@inproceedings{1461cec0b6104cdda3db021fe216dc2a,
title = "Dynamic action inference with recurrent spiking neural networks",
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{\textquoteright}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.",
keywords = "Active inference, Recurrent spiking neural networks, Temporal gradients",
author = "Manuel Traub and Butz, {Martin V} and Robert Legenstein and Sebastian Otte",
year = "2021",
doi = "10.1007/978-3-030-86383-8_19",
language = "English",
isbn = "9783030863821",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "233--244",
editor = "Igor Farka{\v s} and Paolo Masulli and Sebastian Otte and Stefan Wermter",
booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings",
}