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Convolutional Spike-triggered Covariance Analysis for Neural Subunit Models

by Anqi Wu, Il Memming, Park Jonathan, W. Pillow
"... Subunit models provide a powerful yet parsimonious description of neural re-sponses to complex stimuli. They are defined by a cascade of two linear-nonlinear (LN) stages, with the first stage defined by a linear convolution with one or more filters and common point nonlinearity, and the second by po ..."
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-likelihood and the ubiquity of local optima. Here we address this problem by providing a theoretical connection between spike-triggered covariance analy-sis and nonlinear subunit models. Specifically, we show that a “convolutional” decomposition of a spike-triggered average (STA) and covariance (STC) matrix provides

Bayesian Spike-Triggered Covariance Analysis

by Il Memming Park, Jonathan W. Pillow
"... Neurons typically respond to a restricted number of stimulus features within the high-dimensional space of natural stimuli. Here we describe an explicit modelbased interpretation of traditional estimators for a neuron’s multi-dimensional feature space, which allows for several important generalizati ..."
Abstract - Cited by 11 (2 self) - Add to MetaCart
generalizations and extensions. First, we show that traditional estimators based on the spike-triggered average (STA) and spike-triggered covariance (STC) can be formalized in terms of the “expected log-likelihood ” of a Linear-Nonlinear-Poisson (LNP) model with Gaussian stimuli. This model-based formulation

Convergence properties of some spike-triggered analysis techniques

by Liam Paninski - Network: Computation in Neural Systems , 2003
"... We analyze the convergence properties of three spike-triggered data analysis techniques. All of our results are obtained in the setting of a (possibly multidimensional) linear-nonlinear (LN) cascade model for stimulus-driven neural activity. We start by giving exact rate of convergence results for t ..."
Abstract - Cited by 34 (15 self) - Add to MetaCart
We analyze the convergence properties of three spike-triggered data analysis techniques. All of our results are obtained in the setting of a (possibly multidimensional) linear-nonlinear (LN) cascade model for stimulus-driven neural activity. We start by giving exact rate of convergence results

Dimensionality reduction in neural models: an information-theoretic generalization of spike-triggered average and covariance analysis.

by Jonathan W Pillow , Eero P Simoncelli - J. Vis. , 2006
"... We describe an information-theoretic framework for fitting neural spike responses with a Linear-Nonlinear-Poisson cascade model. This framework unifies the spike-triggered average (STA) and spike-triggered covariance (STC) approaches to neural characterization and recovers a set of linear filters t ..."
Abstract - Cited by 35 (11 self) - Add to MetaCart
We describe an information-theoretic framework for fitting neural spike responses with a Linear-Nonlinear-Poisson cascade model. This framework unifies the spike-triggered average (STA) and spike-triggered covariance (STC) approaches to neural characterization and recovers a set of linear filters

Systems/Circuits A Convolutional Subunit Model for Neuronal Responses in

by Macaque V, Brett Vintch, J. Anthonymovshon, Eero P. Simoncelli
"... The response properties of neurons in the early stages of the visual system can be described using the rectified responses of a set of self-similar, spatially shifted linear filters. In macaque primary visual cortex (V1), simple cell responses can be captured with a single filter, whereas complex ce ..."
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cells combine a set of filters, creating position invariance. These filters cannot be estimated using standard methods, such as spike-triggered averaging. Subspace methods like spike-triggered covariance can recover multiple filters but require substantial amounts of data, and recover an orthogonal

W (2003) Maximally informative dimensions: analyzing neural responses to natural signals. In: Advances in neural information processing systems

by Tatyana Sharpee, Nicole C. Rust, William Bialek - Ed 15 (Becker S, Thrun S, Obermayer K
"... We propose a method that allows for a rigorous statistical analysis of neural responses to natural stimuli, which are non-Gaussian and exhibit strong correlations. We have in mind a model in which neurons are se-lective for a small number of stimulus dimensions out of the high di-mensional stimulus ..."
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that of the overall stimulus space, it may become experimentally feasible to map out the neuron’s input-output function even under fully natural stimulus conditions. This contrasts with methods based on correlations functions (reverse correlation, spike-triggered covariance,...) which all require simplified stimulus

Bayesian inference for spiking neuron models with a sparsity prior

by Sebastian Gerwinn, Jakob H Macke, Matthias Seeger, Matthias Bethge - In Platt et , 2008
"... Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the expectation propagation algorithm, we are able to approximate the full posterior distribution over all weights. In additi ..."
Abstract - Cited by 21 (8 self) - Add to MetaCart
between neurons. Furthermore we used the sparsity of the Laplace prior to select those filters from a spike-triggered covariance analysis that are most informative about the neural response. 1

Efficient, adaptive estimation of two-dimensional firing rate surfaces via gaussian process methods. Network: Comput. Neural Syst

by Kamiar Rahnama Rad, Liam Paninski , 2010
"... Estimating two-dimensional firing rate maps is a common problem, arising in a number of contexts: the estimation of place fields in hippocampus, the analysis of temporally nonstationary tuning curves in sensory and motor areas, the estimation of firing rates following spike-triggered covariance anal ..."
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Estimating two-dimensional firing rate maps is a common problem, arising in a number of contexts: the estimation of place fields in hippocampus, the analysis of temporally nonstationary tuning curves in sensory and motor areas, the estimation of firing rates following spike-triggered covariance

unknown title

by Liam Paninski
"... We analyse the convergence properties of three spike-triggered data analysis techniques. Our results are obtained in the setting of a probabilistic linear-- nonlinear (LN) cascade neural encoding model; this model has recently become popular in the study of the neural coding of natural signals. We s ..."
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We analyse the convergence properties of three spike-triggered data analysis techniques. Our results are obtained in the setting of a probabilistic linear-- nonlinear (LN) cascade neural encoding model; this model has recently become popular in the study of the neural coding of natural signals. We

Rare Books Library and

by Blaise Agüera Y Arcas, Adrienne L. Fairhall , 2003
"... The computation performed by a neuron can be formulated as a combination of dimensional reduction in stimulus space and the nonlinearity inherent in a spiking output. White noise stimulus and reverse correlation (the spike-triggered average and spike-triggered covariance) are often used in experimen ..."
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The computation performed by a neuron can be formulated as a combination of dimensional reduction in stimulus space and the nonlinearity inherent in a spiking output. White noise stimulus and reverse correlation (the spike-triggered average and spike-triggered covariance) are often used
Results 1 - 10 of 15