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Aversive Associative Learning and Memory Formation by Pairing Two Chemicals in Caenorhabditis elegans
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Associative memory model with long-tail-distributed Hebbian synaptic connections.

Naoki Hiratani1, Jun-Nosuke Teramae, Tomoki Fukai

  • 1Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo Kashiwa, Japan ; Laboratory for Neural Circuit Theory, RIKEN Brain Science Institute Wako, Japan.

Frontiers in Computational Neuroscience
|February 14, 2013
PubMed
Summary

Heavy-tailed synaptic weight distributions in neural networks, like those found in the brain, can generate beneficial internal noise. This noise optimizes associative memory recall by improving signal processing in spiking neural networks.

Keywords:
attractorhippocampusintegrate-and-firemean-fieldstochastic resonancestorage capacity

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Published on: August 11, 2019

Area of Science:

  • Computational neuroscience
  • Neuroscience
  • Artificial intelligence

Background:

  • Pyramidal neuron postsynaptic potentials exhibit heavy-tailed, non-Gaussian amplitude distributions in the hippocampus and neocortex.
  • Such synaptic weight distributions were previously found to optimize spike propagation in recurrent cortical circuits by generating internal noise.

Purpose of the Study:

  • To investigate if heavy-tailed weight distributions can generate beneficial internal noise for associative memory (AM) networks.
  • To explore the role of internal noise in memory recall within spiking neural network models.

Main Methods:

  • Constructed a spiking neuron associative memory network model.
  • Implemented a lognormal weight distribution for the connection matrix.
  • Analyzed subthreshold membrane-potential distributions in neurons encoding retrieved versus non-retrieved memory patterns.

Main Results:

  • Demonstrated distinct subthreshold membrane-potential distributions for neurons encoding retrieved and non-retrieved memory patterns.
  • Showed that heavy-tailed distributions increase response probability to strong synapses for encoding neurons, while decreasing it for non-encoding neurons.
  • Identified that cross-talk noise in AM networks can be modulated by weight distributions.

Conclusions:

  • Heavy-tailed weight distributions can generate useful internal noise for associative memory recall.
  • This noise mechanism enhances memory retrieval by differentiating between relevant and irrelevant information.
  • The findings suggest a broader computational role for non-Gaussian synaptic weight distributions in neural systems.