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

Naoki Masuda*, Masato Okada, Kazuyuki Aihara

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1854-1870
Number of pages17
JournalNeural Computation
Issue number7
Publication statusPublished - 2007 Jul
Externally publishedYes

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience


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