Spike frequency adaptation supports network computations on temporally dispersed information

Darjan Salaj, Anand Subramoney, Ceca Kraisnikovic, Guillaume Emmanuel Fernand Bellec, Robert Legenstein, Wolfgang Maass

Research output: Working paperPreprint


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.
Original languageEnglish
Number of pages20
Publication statusPublished - 4 Dec 2020

Publication series

NamebioRxiv - the Preprint Server for Biology
PublisherCold Spring Harbor Laboratory Press


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