Estimating linear–nonlinear models using Rényi divergences

Kouh, Minjoon; Sharpee, Tatyana O.

doi:10.1080/09548980902950891

Cited by 28 publications

(47 citation statements)

References 37 publications

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“…The LL x is in units of bits per spike, and is measured relative to the “null” model, which assumes a mean firing rate with no stimulus-tuned elements, making the LL x comparable to the model’s single-spike information (Brenner et al, 2000; Kouh and Sharpee, 2009) (see Methods). For the neuron considered above, the GN model gives a 94% improvement in the LL x , with LL x (LN) = 1.64 bits/spk and LL x (GN) = 3.18 bits/spk.…”

Section: Resultsmentioning

confidence: 99%

Temporal Precision in the Visual Pathway through the Interplay of Excitation and Stimulus-Driven Suppression

Butts¹,

Weng²,

Jin³

et al. 2011

J. Neurosci.

115

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Visual neurons can respond with extremely precise temporal patterning to visual stimuli that change on much slower time scales. Here, we investigate how the precise timing of cat thalamic spike trains – which can have timing as precise as one millisecond – is related to the stimulus, in the context of both artificial noise and natural visual stimuli. Using a nonlinear modeling framework applied to extracellular data, we demonstrate that the precise timing of thalamic spike trains can be explained by the interplay between an excitatory and a delayed suppressive input that resembles inhibition, such that neuronal responses only occur in brief windows where excitation exceeds suppression. The resulting description of thalamic computation resembles earlier models of contrast adaptation, suggesting a more general role for mechanisms of contrast adaptation in visual processing. Thus, we describe a more complex computation underlying thalamic responses to artificial and natural stimuli, which has implications for understanding how visual information is represented in the early stages of visual processing.

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Section: Resultsmentioning

confidence: 99%

Temporal Precision in the Visual Pathway through the Interplay of Excitation and Stimulus-Driven Suppression

Butts¹,

Weng²,

Jin³

et al. 2011

J. Neurosci.

115

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“…4A). Mutual information is a measure that is proportional to the log likelihood of the LN model (38). It should be noted that these percentages relate to the amount of "explainable variance."…”

Section: Resultsmentioning

confidence: 99%

Trade-off between curvature tuning and position invariance in visual area V4

Sharpee

Kouh

Reynolds

2013

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

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Humans can rapidly recognize a multitude of objects despite differences in their appearance. The neural mechanisms that endow high-level sensory neurons with both selectivity to complex stimulus features and "tolerance" or invariance to identity-preserving transformations, such as spatial translation, remain poorly understood. Previous studies have demonstrated that both tolerance and selectivity to conjunctions of features are increased at successive stages of the ventral visual stream that mediates visual recognition. Within a given area, such as visual area V4 or the inferotemporal cortex, tolerance has been found to be inversely related to the sparseness of neural responses, which in turn was positively correlated with conjunction selectivity. However, the direct relationship between tolerance and conjunction selectivity has been difficult to establish, with different studies reporting either an inverse or no significant relationship. To resolve this, we measured V4 responses to natural scenes, and using recently developed statistical techniques, we estimated both the relevant stimulus features and the range of translation invariance for each neuron. Focusing the analysis on tuning to curvature, a tractable example of conjunction selectivity, we found that neurons that were tuned to more curved contours had smaller ranges of position invariance and produced sparser responses to natural stimuli. These trade-offs provide empirical support for recent theories of how the visual system estimates 3D shapes from shading and texture flows, as well as the tiling hypothesis of the visual space for different curvature values.A lthough object recognition feels effortless, it is in fact a challenging computational problem (1). There are two important properties that any system that mediates robust object recognition must have. The first property is known as "invariance": the ability of the system to respond similarly to different views of the same object. The second property is known as "selectivity." Selectivity requires that systems' components, such as neurons within the ventral visual stream, produce different responses to potentially quite similar objects (such as different faces) even when presented from similar viewpoints. It is straightforward to make detectors that are invariant but not selective or selective but not invariant. The difficulty lies in how to make detectors that are both selective and invariant.To address this problem, both computer object recognition algorithms (2) and neural systems use a series of hierarchical stimulus representations, increasing both in complexity and the range of invariance (1, 3). For example, in each successive area of visual processing, neurons become selective for increasingly complex stimulus features (4-9) and grow more tolerant to identity-preserving transformations, such as image translation, scaling, and, to some degree, rotation and the presence of "clutter" from other objects in the scene (3,(10)(11)(12). This has led to the idea that high-level sensory neurons are...

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“…The values of LL reported are adjusted by a baseline LL, defined by the LL of a model that predicted a stimulus-independent mean firing. As a result, the LL is larger to the degree that it achieves a better explanation of the data than this null model, and it is bounded above by the single-spike information (Kouh and Sharpee 2009). It is reported in units of bits per spike.…”

Section: Methodsmentioning

confidence: 98%

Inferring the role of inhibition in auditory processing of complex natural stimuli

Schinkel-Bielefeld

David

Shamma

et al. 2012

Journal of Neurophysiology

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Intracellular studies have revealed the importance of cotuned excitatory and inhibitory inputs to neurons in auditory cortex, but typical spectrotemporal receptive field models of neuronal processing cannot account for this overlapping tuning. Here, we apply a new nonlinear modeling framework to extracellular data recorded from primary auditory cortex (A1) that enables us to explore how the interplay of excitation and inhibition contributes to the processing of complex natural sounds. The resulting description produces more accurate predictions of observed spike trains than the linear spectrotemporal model, and the properties of excitation and inhibition inferred by the model are furthermore consistent with previous intracellular observations. It can also describe several nonlinear properties of A1 that are not captured by linear models, including intensity tuning and selectivity to sound onsets and offsets. These results thus offer a broader picture of the computational role of excitation and inhibition in A1 and support the hypothesis that their interactions play an important role in the processing of natural auditory stimuli.

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Estimating linear–nonlinear models using Rényi divergences

Cited by 28 publications

References 37 publications

Temporal Precision in the Visual Pathway through the Interplay of Excitation and Stimulus-Driven Suppression

Temporal Precision in the Visual Pathway through the Interplay of Excitation and Stimulus-Driven Suppression

Trade-off between curvature tuning and position invariance in visual area V4

Inferring the role of inhibition in auditory processing of complex natural stimuli

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