School of Computer Science, the University of Manchester, Manchester M 13 9PL, UK. steve.furber@manchester.ac.uk

IEEE Transactions on Neural Networks
|May 29, 2007
PubMed
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This study introduces a novel sparse distributed memory (SDM) capable of storing and recalling rank-order information using neural spike timings. The memory demonstrates noise-reduction capabilities and potential implementation with spiking neurons.

Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Memory systems

Background:

  • Human memory may encode information in neural spike timings.
  • Existing memory models often focus on the subset of firing neurons, not their order.

Purpose of the Study:

  • To present a sparse distributed memory (SDM) variant capable of storing and recalling rank-order information.
  • To investigate the memory's performance under noisy conditions.
  • To explore its potential implementation using spiking neurons.

Main Methods:

  • Utilized a Hebbian single-shot learning algorithm to store rank-order information in static synaptic weights.
  • Employed unipolar binary connections for memory operation.
  • Analyzed the memory's behavior and noise-filtering capabilities with simulated noisy inputs.

Related Experiment Videos

Main Results:

  • The SDM variant successfully stored and recalled patterns with rank-order information.
  • The memory demonstrated noise-reduction properties, improving data quality under certain conditions.
  • The architecture is compatible with unipolar binary connections and potentially spiking neurons.

Conclusions:

  • This memory architecture can store and retrieve information encoded in the timing of neural activity.
  • The model exhibits noise-filtering capabilities, enhancing data stability.
  • It offers a potential framework for implementing robust memory systems using spiking neurons.