Event Abstract Back to Event Recurrent Inhibitory Coupling Improves Discrimination of Temporal Spike Patterns Chun-Wei Yuan1, 2* and Christian Leibold1, 3 1 Ludwig Maximilians Universitaet, Division of Neurobiology, Department of Biology II, Germany 2 Bernstein Focus Neuronal Basis of Learning: Plasticity of Neuronal Dynamics, Germany 3 Bernstein Center for Computational Neuroscience Munich, Germany Inhibitory neurons are considered to play a central role as rhythm generator and in shaping feed-forward receptive fields. While much attention has been paid to such effects on excitatory neurons, little is done to study these inhibitory neurons' ability to directly process information. Here we present a linear classification model that investigates the inner workings of a recurrent inhibitory neural network. Our work focuses on quantifying the performance of a recurrent network of inhibitory integrate-and-fire neurons in canonical classification tasks. The model begins with parallel independent excitatory Poisson inputs connected to the recurrent network. Then, the network output is feed-forwardly directed to a read-out linear classifier. The analysis is then conducted as a function of variables such as inhibitory weight, read-out delay, etc. to shed light on the principles behind the network’s computational capacity. It is found that there is an optimum weight amongst the inhibitory neurons that yields the best performance, and this improvement could be as much as 30%. The optimum weight is where the sum of the network’s mutual information (MI) and binary MI is the highest, and it occurs when the network’s response to a stimulus is about 40%-50% silent. This illustrates how a recurrent neural network may optimize its topological parameters to obtain more computational capacity than a simple feed-forward network [1,2].