Empirical modeling and prediction of neuronal dynamics

We present an optimized computational scheme that trains the ANN with biologically plausible input currents. We obtain successful identification for data generated from four different neuron models when using all variables as inputs of the network. We also show that the empiric model obtained is able to generalize and predict the neuronal dynamics generated by variable input currents different from those used to train the artificial network. In the more realistic situation of using only the voltage and the injected current as input data to train the network, we lose predictive ability but, for low-dimensional models, the results are still satisfactory. We understand our contribution as a first step toward obtaining empiric models from experimental voltage traces.PMID:38597964 | DOI:10.1007/s00422-024-00986-z

Source: Biological Cybernetics - April 10, 2024 Category: Science Authors: Pau Fisco-Compte David Aquilu é-Llorens Nestor Roqueiro Enric Fossas Antoni Guillamon Source Type: research