1 Input Prediction and Autonomous Movement Analysis in Recurrent Circuits of Spiking Neurons1

2 Temporal integration of information and prediction of future sensory inputs are assumed to be important computational tasks of generic cortical microcircuits. However it has remained open how cortical microcircuits could possibly achieve this, especially since they consist – in contrast to most neural network models – of neurons and synapses with heterogeneous dynamic responses. However it turns out that the diversity of computational units increases the capability of microcircuit models for temporal integration. Furthermore the prediction of future input may be rather easy for such circuits since it suffices to train the readouts from such microcircuits. In this article we show that very simple readouts from a generic recurrently connected circuit of integrate-and-fire neurons with diverse dynamic synapses can be trained in an unsupervised manner to predict movements of different objects, that move within an unlimited number of combinations of speed, angle, and offset over a simulated sensor field. The autonomously trained microcircuit model is also able to compute the direction of motion, which is a computationally difficult problem ("aperture problem") since i