Contrast normalization contributes to a biologically-plausible model of receptive-field development in primary visual cortex (V1)

Willmore, Ben D. B.; Bulstrode, Harry; Tolhurst, David J.

doi:10.1016/j.visres.2011.12.008

Cited by 11 publications

(14 citation statements)

References 63 publications

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“…Physiological evidence [5,38] affirms that neuronal populations in the primary visual cortex (V1) of mammals exhibit contrast normalization. Neurons that respond strongly to simple visual stimuli, such as sinusoidal gratings, respond less well to the same stimuli when they are presented as part of a more complex stimulus which also excites other, neighbouring neurons.…”

Section: Bio-inspired Treatmentmentioning

confidence: 97%

Bio-Inspired Architecture for Clustering into Natural and Non-Natural Facial Expressions

Sánchez¹,

Hernández²,

Orellana³

2012

Visual Cortex - Current Status and Perspectives

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Section: Bio-inspired Treatmentmentioning

confidence: 97%

Bio-Inspired Architecture for Clustering into Natural and Non-Natural Facial Expressions

Sánchez¹,

Hernández²,

Orellana³

2012

Visual Cortex - Current Status and Perspectives

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“…However, for complex cells, multiple linear spatial filters are 29 typically required, and the output of each filter is passed through a nonlinear function These types of model only describe the responses of neurons to visual stimuli and 42 cannot explain the biophysical mechanisms of complex cell responses. Several models 43 have also investigated different network architectures; these can be divided into three 44 categories: hierarchical, parallel, and recurrent (see [13] for a review). The notion of the 45 hierarchical model was proposed by Hubel and Wiesel [2], where a complex cell pools spatial phase preferences so that it is orientation selective but spatially phase invariant.…”

mentioning

confidence: 99%

“…Law and Cooper applied the BCM 235 plasticity rule to a network using natural images as input stimuli, and showed that this 236 learning rule can learn RFs like those of simple cells [43]. However, since the BCM 237 plasticity rule is the same for every neuron in the network, the learned features of the 238 network tend to be similar [44]. By incorporating response normalization, where the 239 response of a cell is normalized by the responses of other cells in the network [45], a 240 "soft" form of competition is introduced to the network.…”

mentioning

confidence: 99%

“…By incorporating response normalization, where the 239 response of a cell is normalized by the responses of other cells in the network [45], a 240 "soft" form of competition is introduced to the network. Furthermore, strong 241 experimental evidence has been found that normalization operates throughout the visual 242 system, from the retina to the visual cortex (see [46] for a review images [44]. However, the BCM and NBCM plasticity rules ignore some biological 246 constraints such as Dale's Law [47] and non-negative neuronal responses.…”

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confidence: 99%

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Learning receptive field properties of complex cells in V1

Lian

Almasi

Grayden

et al. 2020

Preprint

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There are two distinct classes of cells in the primary visual cortex (V1): simple cells and complex cells. One defining feature of complex cells is their spatial phase invariance; they respond strongly to oriented grating stimuli with a preferred orientation but with a wide range of spatial phases. A classical model of complete spatial phase invariance in complex cells is the energy model, in which the responses are the sum of the squared outputs of two linear spatially phase-shifted filters. However, recent experimental studies have shown that complex cells have a diverse range of spatial phase invariance and only a subset can be characterized by the energy model. While several models have been proposed to explain how complex cells could learn to be selective to orientation but invariant to spatial phase, most existing models overlook many biologically important details. We propose a biologically plausible model for complex cells that learns to pool inputs from simple cells based on the presentation of natural scene stimuli. The model is a three-layer network with rate-based neurons that describes the activities of LGN cells (layer 1), V1 simple cells (layer 2), and V1 complex cells (layer 3). The first two layers implement a recently proposed simple cell model that is biologically plausible and accounts for many experimental phenomena. The neural dynamics of the complex cells is modeled as the integration of simple cells inputs along with response normalization. Connections between LGN and simple cells are learned using Hebbian and anti-Hebbian plasticity. Connections between simple and complex cells are learned using a modified version of the Bienenstock, Cooper, and Munro (BCM) rule. Our results demonstrate that the learning rule can describe a diversity of complex cells, similar to those observed experimentally. Author summaryMany cortical functions originate from the learning ability of the brain. How the properties of cortical cells are learned is vital for understanding how the brain works. There are many models that explain how V1 simple cells can be learned. However, how V1 complex cells are learned still remains unclear. In this paper, we propose a model of May 12, 2020 1/26 learning in complex cells based on the Bienenstock, Cooper, and Munro (BCM) rule. We demonstrate that properties of receptive fields of complex cells can be learned using this biologically plausible learning rule. Quantitative comparisons between the model and experimental data are performed. Results show that model complex cells can account for the diversity of complex cells found in experimental studies. In summary, this study provides a plausible explanation for how complex cells can be learned using biologically plausible plasticity mechanisms. Our findings help us to better understand biological vision processing and provide us with insights into the general signal processing principles that the visual cortex employs to process visual information. 30that is usually double-sided (e.g., squaring nonlinearity). Following this, th...

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“…The natural image model also produced a population of neurons with a bias toward cardinal orientations, resulting from the model learning from natural images, which themselves have a cardinal orientation bias (Willmore et al, 2012).…”

Section: The Horizontal Effectmentioning

confidence: 99%

Psychophysical analysis of three separate anisotropic patterns in vision : putting visual reference frames in conflict to study the class 1 and 2 oblique effects and the horizontal effect.

O’Keefe¹

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Contrast normalization contributes to a biologically-plausible model of receptive-field development in primary visual cortex (V1)

Cited by 11 publications

References 63 publications

Bio-Inspired Architecture for Clustering into Natural and Non-Natural Facial Expressions

Bio-Inspired Architecture for Clustering into Natural and Non-Natural Facial Expressions

Learning receptive field properties of complex cells in V1

Psychophysical analysis of three separate anisotropic patterns in vision : putting visual reference frames in conflict to study the class 1 and 2 oblique effects and the horizontal effect.

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