Presentation 2007-10-18
Fisher information in stochastic neural network models
Masafumi OIZUMI, Keiji MIURA, Masato OKADA,
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Abstract(in English) Fisher information quantitatively measures encoding accuracy of neural population coding. If neurons fired independently, it is easy to calculate the Fisher information. However, taking into account of neural correlations, which are observed in real nervous systems, calculating the Fisher information becomes hard problems. This is because calculating neural correlations, which is second-order statistics, is difficult. We developed a theory in which auto-correlations and cross-correlations can be analytically calculated in a stochastic spiking neuron model called spike response model. Using our theory, the Fisher information can be analytically calculated under the assumption that fluctuations of mean firing rates obey gaussian distribution. We apply our theoretical framework to a model of orientation selectivity in primary visual cortex and investigate how recurrent excitation and lateral inhibition affect information encoding of neural activities.
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Keyword(in English) spike response model / auto-correlation / cross-correlation / Fisher information / primary visual cortex / orientation selectivity
Paper # NC2007-36
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Committee NC
Conference Date 2007/10/11(1days)
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Registration To Neurocomputing (NC)
Language JPN
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Title (in English) Fisher information in stochastic neural network models
Sub Title (in English)
Keyword(1) spike response model
Keyword(2) auto-correlation
Keyword(3) cross-correlation
Keyword(4) Fisher information
Keyword(5) primary visual cortex
Keyword(6) orientation selectivity
1st Author's Name Masafumi OIZUMI
1st Author's Affiliation Graduate School of Frontier Sciences, The University of Tokyo()
2nd Author's Name Keiji MIURA
2nd Author's Affiliation Graduate School of Frontier Sciences, The University of Tokyo:RIKEN Brain Science Institute
3rd Author's Name Masato OKADA
3rd Author's Affiliation Graduate School of Frontier Sciences, The University of Tokyo:RIKEN Brain Science Institute
Date 2007-10-18
Paper # NC2007-36
Volume (vol) vol.107
Number (no) 263
Page pp.pp.-
#Pages 6
Date of Issue