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Abstract <p>A Verilog-A model of a spiking neural network is developed, and its dopamine-like learning is carried out in solving the problem of recognizing the simplest images. The necessity of using unipolar pulses from postsynaptic neurons to implement dynamic plasticity of the “bell-shaped” and “anti-bell-shaped” types, as well as the positive effect of the inhibitory layer of neurons on the operation of the system, is shown. A hardware and software complex implementing this neural network and the dynamics of changes in the conductivity window of a memristor synaptic connection obtained with its help when emulating different “dopamine levels” in a neuromorphic system are presented.</p>

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Modeling of Hardware Dopamine-Like Learning in a Spiking Neural Network with Memristive Synaptic Weights

  • 期刊论文

  • 发表日期：

    2025年6月1日

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  • I. V. Alyaev,
  • I. A. Surazhevsky,
  • A. I. Iliasov,
  • V. V. Rylkov,
  • V. A. Demin

摘要

Abstract

A Verilog-A model of a spiking neural network is developed, and its dopamine-like learning is carried out in solving the problem of recognizing the simplest images. The necessity of using unipolar pulses from postsynaptic neurons to implement dynamic plasticity of the “bell-shaped” and “anti-bell-shaped” types, as well as the positive effect of the inhibitory layer of neurons on the operation of the system, is shown. A hardware and software complex implementing this neural network and the dynamics of changes in the conductivity window of a memristor synaptic connection obtained with its help when emulating different “dopamine levels” in a neuromorphic system are presented.