Filtering of spatial bias and noise inputs by spatially structured neural networks

Naoki Masuda*, Masato Okada, Kazuyuki Aihara

*この研究の対応する著者

研究成果: Article査読

6 被引用数 (Scopus)

抄録

With spatially organized neural networks, we examined how bias and noise inputs with spatial structure result in different network states such as bumps, localized oscillations, global oscillations, and localized synchronous firing that may be relevant to, for example, orientation selectivity. To this end, we used networks of McCulloch-Pitts neurons, which allow theoretical predictions, and verified the obtained results with numerical simulations. Spatial inputs, no matter whether they are bias inputs or shared noise inputs, affect only firing activities with resonant spatial frequency. The component of noise that is independent for different neurons increases the linearity of the neural system and gives rise to less spatial mode mixing and less bistability of population activities.

本文言語English
ページ(範囲)1854-1870
ページ数17
ジャーナルNeural Computation
19
7
DOI
出版ステータスPublished - 2007 7
外部発表はい

ASJC Scopus subject areas

  • 人文科学(その他)
  • 認知神経科学

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