Emergence of time persistence in an interpretable data-driven neural network model
Sebastien Wolf,Guillaume Le Goc,Georges Debregeas,Simona Cocco,Rémi Monasson · COSYNE 2022
Establishing accurate as well as interpretable models of neural networks activity is an open challenge in systems neuroscience. Here we infer an energy-based generative network model of the anterior rhombencephalic turning region (ARTR) of zebrafish larvae using calcium-imaging recordings of the spontaneous activity of hundreds of neurons. While our data-driven model is trained to solely reproduce the short-term statistics of the neural activity, its dynamics exhibits persistence on much longer time scales. The model's persistence time decreases with water temperature in agreement with neurona
Thu, Mar 17, 2022