Show Reference: "The competing benefits of noise and heterogeneity in neural coding."

The competing benefits of noise and heterogeneity in neural coding. Neural Computation, Vol. 26, No. 8. (August 2014), pp. 1600-1623 by Eric Hunsberger, Matthew Scott, Chris Eliasmith
    abstract = {Noise and heterogeneity are both known to benefit neural coding. Stochastic resonance describes how noise, in the form of random fluctuations in a neuron's membrane voltage, can improve neural representations of an input signal. Neuronal heterogeneity refers to variation in any one of a number of neuron parameters and is also known to increase the information content of a population. We explore the interaction between noise and heterogeneity and find that their benefits to neural coding are not independent. Specifically, a neuronal population better represents an input signal when either noise or heterogeneity is added, but adding both does not always improve representation further. To explain this phenomenon, we propose that noise and heterogeneity operate using two shared mechanisms: (1) temporally desynchronizing the firing of neurons in the population and (2) linearizing the response of a population to a stimulus. We first characterize the effects of noise and heterogeneity on the information content of populations of either leaky integrate-and-fire or {FitzHugh}-Nagumo neurons. We then examine how the mechanisms of desynchronization and linearization produce these effects, and find that they work to distribute information equally across all neurons in the population in terms of both signal timing (desynchronization) and signal amplitude (linearization). Without noise or heterogeneity, all neurons encode the same aspects of the input signal; adding noise or heterogeneity allows neurons to encode complementary aspects of the input signal, thereby increasing information content. The simulations detailed in this letter highlight the importance of heterogeneity and noise in population coding, demonstrate their complex interactions in terms of the information content of neurons, and explain these effects in terms of underlying mechanisms.},
    author = {Hunsberger, Eric and Scott, Matthew and Eliasmith, Chris},
    citeulike-article-id = {13251978},
    citeulike-linkout-0 = {},
    citeulike-linkout-1 = {},
    issn = {1530-888X},
    journal = {Neural Computation},
    keywords = {ann, math, noise, representations},
    month = aug,
    number = {8},
    pages = {1600--1623},
    pmid = {24877735},
    posted-at = {2014-07-07 08:49:43},
    priority = {2},
    title = {The competing benefits of noise and heterogeneity in neural coding.},
    url = {},
    volume = {26},
    year = {2014}

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Noisy neuronal responses can improve information transmission in populations (especially when neurons are threshold-like).

Not all neurons are created equal (and that's a good thing): Populations of neurons with diverse neural parameters can represent information better than populations of identical neurons. (Think different threshold values, regions of linearity in transfer functions)

Adding both noise and hetereogeneity to a population-coding network does not always improve coding.

Hunsberger et al. suggest that neural heterogeneity and response stochasticity both decorrelate and linearize population responses and thus improve transmission of information.