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Related Experiment Videos

An analog memory circuit for spiking silicon neurons

J G Elias1, D P Northmore, W Westerman

  • 1Department of Electrical Engineering, University of Delaware, Newark 19716, USA.

Neural Computation
|February 15, 1997
PubMed
Summary

This study introduces a novel analog memory circuit for silicon neurons, enabling dynamic control of firing thresholds. This innovation allows neurons to adapt their excitability based on network activity, facilitating learning and complex behaviors.

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Area of Science:

  • Neuroscience
  • Electrical Engineering
  • Computational Neuroscience

Background:

  • Traditional artificial neurons often lack dynamic memory capabilities.
  • Controlling neuron excitability is crucial for simulating complex neural network functions.
  • Pulsatile inputs offer a method for modulating neural circuit states.

Purpose of the Study:

  • To develop a simple analog memory circuit for silicon neurons.
  • To investigate the control of neuron spike firing threshold using this memory circuit.
  • To demonstrate applications in neural network dynamics and learning.

Main Methods:

  • Designed and implemented a novel analog memory circuit.
  • Integrated the circuit into a silicon neuron model with a dendritic tree.

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  • Utilized pulsatile inputs to control the neuron's excitability.
  • Performed experiments to evaluate circuit performance and applications.

    • Main Results:

    • The analog memory circuit successfully controlled the silicon neuron's spike firing threshold.
    • Demonstrated regulation of neuron excitability over millisecond to minute timescales.
    • Showcased applications in temporal edge sharpening and bistable behavior.
    • Implemented a neural network exhibiting classical conditioning-like learning.

    Conclusions:

    • The developed analog memory circuit provides a versatile mechanism for controlling silicon neuron dynamics.
    • This circuit enables adaptive excitability, crucial for advanced neural computation.
    • The findings support the development of more sophisticated neuromorphic systems capable of learning and complex temporal processing.