Dynamic action inference with recurrent spiking neural networks

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

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings
EditorsIgor Farkaš, Paolo Masulli, Sebastian Otte, Stefan Wermter
Number of pages12
Publication statusPublished - 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12895 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Active inference
  • Recurrent spiking neural networks
  • Temporal gradients

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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