Name
Title | ||
|---|---|---|
Assessing Pattern Recognition Performance Of Neuronal Cultures Through Accurate Simulation |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/NER49283.2021.9441166 | 2021 10TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER) |
DocType | ISSN | Citations |
Conference | 1948-3546 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriele Lagani | 1 | 1 | 1.76 |
| Raffaele Mazziotti | 2 | 0 | 0.34 |
| Fabrizio Falchi | 3 | 459 | 55.65 |
| Claudio Gennaro | 4 | 490 | 57.23 |
| Guido Marco Cicchini | 5 | 0 | 0.34 |
| Tommaso Pizzorusso | 6 | 0 | 0.34 |
| Federico Cremisi | 7 | 0 | 0.34 |
| Giuseppe Amato | 8 | 505 | 106.68 |