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A Comparative Approach for Quantitative Cell Counting Studies in Widely Different Mammalian Brains

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Partitioning neuronal variability.

Robbe L T Goris¹, J Anthony Movshon¹, Eero P Simoncelli²

¹Center for Neural Science, New York University, New York, New York, USA.

Nature Neuroscience

|April 30, 2014

Summary

This summary is machine-generated.

Neural response variability stems from more than just random noise. Our model reveals that fluctuations in neural excitability, influenced by factors like attention, significantly impact sensory neuron responses.

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Area of Science:

Neuroscience
Computational Neuroscience
Systems Neuroscience

Background:

Sensory neuron responses exhibit variability across repeated measurements.
This variability is often attributed to intrinsic neuronal or circuit stochasticity.
However, non-sensory factors like arousal and attention also modulate neuronal excitability.

Purpose of the Study:

To develop a model that distinguishes sensory-driven responses from excitability fluctuations.
To quantify the contribution of stimulus-independent modulatory influences on neuronal variability.
To investigate the origin and characteristics of response variability in the visual pathway.

Main Methods:

Developed a computational model where spike rate is a product of sensory drive and a gain factor.
The gain factor summarizes stimulus-independent modulatory influences on excitability.
Applied the model to analyze response distributions of visual neurons in macaque lateral geniculate nucleus and cortical areas V1, V2, and MT.

Main Results:

The model accurately accounts for response distributions in multiple visual areas.
A significant portion of response variability originates from excitability fluctuations.
These fluctuations are correlated over time and between neurons, increasing in strength along the visual pathway.

Conclusions:

Variability in sensory neuron responses is largely driven by correlated excitability fluctuations.
These fluctuations are influenced by non-sensory factors and are not purely stochastic.
The model offers a parsimonious explanation for firing rate-dependent response variability and covariability.