Neuronal variability: noise or part of the signal?

  title={Neuronal variability: noise or part of the signal?},
  author={Richard B. Stein and E. Roderich Gossen and Kelvin E. Jones},
  journal={Nature Reviews Neuroscience},
Sensory, motor and cortical neurons fire impulses or spikes at a regular, but slowly declining, rate in response to a constant current stimulus. Yet, the intervals between spikes often vary randomly during behaviour. Is this variation an unavoidable effect of generating spikes by sensory or synaptic processes ('neural noise') or is it an important part of the 'signal' that is transmitted to other neurons? Here, we mainly discuss this question in relation to sensory and motor processes, as the… 

Voltage fluctuations in neurons: signal or noise?

Evidence that noise and variability in some cases go hand in hand with behavioral variability and increase behavioral choice, richness, and adaptability opens new avenues for future studies.

Noise induced tuned decrease in firing in neural models: inverse stochastic resonance

Notably, in classical neuronal models and experimentally it is found that in some cases there is a noise level at which the quenching is at a maximum and the cells response is atA minimum, which requires hysteresis between a stable rest point and a limit cycle.

The silencing of neuronal activity by noise and the phenomenon of inverse stochastic resonance

Suppression of rhythmic behavior by noise and inverse stochastic resonance are predicted to occur not only in neuronal systems but more generally in diverse nonlinear dynamical systems where a stable limit cycle is attainable from a stable rest state.

Variation, Signal, and Noise in Cerebellar Sensory–Motor Processing for Smooth-Pursuit Eye Movements

Application to the data of a theoretical and computational analysis suggests that variation in pursuit initiation arises mostly from variation in visual motion signals that provide common inputs to the PC population.

Inferring Cortical Variability from Local Field Potentials

A direct link between the trial-to-trial variability of cortical neuron responses and network activity that is reflected in local field potentials is demonstrated, which provides a foundation to understand the role of sensory cortex in combining sensory and cognitive variables.

Sensitivity of Noisy Neurons to Coincident Inputs

It is concluded that not only are noisy neurons well equipped to detect coincidences, but they are so sensitive to fine correlations that a rate-based description of neural computation is unlikely to be accurate in general.

Neuronal Transmission of Subthreshold Periodic Stimuli Via Symbolic Spike Patterns

The results suggest that sensory neurons can exploit the presence of neural noise to fire spike trains where the information of a subthreshold stimulus is encoded in over expressed and/or in less expressed symbolic patterns.

Variability Measures of Positive Random Variables

Two information-based measures of statistical dispersion of the interspike interval distribution, the entropy-based dispersion and Fisher information- based dispersion are proposed and compared with the frequently used concept of standard deviation.

Flexibility of neuronal codes:adaptation to stimulus statistics in a mechanosensorial neuron firing in bursts

A sensory code is described in a spike-bursting mechanoreceptor in the leech Hirudo medicinalis based on the number of spikes per burst (burst size), which codes for larger velocity values of an object indenting the skin of the animal.



Reliability of spike timing in neocortical neurons.

Data suggest a low intrinsic noise level in spike generation, which could allow cortical neurons to accurately transform synaptic input into spike sequences, supporting a possible role for spike timing in the processing of cortical information by the neocortex.

Large-scale recording of neuronal ensembles

Large-scale recordings from neuronal ensembles now offer the opportunity to test competing theoretical frameworks and require further development of the neuron–electrode interface, automated and efficient spike-sorting algorithms for effective isolation and identification of single neurons, and new mathematical insights for the analysis of network properties.

Synaptic noise and other sources of randomness in motoneuron interspike intervals.

None of the models in the literature has utilized the measured properties of either the steady-state synaptic input or of the spike-generating mechanism of the very neuron whose interval variability they wished to predict.

Spike-based strategies for rapid processing


  • R. Stein
  • Psychology
    Biophysical journal
  • 1965

Spike times make sense

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