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Related Experiment Videos

Multiplying with neurons: compensation for irregular input spike trains by using time-dependent synaptic

G Bugmann1

  • 1NEC Fundamental Research Laboratories, Tsukuba-Ibaraki, Japan.

Biological Cybernetics
|January 1, 1992
PubMed
Summary

Leaky integrate-and-fire neurons can multiply signals, but irregular spike timing causes errors. Using time-dependent synaptic weights improves multiplier accuracy in neuronal computation.

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Area of Science:

  • Computational neuroscience
  • Neural computation

Background:

  • Leaky integrate-and-fire (LIF) neurons can function as multipliers by detecting input spike coincidences.
  • Irregular interspike delays in input spike trains lead to false coincidences, degrading multiplier performance.

Purpose of the Study:

  • To address the degradation of multiplier function in LIF neurons caused by irregular input spike trains.
  • To propose a mechanism for accurate neuronal computation without frequency decoding.

Main Methods:

  • Implementing time-dependent synaptic weights that reset to zero after each input spike.
  • Utilizing synaptic weights that recover with a time constant matching the excitatory postsynaptic potential (EPSP) decay time.

Main Results:

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  • Achieved EPSP amplitudes independent of input interspike delays.
  • Enabled neuronal computation to operate without relying on frequency decoding.

    • Conclusions:

    • Time-dependent synaptic weights offer a solution to improve multiplier accuracy in LIF neurons.
    • This mechanism allows for robust neuronal computation, unaffected by input timing irregularities.