Functional identification of biological neural networks using reservoir adaptation for point processes

Authors:
Tayfun Gürel;Stefan Rotter;Ulrich Egert
Affiliations:
Bernstein Center for Computational Neuroscience Freiburg and Institute for Computer Science, Albert-Ludwig University, Freiburg, Germany;Bernstein Center for Computational Neuroscience Freiburg and Faculty of Biology, Albert-Ludwig University, Freiburg, Germany;Department of Microsystems Engineering, Albert-Ludwig University, Freiburg, Germany and Bernstein Center for Computational Neuroscience, Albert-Ludwig University, Freiburg, Germany and Bernstein F ...
Venue:
Journal of Computational Neuroscience
Year:
2010

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Abstract

The complexity of biological neural networks does not allow to directly relate their biophysical properties to the dynamics of their electrical activity. We present a reservoir computing approach for functionally identifying a biological neural network, i.e. for building an artificial system that is functionally equivalent to the reference biological network. Employing feed-forward and recurrent networks with fading memory, i.e. reservoirs, we propose a point process based learning algorithm to train the internal parameters of the reservoir and the connectivity between the reservoir and the memoryless readout neurons. Specifically, the model is an Echo State Network (ESN) with leaky integrator neurons, whose individual leakage time constants are also adapted. The proposed ESN algorithm learns a predictive model of stimulus-response relations in in vitro and simulated networks, i.e. it models their response dynamics. Receiver Operating Characteristic (ROC) curve analysis indicates that these ESNs can imitate the response signal of a reference biological network. Reservoir adaptation improved the performance of an ESN over readout-only training methods in many cases. This also held for adaptive feed-forward reservoirs, which had no recurrent dynamics. We demonstrate the predictive power of these ESNs on various tasks with cultured and simulated biological neural networks.