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Spike frequency adaptation supports network computations on temporally dispersed information

Darjan Salaj, View ORCID ProfileAnand Subramoney, Ceca Kraišniković, Guillaume Bellec, Robert Legenstein, Wolfgang Maass
doi: https://doi.org/10.1101/2020.05.11.081513
Darjan Salaj
1Institute of Theoretical Computer Science, Graz University of Technology, Inffeldgasse 16b, Graz, Austria
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Anand Subramoney
1Institute of Theoretical Computer Science, Graz University of Technology, Inffeldgasse 16b, Graz, Austria
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  • ORCID record for Anand Subramoney
Ceca Kraišniković
1Institute of Theoretical Computer Science, Graz University of Technology, Inffeldgasse 16b, Graz, Austria
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Guillaume Bellec
1Institute of Theoretical Computer Science, Graz University of Technology, Inffeldgasse 16b, Graz, Austria
2Laboratory of Computational Neuroscience, Ecole Polytechnique Fédérale de Lausanne (EPFL), Bátiment AAB, offices 135-141, CH-1015 Lausanne
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Robert Legenstein
1Institute of Theoretical Computer Science, Graz University of Technology, Inffeldgasse 16b, Graz, Austria
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Wolfgang Maass
1Institute of Theoretical Computer Science, Graz University of Technology, Inffeldgasse 16b, Graz, Austria
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  • For correspondence: maass@igi.tugraz.at
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Abstract

For solving tasks such as recognizing a song, answering a question, or inverting a sequence of symbols, cortical microcircuits need to integrate and manipulate information that was dispersed over time during the preceding seconds. Creating biologically realistic models for the underlying computations, especially with spiking neurons and for behaviorally relevant integration time spans, is notoriously difficult. We examine the role of spike frequency adaptation in such computations and find that it has a surprisingly large impact. The inclusion of this well known property of a substantial fraction of neurons in the neocortex — especially in higher areas of the human neocortex — moves the performance of spiking neural network models for computations on network inputs that are temporally dispersed from a fairly low level up to the performance level of the human brain.

Competing Interest Statement

The authors have declared no competing interest.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted December 04, 2020.
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Spike frequency adaptation supports network computations on temporally dispersed information
Darjan Salaj, Anand Subramoney, Ceca Kraišniković, Guillaume Bellec, Robert Legenstein, Wolfgang Maass
bioRxiv 2020.05.11.081513; doi: https://doi.org/10.1101/2020.05.11.081513
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Spike frequency adaptation supports network computations on temporally dispersed information
Darjan Salaj, Anand Subramoney, Ceca Kraišniković, Guillaume Bellec, Robert Legenstein, Wolfgang Maass
bioRxiv 2020.05.11.081513; doi: https://doi.org/10.1101/2020.05.11.081513

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