A Spike-Train Probability Model

Kass, Robert E.; Ventura, Valérie

doi:10.1162/08997660152469314

Cited by 208 publications

(207 citation statements)

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“…Each neuron's spiking response to input current was fit by a generalized linear model (GLM). GLMs extend stimulus-based reverse correlation or linear-nonlinear-Poisson (LNP) models (20, 24) by including terms that describe how a neuron's spike probability is modulated via its previous spikes (18,22). Here each GLM had…”

Section: Resultsmentioning

confidence: 99%

“…In addition, simplistic readouts of population spiking output may underestimate the richness of the underlying neural code (1, 10, 21). Our approach allows the influence of intrinsic diversity to be isolated from synaptic differences and captures the full potential of these diverse populations for stimulus encoding.Specifically, we developed measures of neuronal population diversity based on statistical generalized linear models (18,22) that accurately reproduce the responses of recorded individual olfactory bulb mitral cells (MCs). These cells have been shown to express significant biophysical variability from neuron to neuron (5-7).…”

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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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Section: Resultsmentioning

confidence: 99%

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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“…History effects may be of the IMI form discussed here, or they may reach back further in time, incoporating effects of many spikes (Kass and Ventura, 2001;Paninski, 2004;Kass et al, 2005;Truccolo et al, 2005;Paninski et al, 2007b), while trial-to-trial variation may be accommodated using slowly-varying, trial-dependent contributions to firing rate Czanner et al, 2008). The advantages of this sort of model become more apparent when one considers multiple simultaneously-recorded spike trains (Brown et al, 2004), where interactions among neurons may be modeled by inclusion of additional terms that define the conditional intensity (Chornoboy et al, 1988;Paninski et al, 2004a;Okatan et al, 2005;Truccolo et al, 2005;Kulkarni and Paninski, 2007;Pillow et al, 2008;Czanner et al, 2008).…”

Section: Resultsmentioning

confidence: 99%

“…(More generally, this equation is a good approximation whenever the spike history effect h(τ ) is negligible for τ larger than the typical interspike interval in the data.) Models of the general form of Equation (5) have been called "inhomogeneous Markov interval (IMI) models" by (Kass and Ventura, 2001). We may consider three special cases of Equation (5):…”

Section: Approximating the If Model Via Simpler Point-process Modelsmentioning

confidence: 99%

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Statistical Models of Spike Trains

Paninski¹,

Brown²,

Iyengar³

2009

Stochastic Methods in Neuroscience

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A Survey of Statistics in the Neurological Sciences with a Focus on Human Neuroimaging

Caffo

Zhao

Eloyan

et al. 2018

Wiley StatsRef: Statistics Reference Online

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Neuroscience is a vast subject; understanding the brain is one of the most complex, deep, and challenging tasks in all of science. In this article, we survey statistical contributions to the field of neuroscience, though focus heavily on human brain imaging. Statistics has made fundamental contributions in the processing and analysis of neuroscience and neuroimaging data. Contributions range from processing the measurements of new technologies to analyzing large groups of subjects and inference on the impact of behavior or disease. Developments in statistical algorithms and signal processing help in the pipeline that takes raw images and converts them to those used for diagnosis or research. Statistics has provided key protections from type I errors for the high‐dimensional spatially correlated and complex data arising in this domain. In addition, novel modeling and testing approaches have allowed researchers to perform inference for this challenging data. We end the article with recommendations for statisticians and other quantitative scientists to get involved in this exciting and rapidly evolving field.

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A Spike-Train Probability Model

Cited by 208 publications

References 10 publications

Intermediate intrinsic diversity enhances neural population coding

Intermediate intrinsic diversity enhances neural population coding

Statistical Models of Spike Trains

A Survey of Statistics in the Neurological Sciences with a Focus on Human Neuroimaging

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