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A Conductance-Based Silicon Synapse Circuit.

Ashish Gautam1, Takashi Kohno1

  • 1Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan.

Biomimetics (Basel, Switzerland)
|December 22, 2022
PubMed
Summary

This study introduces a new analog silicon synapse circuit for neuromorphic chips, enabling more bio-realistic neuron models and demonstrating shunting inhibition. The circuit successfully performs a spike pattern detection task using adaptive STDP learning.

Keywords:
adaptive STDPbiomimetic synapse circuitneuromorphic computingshunting inhibitionspike pattern detectionsynaptic resolutionsynaptic reversal potential

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Area of Science:

  • Neuroscience
  • Electrical Engineering
  • Computer Science

Background:

  • Neuromorphic chips mimic brain function using neuron, synapse, and learning circuits.
  • Existing low-power analog synapse circuits typically use current-based models, suitable for simpler neuron models.
  • These current-based models often neglect the postsynaptic membrane potential's effect, limiting emulation of complex neural processes like shunting inhibition.

Purpose of the Study:

  • To present a novel conductance-based analog silicon synapse circuit.
  • To enable the implementation of reduced or multi-compartment, bio-realistic neuron models in neuromorphic systems.
  • To incorporate the crucial effect of postsynaptic membrane potential for phenomena like shunting inhibition.

Main Methods:

  • Developed a conductance-based analog silicon synapse circuit utilizing an oscillator-based resistor-type element.
  • Integrated the synapse circuit with a biomimetic soma circuit on a mixed-signal chip.
  • Tested the circuit's ability to demonstrate shunting inhibition and perform a spatiotemporal spike pattern detection task using adaptive spike-timing-dependent plasticity (STDP).

Main Results:

  • The proposed circuit successfully demonstrated the shunting inhibition phenomenon.
  • The synapse circuit, when integrated into a chip with 256 units, effectively performed a spatiotemporal spike pattern detection task.
  • The adaptive STDP learning rule was successfully employed within the neuromorphic chip architecture.

Conclusions:

  • The developed analog silicon synapse circuit is suitable for implementing bio-realistic multi-compartment neuron models.
  • The circuit effectively incorporates shunting inhibition, a vital aspect of brain information processing.
  • This work advances neuromorphic engineering by providing a more biologically plausible synapse for advanced neural network emulation.