Flexible timing with delay networks–The scalar property and neural scaling

Joost de Jong*, Aaron Voelker, Hedderik van Rijn, Terrence Stewart, Chris Eliasmith

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

37 Downloads (Pure)

Samenvatting

We propose a spiking recurrent neural network model of flexible human timing behavior based on the delay network. The well-known ‘scalar property’ of timing behavior arises from the model in a natural way, and critically depends on how many dimensions are used to represent the history of stimuli. The model also produces heterogeneous firing patterns that scale with the timed interval, consistent with available neural data. This suggests that the scalar property and neural scaling are tightly linked. Further extensions of the model are discussed that may capture additional behavior, such as continuative timing, temporal cognition, and learning how to time.
Originele taal-2English
TitelProceedings of the 17th International Conference on Cognitive Modelling
Pagina's77-82
Aantal pagina's6
StatusPublished - 19-jul-2019
Evenement17th Annual Meeting of the International Conference on Cognitive Modelling - Montreal, Canada
Duur: 19-jul-201922-jul-2019

Conference

Conference17th Annual Meeting of the International Conference on Cognitive Modelling
Land/RegioCanada
StadMontreal
Periode19/07/201922/07/2019

Citeer dit