Abstract

This paper describes a digital silicon neuronal network trained by the Hebbian learning rule that can execute the auto-associative memory. In our previous work, a fully connected network of 256 silicon neurons based on the digital spiking silicon neuron (DSSN) model and kinetic-model-based silicon synapses were implemented. In this work, we added circuit modules that append Hebbian learning function and fitted it to a Xilinx Virtex 6 XC6VSX315T FPGA device. The performances of auto-associative memory with several spike-time-dependent Hebbian learning rules and the correlation rule are compared. The results show that Hebbian learning rules that model both synaptic potentiation and depression improve the retrieval probability in our silicon neuronal network.

Highlights

  • Sensory experiences are thought to be configuring the nerve system

  • The modification that lasts for long time has two types: long-term potentiation (LTP) and long-term depression (LTD) which respectively leads to reinforced and weakened synapses

  • The model of our silicon neuronal network is composed of the Digital Spiking Silicon Neuron (DSSN) model(12) and a silicon synapse model proposed in our previous work(13)

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Summary

Introduction

Sensory experiences are thought to be configuring the nerve system. The synaptic plasticity underlies this phenomenon, which refers to the change of the connection strength between two neurons. We proposed a silicon neuronal network implemented in an FPGA device It is composed of silicon neurons and synapses that are optimized for implementation by digital arithmetical circuits and capable of real-time operation in entry-level FGPA devices. The model of these silicon neurons was designed in the viewpoint of nonlinear dynamics and can reproduce the graded spike response in the Class II neurons in the Hodgkin’s classification(11).

Hebbian learning rules
Model of silicon neuronal network
FPGA implementation
Configuration of the network
Simulation results
Conclusions

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