Bio-realistic neural network implementation on Loihi 2 with Izhikevich neurons

Recep Buğra Uludağ*, Serhat Çağdaş, Yavuz Selim İşler, Neslihan Serap Şengör, İsmail Aktürk*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Neuromorphic systems are designed to emulate the principles of biological information processing, with the goals of improving computational efficiency and reducing energy usage. A critical aspect of these systems is the fidelity of neuron models and neural networks to their biological counterparts. In this study, we implemented the Izhikevich neuron model on Intel’s Loihi 2 neuromorphic processor. The Izhikevich neuron model offers a more biologically accurate alternative to the simpler leaky-integrate and fire model, which is natively supported by Loihi 2. We compared these two models within a basic two-layer network, examining their energy consumption, processing speeds, and memory usage. Furthermore, to demonstrate Loihi 2’s ability to realize complex neural structures, we implemented a basal ganglia circuit to perform a Go/No-Go decision-making task. Our findings demonstrate the practicality of customizing neuron models on Loihi 2, thereby paving the way for constructing spiking neural networks that better replicate biological neural networks and have the potential to simulate complex cognitive processes.

Original languageEnglish
Article number024013
JournalNeuromorphic Computing and Engineering
Volume4
Issue number2
DOIs
Publication statusPublished - 1 Jun 2024

Bibliographical note

Publisher Copyright:
© 2024 The Author(s). Published by IOP Publishing Ltd.

Keywords

  • Izhikevich neuron
  • Loihi 2
  • basal ganglia circuit
  • energy efficiency
  • neuromorphic processor

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