Spike-based symbolic computations on bit strings and numbers

Ceca Kraisnikovic, Wolfgang Maass*, Robert Legenstein*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


The brain uses recurrent spiking neural networks for higher cognitive functions such as symbolic computations, in particular, mathematical computations. We review the current state of research on spike-based symbolic computations of this type. In addition, we present new results which show that surprisingly small spiking neural networks can perform symbolic computations on bit sequences and numbers and even learn such computations using a biologically plausible learning rule. The resulting networks operate in a rather low firing rate regime, where they could not simply emulate artificial neural networks by encoding continuous values through firing rates. Thus, we propose here a new paradigm for symbolic computation in neural networks that provides concrete hypotheses about the organization of symbolic computations in the brain. The employed spike-based network models are the basis for drastically more energy-efficient computer hardware - neuromorphic hardware. Hence, our results can be seen as creating a bridge from symbolic artificial intelligence to energy-efficient implementation in spike-based neuromorphic hardware.

Original languageEnglish
Title of host publicationNeuro-Symbolic Artificial Intelligence
Subtitle of host publicationThe State of the Art
EditorsPascal Hitzler, Md Kamruzzaman Sarker
PublisherIOS Press
ChapterChapter 9
Number of pages21
ISBN (Electronic)9781643682440
Publication statusPublished - 2022

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389

ASJC Scopus subject areas

  • Artificial Intelligence


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