Assessing pattern recognition performance of neuronal cultures through accurate simulation
Lagani G, Mazziotti R, Falchi F, Gennaro C, Cicchini Gm, Pizzorusso T, Cremisi F, Amato G
Computer vision simulation MEA STDP neural network artificial intelligence Computer Vision and Pattern Recognition (cs.CV) FOS: Computer and information sciences Bio-inspired Neural networks bioinspired artificial intelligence neuronal culture Spiking Computer Science - Computer Vision and Pattern Recognition
Previous work has shown that it is possible to train neuronal cultures on Multi-Electrode Arrays (MEAs), to recognize very simple patterns. However, this work was mainly focused to demonstrate that it is possible to induce plasticity in cultures, rather than performing a rigorous assessment of their pattern recognition performance. In this paper, we address this gap by developing a methodology that allows us to assess the performance of neuronal cultures on a learning task. Specifically, we propose a digital model of the real cultured neuronal networks; we identify biologically plausible simulation parameters that allow us to reliably reproduce the behavior of real cultures; we use the simulated culture to perform handwritten digit recognition and rigorously evaluate its performance; we also show that it is possible to find improved simulation parameters for the specific task, which can guide the creation of real cultures.
Source: INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING, pp. 726-729. Online, 4-6/05/2021
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@inproceedings{oai:it.cnr:prodotti:457530,
title = {Assessing pattern recognition performance of neuronal cultures through accurate simulation},
author = {Lagani G and Mazziotti R and Falchi F and Gennaro C and Cicchini Gm and Pizzorusso T and Cremisi F and Amato G},
doi = {10.1109/ner49283.2021.9441166 and 10.48550/arxiv.2012.10355},
booktitle = {INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING, pp. 726-729. Online, 4-6/05/2021},
year = {2021}
}
0000-0003-0171-4315
