Modeling lateral geniculate nucleus response with contrast gain control Part 1: formulation

Cope, Davis K.; Blakeslee, Barbara; McCourt, Mark E.

doi:10.1364/josaa.30.002401

Cited by 5 publications

(11 citation statements)

References 39 publications

(50 reference statements)

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“…The balance is nonnegative, that is, β CS ≤ 1, in agreement with observed LGN properties, i.e., the majority of LGN cells are found to possess stronger center versus surround responses, so that normally β CS < 1 [1]. Fig.…”

Section: A Model For On-center Lgn Cell Responsesupporting

confidence: 68%

“…For this class of models, the intrinsic noise parameter ν G sets a scale for the stimulus magnitude ν MAG , and only the ratio ν MAG / ν G (the SNR) occurs in the response of the model. Furthermore, as shown in [1], the models have the following general properties (all observed experimentally):…”

Section: Discussionmentioning

confidence: 81%

“…Cope et al [1] presented a class of nonlinear models for ON-center LGN behavior. The general formulation consisted of a linear field response (determined by a receptive field function), a gain control response (determined by a gain control function and an intrinsic noise parameter ν G ), and a total response (defined by the quotient of the linear field and gain control responses).…”

Section: Discussionmentioning

confidence: 99%

“…The preceding section formulated a specific case of a general class of models introduced in Cope et al [1] which derived the following results for the general class: (1) the intrinsic noise parameter ν G defines a scale for SNR; (2) for stimuli with small magnitude, the response behaves linearly; (3) responses saturate with increasing stimulus magnitude with the exception of the response to a homogeneous (zero contrast) stimulus; (4) the response to a homogeneous stimulus increases without bound (luxotonic behavior). These results, briefly discussed below, are properties of the general formulation of the model and are independent of the specific choices of receptive field function and gain control function above.…”

Section: A Model For On-center Lgn Cell Responsementioning

confidence: 99%

“…We analyze a specific formulation of a class of models introduced by Cope, Blakeslee, and McCourt [1] describing the response of a single LGN ON-center cell to general stimulus patterns. Our stimuli are spatial patterns of afferent neural activity [spikes/ (sec ·deg 2 )], that is, neural images in the sense described by Robson [2].…”

Section: Introductionmentioning

confidence: 99%

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Modeling lateral geniculate nucleus response with contrast gain control Part 2: analysis

2014

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Cope, Blakeslee and McCourt (2013) proposed a class of models for LGN ON-cell behavior consisting of a linear response with divisive normalization by local stimulus contrast. Here we analyze a specific model with the linear response defined by a difference-of-Gaussians filter and a circular Gaussian for the gain pool weighting function. For sinusoidal grating stimuli, the parameter region for band-pass behavior of the linear response is determined, the gain control response is shown to act as a switch (changing from “off” to “on” with increasing spatial frequency), and it is shown that large gain pools stabilize the optimal spatial frequency of the total nonlinear response at a fixed value independent of contrast and stimulus magnitude. Under- and super-saturation as well as contrast saturation occur as typical effects of stimulus magnitude. For circular spot stimuli, it is shown that large gain pools stabilize the spot size that yields the maximum response.

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Section: A Model For On-center Lgn Cell Responsesupporting

confidence: 68%

Section: Discussionmentioning

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Section: A Model For On-center Lgn Cell Responsementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling lateral geniculate nucleus response with contrast gain control Part 2: analysis

2014

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Towards building a more complex view of the lateral geniculate nucleus: Recent advances in understanding its role

Ghodrati

Khaligh‐Razavi

Lehky³

2017

Progress in Neurobiology

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The lateral geniculate nucleus (LGN) has often been treated in the past as a linear filter that adds little to retinal processing of visual inputs. Here we review anatomical, neurophysiological, brain imaging, and modeling studies that have in recent years built up a much more complex view of LGN. These include effects related to nonlinear dendritic processing, cortical feedback, synchrony and oscillations across LGN populations, as well as involvement of LGN in higher level cognitive processing. Although recent studies have provided valuable insights into early visual processing including the role of LGN, a unified model of LGN responses to real-world objects has not yet been developed. In the light of recent data, we suggest that the role of LGN deserves more careful consideration in developing models of high-level visual processing.

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An exponential filter model predicts lightness illusions

Zeman

Brooks

Ghebreab

2015

Front. Hum. Neurosci.

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Lightness, or perceived reflectance of a surface, is influenced by surrounding context. This is demonstrated by the Simultaneous Contrast Illusion (SCI), where a gray patch is perceived lighter against a black background and vice versa. Conversely, assimilation is where the lightness of the target patch moves toward that of the bounding areas and can be demonstrated in White's effect. Blakeslee and McCourt (1999) introduced an oriented difference-of-Gaussian (ODOG) model that is able to account for both contrast and assimilation in a number of lightness illusions and that has been subsequently improved using localized normalization techniques. We introduce a model inspired by image statistics that is based on a family of exponential filters, with kernels spanning across multiple sizes and shapes. We include an optional second stage of normalization based on contrast gain control. Our model was tested on a well-known set of lightness illusions that have previously been used to evaluate ODOG and its variants, and model lightness values were compared with typical human data. We investigate whether predictive success depends on filters of a particular size or shape and whether pooling information across filters can improve performance. The best single filter correctly predicted the direction of lightness effects for 21 out of 27 illusions. Combining two filters together increased the best performance to 23, with asymptotic performance at 24 for an arbitrarily large combination of filter outputs. While normalization improved prediction magnitudes, it only slightly improved overall scores in direction predictions. The prediction performance of 24 out of 27 illusions equals that of the best performing ODOG variant, with greater parsimony. Our model shows that V1-style orientation-selectivity is not necessary to account for lightness illusions and that a low-level model based on image statistics is able to account for a wide range of both contrast and assimilation effects.

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Modeling lateral geniculate nucleus response with contrast gain control Part 1: formulation

Cited by 5 publications

References 39 publications

Modeling lateral geniculate nucleus response with contrast gain control Part 2: analysis

Modeling lateral geniculate nucleus response with contrast gain control Part 2: analysis

Towards building a more complex view of the lateral geniculate nucleus: Recent advances in understanding its role

An exponential filter model predicts lightness illusions

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