Weber's law implies neural discharge more regular than a Poisson process

Area of Science:

• Psychophysics
• Computational Neuroscience
• Neuroscience

Background:

• Weber's law is a fundamental psychophysical principle.
• The relationship between psychophysical laws and neuronal activity remains unclear.

Purpose of the Study:

• To quantitatively link Weber's law to neural spike train statistics.
• To investigate the conditions under which Weber's law is satisfied by neuronal populations.

Main Methods:

• Analysis of spike train statistics in relation to Weber's law.
• Modeling using a biased competition attractor network.

Main Results:

• A single neuron's efferent spike train is less variable than a Poisson process when Weber's law holds.
• Weber's law is satisfied for small neuronal populations (< 10 neurons).
• Weber's law holds for any population size if neurons share weak correlations and exhibit regular firing (less variable than Poisson).

Conclusions:

• Neuronal firing regularity, not just population size, is critical for Weber's law.
• The coefficient of variation of interspike intervals in the winning neural pool must be less than one for Weber's law validity.
• Establishes a quantitative link between psychophysical behavior (Weber's law) and neural firing properties.