Physics of psychophysics: Large dynamic range in critical square lattices of spiking neurons

Psychophysics tries to relate physical input magnitudes to psychological or neural correlates. Microscopic models to account for macroscopic psychophysical laws, in the sense of statistical physics, are an almost unexplored area. Here we examine a sensory epithelium composed of two connected square...

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Main Authors: Emilio F. Galera, Osame Kinouchi
Format: Article
Language:English
Published: American Physical Society 2020-07-01
Series:Physical Review Research
Online Access:http://doi.org/10.1103/PhysRevResearch.2.033057
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author Emilio F. Galera
Osame Kinouchi
author_facet Emilio F. Galera
Osame Kinouchi
author_sort Emilio F. Galera
collection DOAJ
description Psychophysics tries to relate physical input magnitudes to psychological or neural correlates. Microscopic models to account for macroscopic psychophysical laws, in the sense of statistical physics, are an almost unexplored area. Here we examine a sensory epithelium composed of two connected square lattices of stochastic integrate-and-fire cells. With one square lattice, we obtain a Stevens's law ρ∝h^{m} with Stevens's exponent m=0.254 and a sigmoidal saturation, where ρ is the neuronal network activity and h is the input intensity (external field). We relate Stevens's power-law exponent with the field critical exponent as m=1/δ_{h}=β/σ. We also show that this system pertains to the directed percolation (DP) universality class (or, perhaps, the compact-DP class). With two stacked layers of square lattices and a fraction of connectivity between the first and second layer, we obtain at the output layer ρ_{2}∝h^{m_{2}}, with m_{2}=0.08≈m^{2}, which corresponds to a huge dynamic range. This enhancement of the dynamic range only occurs when the layers are close to their critical point.
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spelling doaj.art-af438fcfd67844ada8e590f572e017882024-04-12T16:56:58ZengAmerican Physical SocietyPhysical Review Research2643-15642020-07-012303305710.1103/PhysRevResearch.2.033057Physics of psychophysics: Large dynamic range in critical square lattices of spiking neuronsEmilio F. GaleraOsame KinouchiPsychophysics tries to relate physical input magnitudes to psychological or neural correlates. Microscopic models to account for macroscopic psychophysical laws, in the sense of statistical physics, are an almost unexplored area. Here we examine a sensory epithelium composed of two connected square lattices of stochastic integrate-and-fire cells. With one square lattice, we obtain a Stevens's law ρ∝h^{m} with Stevens's exponent m=0.254 and a sigmoidal saturation, where ρ is the neuronal network activity and h is the input intensity (external field). We relate Stevens's power-law exponent with the field critical exponent as m=1/δ_{h}=β/σ. We also show that this system pertains to the directed percolation (DP) universality class (or, perhaps, the compact-DP class). With two stacked layers of square lattices and a fraction of connectivity between the first and second layer, we obtain at the output layer ρ_{2}∝h^{m_{2}}, with m_{2}=0.08≈m^{2}, which corresponds to a huge dynamic range. This enhancement of the dynamic range only occurs when the layers are close to their critical point.http://doi.org/10.1103/PhysRevResearch.2.033057
spellingShingle Emilio F. Galera
Osame Kinouchi
Physics of psychophysics: Large dynamic range in critical square lattices of spiking neurons
Physical Review Research
title Physics of psychophysics: Large dynamic range in critical square lattices of spiking neurons
title_full Physics of psychophysics: Large dynamic range in critical square lattices of spiking neurons
title_fullStr Physics of psychophysics: Large dynamic range in critical square lattices of spiking neurons
title_full_unstemmed Physics of psychophysics: Large dynamic range in critical square lattices of spiking neurons
title_short Physics of psychophysics: Large dynamic range in critical square lattices of spiking neurons
title_sort physics of psychophysics large dynamic range in critical square lattices of spiking neurons
url http://doi.org/10.1103/PhysRevResearch.2.033057
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