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The complexity of biological neural networks (BNN) necessitates automated methods for investigating their stimulus-response and structure-dynamics relations. In the present work, we aim at building a functionally equivalent network to a reference BNN. The response signal of the BNN to various input streams is regarded as a characterization of its function. Therefore, we train an artificial system that imitates the input-output relation of the reference BNN under the applied stimulus range. In other words, we take a system identification approach for biological neural networks. Generic network models with fixed random connectivity, recurrent dynamics and fading memory, reservoirs, were shown to have a strong separation property on various input streams. Equipped with additional simple readout units, such systems have been successfully applied to several nonlinear modeling and engineering tasks [1].

Reservoir computing methods for functional identification of biological networks