Genetic algorithmic parameter optimisation of a recurrent spiking neural network model

Ifeatu Ezenwe, Alok Joshi, KongFatt Wong-Lin

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)
129 Downloads (Pure)


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 optimize model parameters in a recurrent cortical based SNN.
Original languageEnglish
Number of pages6
Publication statusPublished (in print/issue) - 31 Aug 2020
EventISSC 2020 - 31st Irish Signals and Systems Conference: ISSC 2020 - Letterkenny Institute of Technology (virtual/online), Letterkenny, Ireland
Duration: 11 Jun 202012 Jun 2020


ConferenceISSC 2020 - 31st Irish Signals and Systems Conference
Internet address


  • Izhikevich neuronal model
  • Model parameter optimisation
  • genetic algorithm
  • recurrent spiking neural network model


Dive into the research topics of 'Genetic algorithmic parameter optimisation of a recurrent spiking neural network model'. Together they form a unique fingerprint.

Cite this