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First Passage Time Memory Lifetimes for Simple, Multistate Synapses.

Terry Elliott1

  • 1Department of Electronics and Computer Science, University of Southampton, Highfield, Southampton, SO17 1BJ, U.K. te@ecs.soton.ac.uk.

Neural Computation
|September 29, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces the first passage time (FPT) distribution for multistate synapses, offering a superior method for defining memory lifetimes over signal-to-noise ratio (SNR) definitions. The findings reveal that optimality conditions in memory models are often artifacts of the SNR approach.

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Area of Science:

  • Computational neuroscience
  • Theoretical neuroscience
  • Synaptic plasticity

Background:

  • Synaptic memory models with discrete strengths face trade-offs between storing new memories and forgetting old ones.
  • Existing memory lifetime definitions, such as signal-to-noise ratio (SNR), present limitations and potential artifacts, especially with multistate synapses.
  • Previous work established mean first passage time (MFPT) as a more robust metric for binary-strength synapses.

Purpose of the Study:

  • To compute the entire first passage time (FPT) distribution for simple, multistate synapses.
  • To analyze memory lifetime statistics, including MFPT, derived from the FPT distribution.
  • To investigate the presence and model-dependence of optimality conditions in memory models using both MFPT and SNR lifetime definitions.

Main Methods:

  • Development of a Fokker-Planck equation utilizing jump moments for perceptron activation.
  • Analysis of two distinct multistate synapse models satisfying a specific eigenvector condition.
  • Computation and comparison of FPT distributions, MFPT lifetimes, and SNR lifetimes across models.

Main Results:

  • MFPT lifetimes in the studied models do not exhibit optimality conditions.
  • SNR lifetimes show optimality in one model but not the other, highlighting their artifactual and model-dependent nature.
  • Examination of FPT variance identifies regions of high memory storage variability where SNR lifetimes are often zero, unlike robustly positive MFPT lifetimes.

Conclusions:

  • The FPT-defined memory lifetime offers an analytically superior approach compared to SNR.
  • Optimality conditions observed in some memory models are artifacts of the SNR definition and are strongly model-dependent.
  • FPT-defined memory lifetimes are directly linked to neuronal firing properties, providing a more biologically relevant measure.