Neuronal variability: noise or part of the signal?

By Richard B. Stein, E. Gossen, Kelvin E. Jones. Nature Reviews Neuroscience, 2005

681 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/31667e8fc9c8fd9b87902197c46b461801310aa4

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Introduction to spiking neural networks: Information processing, learning and applications. (2011, background): “…results, however, suggest that, at least in some neural systems, efficient processing of information is more likely to be based on the precise timing of action potentials rather than on their firing rate (Lestienne 2001, Bohte 2004, Stein et al. 2005, Faisal et al. 2008, Tiesinga et al. 2008).”
Increased Brain Signal Variability Accompanies Lower Behavioral Variability in Development (2008, background): “From cell channels to synapse to neural ensembles, noise seems to be an important parameter that shapes responsivity [10,11].”
Synaptic and network mechanisms of sparse and reliable visual cortical activity during nonclassical receptive field stimulation. (2010, background): “However, it remains unclear how sparse single-neuron responses can be reliably transmitted to downstream neurons in the face of typical trial-to-trial response variability (Shadlen and Newsome, 1998; Stein et al., 2005).”
Muscle fatigue: what, why and how it influences muscle function (2008, background): “The greater increase in the coefficient of variation during the position task suggests a more rapid rise in synaptic noise during this task (Stein et al. 2005).”
The benefits of noise in neural systems: bridging theory and experiment (2011, background): “It is often very difficult to distinguish signal from nois”
Balanced Networks of Spiking Neurons with Spatially Dependent Recurrent Connections (2013, background): “In the continuum limit we derive that stable, balanced firing rate solutions require that the spatial spread of external inputs be broader than that of recurrent excitation, which in turn must be broader than or equal to that of recurrent inhibition.”
What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology (2009, background): “Example discussions of the many possible ways in which randomness may be useful in biology and neuroscience appear in [6–8].”
Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting (2020, background): “A beautiful coincidence in neuroscience is that neural variability in the rate of response to a steady stimulus also penalizes the information carried by nerve impulses (spikes) (Stein et al., 2005; Houghton, 2019).”