Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.05.14.095703v1?rss=1 Authors: Zhang, Y., Yang, L.-S. Abstract: This paper is about the use of Deep Neural Networks (DNN) to assist in the statistical analysis of a network of a few hundred integrate-and-fire neurons intended to model local circuits in the cerebral cortex. Using training data produced by direct numerical simulations, our first task was to discover, with the aid of a DNN, the mapping that yields model response for each set of parameters and input values. After evaluating the performance of the DNN surrogate both in the accuracy of its outputs and in its performance in parameter tuning, we analyzed the outputs of the well-trained DNN to gain insight into local circuits as basic cortical computational units. Because the DNN surrogate computed with vastly higher speeds than actual simulations of the neuronal network, we were able to sample large sets of parameters and input values to produce a broad statistical picture of input-output relations. One of the aims of this paper is to demonstrate that statistical analyses of this kind can provide general theoretical information on model behavior as well as suggest cortical mechanisms. Among our results are the following: Through a derivative analysis of model responses we identified a certain dichotomy in the behavior of I-neurons, leading to a characterization of high gain states which in turn offered insight into mechanisms for surround suppression. A second-derivative analysis revealed limitations of models of integrate-and-fire neurons, namely their inability to replicate the nonlinearities in gain curves observed in real neurons. Copy rights belong to original authors. Visit the link for more info