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Discriminative learning of receptive fields from responses to non-Gaussian stimulus ensembles.

Meyer AF, Diepenbrock JP, Happel MF, Ohl FW, Anemüller J - PLoS ONE (2014)

Bottom Line: Results demonstrate that the proposed approach successfully identifies correct underlying STRFs, even in cases where second-order methods based on the spike-triggered average (STA) do not.Applied to small data samples, the method is shown to converge on smaller amounts of experimental recordings and with lower estimation variance than the generalized linear model and recent information theoretic methods.Thus, CbRF estimation may prove useful for investigation of neuronal processes in response to natural stimuli and in settings where rapid adaptation is induced by experimental design.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Physics and Acoustics and Cluster of Excellence ''Hearing4all'', University of Oldenburg, Oldenburg, Germany.

ABSTRACT
Analysis of sensory neurons' processing characteristics requires simultaneous measurement of presented stimuli and concurrent spike responses. The functional transformation from high-dimensional stimulus space to the binary space of spike and non-spike responses is commonly described with linear-nonlinear models, whose linear filter component describes the neuron's receptive field. From a machine learning perspective, this corresponds to the binary classification problem of discriminating spike-eliciting from non-spike-eliciting stimulus examples. The classification-based receptive field (CbRF) estimation method proposed here adapts a linear large-margin classifier to optimally predict experimental stimulus-response data and subsequently interprets learned classifier weights as the neuron's receptive field filter. Computational learning theory provides a theoretical framework for learning from data and guarantees optimality in the sense that the risk of erroneously assigning a spike-eliciting stimulus example to the non-spike class (and vice versa) is minimized. Efficacy of the CbRF method is validated with simulations and for auditory spectro-temporal receptive field (STRF) estimation from experimental recordings in the auditory midbrain of Mongolian gerbils. Acoustic stimulation is performed with frequency-modulated tone complexes that mimic properties of natural stimuli, specifically non-Gaussian amplitude distribution and higher-order correlations. Results demonstrate that the proposed approach successfully identifies correct underlying STRFs, even in cases where second-order methods based on the spike-triggered average (STA) do not. Applied to small data samples, the method is shown to converge on smaller amounts of experimental recordings and with lower estimation variance than the generalized linear model and recent information theoretic methods. Thus, CbRF estimation may prove useful for investigation of neuronal processes in response to natural stimuli and in settings where rapid adaptation is induced by experimental design.

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Effect of incorporating class prior probabilities into the objective function.LNP simulation with a -dimensional uncorrelated stimulus, asymmetric stimulus distribution and sigmoid-shaped nonlinearity. The dashed line indicates direction of the ground truth linear filter. Light and dark gray dots represent presence or absence of spikes, respectively, with . Filter estimates represented by the normal vectors of the decision hyperplanes have been estimated using the proposed classification-based method with true class prior weighting (blue arrow) and uniform weighting (black arrow) of misclassification errors. The green arrow illustrates the spike-triggered average (STA) solution. The filter direction estimate obtained with uniform weighting systematically deviates from the true direction. For visualization purposes, normal vectors of decision hyperplanes have been rescaled equal length.
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pone-0093062-g002: Effect of incorporating class prior probabilities into the objective function.LNP simulation with a -dimensional uncorrelated stimulus, asymmetric stimulus distribution and sigmoid-shaped nonlinearity. The dashed line indicates direction of the ground truth linear filter. Light and dark gray dots represent presence or absence of spikes, respectively, with . Filter estimates represented by the normal vectors of the decision hyperplanes have been estimated using the proposed classification-based method with true class prior weighting (blue arrow) and uniform weighting (black arrow) of misclassification errors. The green arrow illustrates the spike-triggered average (STA) solution. The filter direction estimate obtained with uniform weighting systematically deviates from the true direction. For visualization purposes, normal vectors of decision hyperplanes have been rescaled equal length.

Mentions: Figure 2 shows the difference between solutions with and without weighting of misclassification errors for a two-dimensional example with . Without weighting, the solution systematically deviates from the true solution, whereas the weighted solution recovers the ground truth RF. For comparison we also tested the linear spike-triggered average (STA) estimator (see Methods S1). The STA solution is highly biased due to violation of the symmetry assumption.


Discriminative learning of receptive fields from responses to non-Gaussian stimulus ensembles.

Meyer AF, Diepenbrock JP, Happel MF, Ohl FW, Anemüller J - PLoS ONE (2014)

Effect of incorporating class prior probabilities into the objective function.LNP simulation with a -dimensional uncorrelated stimulus, asymmetric stimulus distribution and sigmoid-shaped nonlinearity. The dashed line indicates direction of the ground truth linear filter. Light and dark gray dots represent presence or absence of spikes, respectively, with . Filter estimates represented by the normal vectors of the decision hyperplanes have been estimated using the proposed classification-based method with true class prior weighting (blue arrow) and uniform weighting (black arrow) of misclassification errors. The green arrow illustrates the spike-triggered average (STA) solution. The filter direction estimate obtained with uniform weighting systematically deviates from the true direction. For visualization purposes, normal vectors of decision hyperplanes have been rescaled equal length.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3974709&req=5

pone-0093062-g002: Effect of incorporating class prior probabilities into the objective function.LNP simulation with a -dimensional uncorrelated stimulus, asymmetric stimulus distribution and sigmoid-shaped nonlinearity. The dashed line indicates direction of the ground truth linear filter. Light and dark gray dots represent presence or absence of spikes, respectively, with . Filter estimates represented by the normal vectors of the decision hyperplanes have been estimated using the proposed classification-based method with true class prior weighting (blue arrow) and uniform weighting (black arrow) of misclassification errors. The green arrow illustrates the spike-triggered average (STA) solution. The filter direction estimate obtained with uniform weighting systematically deviates from the true direction. For visualization purposes, normal vectors of decision hyperplanes have been rescaled equal length.
Mentions: Figure 2 shows the difference between solutions with and without weighting of misclassification errors for a two-dimensional example with . Without weighting, the solution systematically deviates from the true solution, whereas the weighted solution recovers the ground truth RF. For comparison we also tested the linear spike-triggered average (STA) estimator (see Methods S1). The STA solution is highly biased due to violation of the symmetry assumption.

Bottom Line: Results demonstrate that the proposed approach successfully identifies correct underlying STRFs, even in cases where second-order methods based on the spike-triggered average (STA) do not.Applied to small data samples, the method is shown to converge on smaller amounts of experimental recordings and with lower estimation variance than the generalized linear model and recent information theoretic methods.Thus, CbRF estimation may prove useful for investigation of neuronal processes in response to natural stimuli and in settings where rapid adaptation is induced by experimental design.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Physics and Acoustics and Cluster of Excellence ''Hearing4all'', University of Oldenburg, Oldenburg, Germany.

ABSTRACT
Analysis of sensory neurons' processing characteristics requires simultaneous measurement of presented stimuli and concurrent spike responses. The functional transformation from high-dimensional stimulus space to the binary space of spike and non-spike responses is commonly described with linear-nonlinear models, whose linear filter component describes the neuron's receptive field. From a machine learning perspective, this corresponds to the binary classification problem of discriminating spike-eliciting from non-spike-eliciting stimulus examples. The classification-based receptive field (CbRF) estimation method proposed here adapts a linear large-margin classifier to optimally predict experimental stimulus-response data and subsequently interprets learned classifier weights as the neuron's receptive field filter. Computational learning theory provides a theoretical framework for learning from data and guarantees optimality in the sense that the risk of erroneously assigning a spike-eliciting stimulus example to the non-spike class (and vice versa) is minimized. Efficacy of the CbRF method is validated with simulations and for auditory spectro-temporal receptive field (STRF) estimation from experimental recordings in the auditory midbrain of Mongolian gerbils. Acoustic stimulation is performed with frequency-modulated tone complexes that mimic properties of natural stimuli, specifically non-Gaussian amplitude distribution and higher-order correlations. Results demonstrate that the proposed approach successfully identifies correct underlying STRFs, even in cases where second-order methods based on the spike-triggered average (STA) do not. Applied to small data samples, the method is shown to converge on smaller amounts of experimental recordings and with lower estimation variance than the generalized linear model and recent information theoretic methods. Thus, CbRF estimation may prove useful for investigation of neuronal processes in response to natural stimuli and in settings where rapid adaptation is induced by experimental design.

Show MeSH
Related in: MedlinePlus