Dynamic action inference with recurrent spiking neural networks

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

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

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.

Originalspracheenglisch
TitelArtificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings
Redakteure/-innenIgor Farkaš, Paolo Masulli, Sebastian Otte, Stefan Wermter
Seiten233-244
Seitenumfang12
DOIs
PublikationsstatusVeröffentlicht - 2021

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band12895 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

ASJC Scopus subject areas

  • Theoretische Informatik
  • Informatik (insg.)

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