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Pregled bibliografske jedinice broj: 457668

Brightness estimation in a neural network model with presynaptic inhibition


Rebić, Veseljka; Šetić, Mia; Domijan, Dražen
Brightness estimation in a neural network model with presynaptic inhibition // Fechner Day Proceedings of the 25th Annual Meeting of the International Society for Psychophysics / Elliott, M.A. ; Antonijevic, S. Berthaud ; Mulchay, P ; Martyn, C ; Bargery, B ; Schmidt, H ; (ur.).
Galway: International Society for Psychophysics, 2009. str. 359-362 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


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Naslov
Brightness estimation in a neural network model with presynaptic inhibition

Autori
Rebić, Veseljka ; Šetić, Mia ; Domijan, Dražen

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Fechner Day Proceedings of the 25th Annual Meeting of the International Society for Psychophysics / Elliott, M.A. ; Antonijevic, S. Berthaud ; Mulchay, P ; Martyn, C ; Bargery, B ; Schmidt, H ; - Galway : International Society for Psychophysics, 2009, 359-362

Skup
Fechner Day 2009

Mjesto i datum
Irska, 21-24.09.2009

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
jarkost; svjetlina; percpecija; neuralna mreža
(brightness; perception; neural network)

Sažetak
Recent psychophysical and neurophysiological investigations showed that visual system encodes luminance and use it to estimate illumination and surface brightness. We proposed a novel neural model for luminance coding based on recurrent inhibition, from the retinal ganglion cells to the axons of the bipolar cells, which modulates the amount of sensory input that ganglion cells receive (Sagdullaev et al., 2006). Extended version of the model, where the amount of presynaptic inhibition is made proportional to the maximum luminance in the visual scene, implements gain control mechanism which adjusts the raw luminance into a measure of brightness of surface. Computer simulations showed that the model scales brightness estimates consistent with the highest-luminance-as-white anchoring rule (Gilchist et al., 2004). Simulations also showed that the model is able to act as a change detector when the presynaptic inhibition temporally lags behind the excitatory input to the ganglion cell.

Izvorni jezik
Engleski

Znanstvena područja
Psihologija



POVEZANOST RADA


Projekt / tema
130-0000000-3295 - Integracija informacija kao osnova cjelovitog doživljavanja (Ivanec, Dragutin, MZOS - )

Ustanove
Filozofski fakultet, Zagreb

Profili:

Avatar Url Mia Šetić (autor)

Avatar Url Dražen Domijan (autor)

Avatar Url Veseljka Rebić (autor)

Citiraj ovu publikaciju

Rebić, Veseljka; Šetić, Mia; Domijan, Dražen
Brightness estimation in a neural network model with presynaptic inhibition // Fechner Day Proceedings of the 25th Annual Meeting of the International Society for Psychophysics / Elliott, M.A. ; Antonijevic, S. Berthaud ; Mulchay, P ; Martyn, C ; Bargery, B ; Schmidt, H ; (ur.).
Galway: International Society for Psychophysics, 2009. str. 359-362 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Rebić, V., Šetić, M. & Domijan, D. (2009) Brightness estimation in a neural network model with presynaptic inhibition. U: Elliott, M., Antonijevic, S., Mulchay, P., Martyn, C., Bargery, B., Schmidt, H. & (ur.)Fechner Day Proceedings of the 25th Annual Meeting of the International Society for Psychophysics.
@article{article, year = {2009}, pages = {359-362}, keywords = {brightness, perception, neural network}, title = {Brightness estimation in a neural network model with presynaptic inhibition}, keyword = {brightness, perception, neural network}, publisher = {International Society for Psychophysics}, publisherplace = {Irska} }




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