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Bio-realistic neural network implementation on loihi 2 with izhikevich neurons

Title	Bio-realistic neural network implementation on loihi 2 with izhikevich neurons
Author	Akturk, Ismail, Sengor, N. S., Isler, Y. S., Cagdas, S., Uludag, Recep Bugra
Publication Date:	2024-06-18
Publication Place	- IOP Publishing
Subject	Energy efficiency, Basal ganglia circuit, Izhikevich neuron, Loihi 2, Neuromorphic processor
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	2634-4386
Record ID	305524d9-0d78-4132-ac1c-accc4f198d52
Library Location	Computer Science
Date	2024-06-18
Notes	Intel's Neuromorphic Research Community (INRC) ; ITU Scientific Research Projects Office
Sample Text	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.
DOI	10.1088/2634-4386/ad5584
Cilt	4

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Bio-realistic neural network implementation on loihi 2 with izhikevich neurons

Author Akturk, Ismail, Sengor, N. S., Isler, Y. S., Cagdas, S., Uludag, Recep Bugra

Publication Date 2024-06-18

Publication Place - IOP Publishing

Subject Energy efficiency, Basal ganglia circuit, Izhikevich neuron, Loihi 2, Neuromorphic processor

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 2634-4386

Record ID 305524d9-0d78-4132-ac1c-accc4f198d52

Library Location Computer Science

Date 2024-06-18

Notes Intel's Neuromorphic Research Community (INRC) ; ITU Scientific Research Projects Office

Sample Text 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.

DOI 10.1088/2634-4386/ad5584

Cilt 4