Fast adaptation to rule switching using neuronal surprise.
In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signal is extracted from an increase in neural ac...
Main Authors: | Martin L L R Barry, Wulfram Gerstner |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2024-02-01
|
Series: | PLoS Computational Biology |
Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011839&type=printable |
Similar Items
-
An online Hebbian learning rule that performs independent component analysis
by: Longtin André, et al.
Published: (2008-07-01) -
Efficient modeling of neural activity using coupled renewal processes
by: Gerstner Wulfram, et al.
Published: (2011-07-01) -
Changing the responses of cortical neurons from sub- to suprathreshold using single spikes in vivo
by: Verena Pawlak, et al.
Published: (2013-01-01) -
Cortical Dynamics in Presence of Assemblies of Densely Connected Weight-Hub Neurons
by: Hesam Setareh, et al.
Published: (2017-06-01) -
Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network
by: Aditya Gilra, et al.
Published: (2017-11-01)