English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We study associative memory neural networks of the Hodgkin-Huxley type ofspiking neurons in which multiple periodic spatio-temporal patterns of spiketiming are memorized as limit-cycle-type attractors. In encoding thespatio-temporal patterns, we assume the spike-timing-dependent synapticplasticity with the asymmetric time window. Analysis for periodic solution ofretrieval state reveals that if the area of the negative part of the timewindow is equivalent to the positive part, then crosstalk among encodedpatterns vanishes. Phase transition due to the loss of the stability ofperiodic solution is observed when we assume fast alpha-function for directinteraction among neurons. In order to evaluate the critical point of thisphase transition, we employ Floquet theory in which the stability problem ofthe infinite number of spiking neurons interacting with alpha-function isreduced into the eigenvalue problem with the finite size of matrix. Numericalintegration of the single-body dynamics yields the explicit value of thematrix, which enables us to determine the critical point of the phasetransition with a high degree of precision.Comment: Accepted for publication in Phys. Rev.

Linear stability analysis of retrieval state in associative memory neural networks of spiking neurons