Optimal Heterogeneity for Coding in Spiking Neural Networks

Mejías, Jorge F.; Longtin, André

doi:10.1103/physrevlett.108.228102

Cited by 114 publications

(129 citation statements)

References 29 publications

(36 reference statements)

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“…Too much heterogeneity results in some neurons that have firing thresholds well below or well above all signal values, causing them to either fire tonically independent of the input signal value, or to fire for no part of the input signal, respectively. This type of resonance in heterogeneity was recently observed by Mejias and Longtin (2012), both theoretically and in numerical experiments.…”

Section: Resultsmentioning

confidence: 73%

“…While this does capture the variable levels of excitability seen in in vivo neurons (Mejias and Longtin, 2012), due to variations in neuron physiology or differences in the background activity of afferent neurons, there are clearly many other types of heterogeneity in neurons that also deserve attention. We hypothesize that if heterogeneity were added to other neuron parameters, the information content of a population of FHN neurons could be optimized without noise, as with LIF neurons.…”

Section: Discussionmentioning

confidence: 99%

“…Heterogeneity is added by randomly selecting the bias voltages b i from a uniform interval of varying width. This type of heterogeneity has been used in previous studies (e.g., Brody and Hopfield, 2003), and captures the inherent variation in excitability among neurons in vivo (Mejias and Longtin, 2012) due to variations in neuron physiology or differences in the background activity of afferent neurons.…”

Section: Introductionmentioning

confidence: 99%

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The Competing Benefits of Noise and Heterogeneity in Neural Coding

2014

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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, both in terms of signal timing (desynchronization) and in terms of 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 paper 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.

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Section: Resultsmentioning

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Competing Benefits of Noise and Heterogeneity in Neural Coding

2014

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“…In contrast, eliminating redundancy (also referred to as biological degeneracy, ref. 14) may make stimulus coding less robust to noise or damage (15), thus we hypothesized that an optimal coding strategy would require balancing diversity with feature similarity or overlap.Although theorists have previously explored this issue (12,16,17), analysis of the function of the diversity of real populations of neurons requires overcoming methodological hurdles associated with studying cell-to-cell variability (3, 4). Cell-level differences (that are typically averaged away) must be captured and quantified.…”

mentioning

confidence: 99%

“…Although theorists have previously explored this issue (12,16,17), analysis of the function of the diversity of real populations of neurons requires overcoming methodological hurdles associated with studying cell-to-cell variability (3,4). Cell-level differences (that are typically averaged away) must be captured and quantified.…”

mentioning

confidence: 99%

Intermediate intrinsic diversity enhances neural population coding

Tripathy

Padmanabhan

Gerkin

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

128

125

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Cell-to-cell variability in molecular, genetic, and physiological features is increasingly recognized as a critical feature of complex biological systems, including the brain. Although such variability has potential advantages in robustness and reliability, how and why biological circuits assemble heterogeneous cells into functional groups is poorly understood. Here, we develop analytic approaches toward answering how neuron-level variation in intrinsic biophysical properties of olfactory bulb mitral cells influences population coding of fluctuating stimuli. We capture the intrinsic diversity of recorded populations of neurons through a statistical approach based on generalized linear models. These models are flexible enough to predict the diverse responses of individual neurons yet provide a common reference frame for comparing one neuron to the next. We then use Bayesian stimulus decoding to ask how effectively different populations of mitral cells, varying in their diversity, encode a common stimulus. We show that a key advantage provided by physiological levels of intrinsic diversity is more efficient and more robust encoding of stimuli by the population as a whole. However, we find that the populations that best encode stimulus features are not simply the most heterogeneous, but those that balance diversity with the benefits of neural similarity.generalized linear models | intrinsic biophysics | neural variability | stimulus coding | ion channels B iological systems including brains must function efficiently under many constraints, including constraints on the numbers of individual neurons dedicated to a given task. Brain function therefore depends on an appropriate division of labor, with specific neurons dedicated to different functions. For example, different types of retinal ganglion cells represent visual information at different timescales (1), and distinct classes of cortical interneurons play diverse roles in coordinating network activity (2). Whereas attempts to understand how distinct classes of cells encode information have proven successful (1), the importance of within-type variability remains poorly understood (3, 4) although has recently become a topic of great interest (5-8).Although neuron-to-neuron variability is often viewed as an epiphenomenon of biological imprecision (3, 4), having neurons of the same type that respond to different stimulus features may improve stimulus encoding. This variability may be leveraged to improve functions such as stimulus encoding if heterogeneous output of neurons of a single type is collectively used for population coding. Such populations of neurons could efficiently represent complex stimuli by collectively covering the relevant stimulus space (1, 9, 10). Network interactions could further increase the efficiency of information transmission by decorrelating neural responses and reducing the redundancy between their outputs (11-13). In contrast, eliminating redundancy (also referred to as biological degeneracy, ref. 14) may make stimulus coding less robu...

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Single neuron transcriptome analysis can reveal more than cell type classification

2016

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A recent single cell mRNA sequencing study by Dueck et al. compares neuronal transcriptomes to the transcriptomes of adipocytes and cardiomyocytes. Single cell ‘omic approaches such as those used by the authors are at the leading edge of molecular and biophysical measurement. Many groups are currently employing single cell sequencing approaches to understand cellular heterogeneity in cancer and during normal development. These single cell approaches also are beginning to address long‐standing questions regarding nervous system diversity. Beyond an innate interest in cataloging cell type diversity in the brain, single cell neuronal diversity has important implications for neurotypic neural circuit function and for neurological disease. Herein, we review the authors’ methods and findings, which most notably include evidence of unique expression profiles in some single neurons.

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Optimal Heterogeneity for Coding in Spiking Neural Networks

Cited by 114 publications

References 29 publications

The Competing Benefits of Noise and Heterogeneity in Neural Coding

The Competing Benefits of Noise and Heterogeneity in Neural Coding

Intermediate intrinsic diversity enhances neural population coding

Single neuron transcriptome analysis can reveal more than cell type classification

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