TY - JOUR
T1 - Filtering of spatial bias and noise inputs by spatially structured neural networks
AU - Masuda, Naoki
AU - Okada, Masato
AU - Aihara, Kazuyuki
PY - 2007/7
Y1 - 2007/7
N2 - 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.
AB - 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.
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U2 - 10.1162/neco.2007.19.7.1854
DO - 10.1162/neco.2007.19.7.1854
M3 - Article
C2 - 17521281
AN - SCOPUS:34447271521
VL - 19
SP - 1854
EP - 1870
JO - Neural Computation
JF - Neural Computation
SN - 0899-7667
IS - 7
ER -