{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T21:40:12Z","timestamp":1649194812716},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,22]]},"abstract":"<jats:p>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 \u2013 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.<\/jats:p>","DOI":"10.3233\/faia210356","type":"book-chapter","created":{"date-parts":[[2022,1,3]],"date-time":"2022-01-03T10:43:45Z","timestamp":1641206625000},"source":"Crossref","is-referenced-by-count":0,"title":["Chapter 9. Spike-Based Symbolic Computations on Bit Strings and Numbers"],"prefix":"10.3233","author":[{"given":"Ceca","family":"Krai\u0161nikovi\u0107","sequence":"first","affiliation":[{"name":"Institute of Theoretical Computer Science, Graz University of Technology, Inffeldgasse 16b, Graz, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wolfgang","family":"Maass","sequence":"additional","affiliation":[{"name":"Institute of Theoretical Computer Science, Graz University of Technology, Inffeldgasse 16b, Graz, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Legenstein","sequence":"additional","affiliation":[{"name":"Institute of Theoretical Computer Science, Graz University of Technology, Inffeldgasse 16b, Graz, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Neuro-Symbolic Artificial Intelligence: The State of the Art"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA210356","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,3]],"date-time":"2022-01-03T10:43:46Z","timestamp":1641206626000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA210356"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,22]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia210356","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,22]]}}}