Dynamic Action Inference with Recurrent Spiking Neural Networks

Manuel Traub, Martin V. Butz, Robert Legenstein, Sebastian Otte

Abstract: In this paper, we demonstrate that goal-directed behavior unfolds in recurrent spiking neural networks (RSNNs) when intentions are projected onto continuously progressing spike dynamics encoding the recent history of an agent’s state. The projections, which can either be realized via backpropagation through time (BPTT) over a certain time window or even directly and temporally local in an online fashion using a biologically inspired inference rule. In contrast to previous studies that use, for instance, LSTM-like models, our approach is biologically more plausible as it fully relies on spike-based processing of sensorimotor experiences. Specifically, we show that precise control of a flying vehicle in a 3D environment is possible. Moreover, we show that more complex mental traces of foresighted movement imagination unfold that effectively help to circumvent learned obstacles.