ReplaceNet: real-time replacement of a biological neural circuit with a hardware-assisted spiking neural network.

ABSTRACT: Recent developments in artificial neural networks and their learning algorithms have enabled new research directions in computer vision, language modeling, and neuroscience. Among various neural network algorithms, spiking neural networks (SNNs) are well-suited for understanding the behavior of biological neural circuits. In this work, we propose to guide the training of a sparse SNN in order to replace a sub-region of a cultured hippocampal network with limited hardware resources. To verify our approach with a realistic experimental setup, we record spikes of cultured hippocampal neurons with a microelectrode array (in vitro). The main focus of this work is to dynamically cut unimportant synapses during SNN training on the fly so that the model can be realized on resource-constrain

SUBMITTER: Hwang S 

PROVIDER: S-EPMC10448768 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature