Spike frequency adaptation supports network computations on temporally dispersed information

eLife

Robert Legenstein¹  Darjan Salaj¹  Anand Subramoney¹  Ceca Kraisnikovic¹  Wolfgang Maass¹  Guillaume Bellec² 

[1] Institute of Theoretical Computer Science, Graz University of Technology, Graz, Austria;Institute of Theoretical Computer Science, Graz University of Technology, Graz, Austria;Laboratory of Computational Neuroscience, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland;

关键词: computational neuroscience;    simulation;    working memory;    spiking neurons;    spike-frequency adaptation;    None;   

DOI  :  10.7554/eLife.65459

来源: eLife Sciences Publications, Ltd

【 摘 要 】

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.

【 授权许可】

CC BY