Genetic Algorithmic Parameter Optimisation of a Recurrent Spiking Neural Network Model

03/30/2020
by   Ifeatu Ezenwe, et al.
0

Neural networks are complex algorithms that loosely model the behaviour of the human brain. They play a significant role in computational neuroscience and artificial intelligence. The next generation of neural network models is based on the spike timing activity of neurons: spiking neural networks (SNNs). However, model parameters in SNNs are difficult to search and optimise. Previous studies using genetic algorithm (GA) optimisation of SNNs were focused mainly on simple, feedforward, or oscillatory networks, but not much work has been done on optimising cortex-like recurrent SNNs. In this work, we investigated the use of GAs to search for optimal parameters in recurrent SNNs to reach targeted neuronal population firing rates, e.g. as in experimental observations. We considered a cortical column based SNN comprising 1000 Izhikevich spiking neurons for computational efficiency and biologically realism. The model parameters explored were the neuronal biased input currents. First, we found for this particular SNN, the optimal parameter values for targeted population averaged firing activities, and the convergence of algorithm by  100 generations. We then showed that the GA optimal population size was within  16-20 while the crossover rate that returned the best fitness value was  0.95. Overall, we have successfully demonstrated the feasibility of implementing GA to optimise model parameters in a recurrent cortical based SNN.

READ FULL TEXT

page 4

page 5

research
05/14/2021

Multi-Objective Optimisation of Cortical Spiking Neural Networks With Genetic Algorithms

Spiking neural networks (SNNs) communicate through the all-or-none spiki...
research
09/21/2020

A multi-agent model for growing spiking neural networks

Artificial Intelligence has looked into biological systems as a source o...
research
02/15/2022

Memory via Temporal Delays in weightless Spiking Neural Network

A common view in the neuroscience community is that memory is encoded in...
research
05/18/2023

SPENSER: Towards a NeuroEvolutionary Approach for Convolutional Spiking Neural Networks

Spiking Neural Networks (SNNs) have attracted recent interest due to the...
research
04/25/2019

Winner-Take-All Computation in Spiking Neural Networks

In this work we study biological neural networks from an algorithmic per...
research
03/28/2018

On the Algorithmic Power of Spiking Neural Networks

Spiking Neural Networks (SNN) are mathematical models in neuroscience to...
research
08/31/2015

A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers

Learning Classifier Systems (LCS) are population-based reinforcement lea...

Please sign up or login with your details

Forgot password? Click here to reset