Pooling in a predictive model of V1 explains functional and structural diversity across species

Neurons in the primary visual cortex are selective to orientation with various degrees of selectivity to the spatial phase, from high selectivity in simple cells to low selectivity in complex cells. Various computational models have suggested a possible link between the presence of phase invariant cells and the existence of cortical orientation maps in higher mammals' V1. These models, however, do not explain the emergence of complex cells in animals that do not show orientation maps. In this study, we build a model of V1 based on a convolutional network called Sparse Deep Predictive Coding (SDPC) and show that a single computational mechanism, pooling, allows the SDPC model to account for the emergence of complex cells as well as cortical orientation maps in V1, as observed in distinct species of mammals. By using different pooling functions, our model developed complex cells in networks that exhibit orientation maps (e.g., like in carnivores and primates) or not (e.g., rodents and lagomorphs). The SDPC can therefore be viewed as a unifying framework that explains the diversity of structural and functional phenomena observed in V1. In particular, we show that orientation maps emerge naturally as the most cost-efficient structure to generate complex cells under the predictive coding principle.

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Simultaneous development and periodic clustering of simple and complex cells in visual cortex

10.1101/782151 ◽

2019 ◽

Author(s):

Gwangsu Kim ◽

Jaeson Jang ◽

Se-Bum Paik

Keyword(s):

Visual Cortex ◽

Periodic Variation ◽

Orientation Tuning ◽

List Type ◽

Simple Complex ◽

Single Class ◽

Complex Cells ◽

Orientation Maps ◽

Simple And Complex Cells ◽

Retinal Afferents

AbstractNeurons in the primary visual cortex (V1) are often classified as simple or complex cells, but it is debated whether they are discrete hierarchical classes of neurons developing sequentially, or if they represent a continuum of variation within a single class of cells developing simultaneously. Herein, we show that simple and complex cells may arise simultaneously from the universal process of retinal development. From analysis of the cortical receptive fields in cats, we show evidence that simple and complex cells originate from the periodic variation of ON-OFF segregation in the feedforward projection of retinal mosaics, by which they organize into periodic clusters in V1. Our key prediction that clusters of simple and complex cells correlate topographically with orientation maps was confirmed by data in cats. Our results suggest that simple and complex cells are not two distinct neural populations but arise from common retinal afferents, simultaneous with orientation tuning.HighlightsSimple and complex cells arise simultaneously from retinal afferents.Simple/complex cells are organized into periodic clusters across visual cortex.Simple/complex clusters are topographically correlated with orientation maps.Development of clustered cells in V1 is explained by the Paik-Ringach model.

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Generative perspective of the primary visual cortex

10.1101/2021.05.07.443208 ◽

2021 ◽

Author(s):

Zedong Bi

Keyword(s):

Visual Cortex ◽

Primary Visual Cortex ◽

Power Law ◽

Computational Models ◽

Higher Order ◽

Top Down ◽

Complex Cells ◽

Shape Morphing ◽

Neural Representations ◽

Learning Principles

According to analysis-by-synthesis theories of perception, the primary visual cortex (V1) reconstructs visual stimuli through top-down pathway, and higher-order cortex reconstructs V1 activity. Experiments also found that neural representations are generated in a top-down cascade during visual imagination. What code does V1 provide higher-order cortex to reconstruct or simulate to improve perception or imaginative creativity? What unsupervised learning principles shape V1 for reconstructing stimuli so that V1 activity eigenspectrum is power-law with close-to-1 exponent? Using computational models, we reveal that reconstructing the activities of V1 complex cells facilitate higher-order cortex to form representations smooth to shape morphing of stimuli, improving perception and creativity. Power-law eigenspectrum with close-to-1 exponent results from the constraints of sparseness and temporal slowness when V1 is reconstructing stimuli, at a sparseness strength that best whitens V1 code and makes the exponent most insensitive to slowness strength. Our results provide fresh insights into V1 computation.

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A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽

10.3390/cancers13092111 ◽

2021 ◽

Vol 13 (9) ◽

pp. 2111

Author(s):

Bo-Wei Zhao ◽

Zhu-Hong You ◽

Lun Hu ◽

Zhen-Hao Guo ◽

Lei Wang ◽

...

Keyword(s):

Drug Target ◽

Large Scale ◽

Computational Models ◽

Structural Information ◽

Characteristic Curve ◽

Representation Learning ◽

Graph Representation ◽

Convolutional Network ◽

Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

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Predictive coding for motion stimuli in human early visual cortex

Brain Structure and Function ◽

10.1007/s00429-014-0942-2 ◽

2014 ◽

Vol 221 (2) ◽

pp. 879-890 ◽

Cited By ~ 10

Author(s):

Wouter Schellekens ◽

Richard J. A. van Wezel ◽

Natalia Petridou ◽

Nick F. Ramsey ◽

Mathijs Raemaekers

Keyword(s):

Visual Cortex ◽

Predictive Coding ◽

Early Visual Cortex

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Encoding of Binocular Disparity by Complex Cells in the Cat's Visual Cortex

Journal of Neurophysiology ◽

10.1152/jn.1997.77.6.2879 ◽

1997 ◽

Vol 77 (6) ◽

pp. 2879-2909 ◽

Cited By ~ 146

Author(s):

Izumi Ohzawa ◽

Gregory C. Deangelis ◽

Ralph D. Freeman

Keyword(s):

Visual Cortex ◽

Striate Cortex ◽

Binocular Disparity ◽

Substantial Reduction ◽

Energy Model ◽

Simple Type ◽

Correspondence Problem ◽

Complex Cell ◽

Stimulus Contrast ◽

Complex Cells

Ohzawa, Izumi, Gregory C. DeAngelis, and Ralph D. Freeman. Encoding of binocular disparity by complex cells in the cat's visual cortex. J. Neurophysiol. 77: 2879–2909, 1997. To examine the roles that complex cells play in stereopsis, we have recorded extracellularly from isolated single neurons in the striate cortex of anesthetized paralyzed cats. We measured binocular responses of complex cells using a comprehensive stimulus set that encompasses all possible combinations of positions over the receptive fields for the two eyes. For a given position combination, stimulus contrast could be the same for the two eyes (2 bright or 2 dark bars) or opposite (1 bright and 1 dark). These measurements provide a binocular receptive field (RF) profile that completely characterizes complex cell responses in a joint domain of left and right stimulus positions. Complex cells typically exhibit a strong selectivity for binocular disparity, but are only broadly selective for stimulus position. For most cells, selectivity for disparity is more than twice as narrow as that for position. These characteristics are highly desirable if we assume that a disparity sensor should exhibit position invariance while encoding small changes in stimulus depth. Complex cells have nearly identical binocular RFs for bright and dark stimuli as long as the sign of stimulus contrast is the same for the two eyes. When stimulus contrast is opposite, the binocular RF also is inverted such that excitatory subregions become suppressive. We have developed a disparity energy model that accounts for the behavior of disparity-sensitive complex cells. This is a hierarchical model that incorporates specific constraints on the selection of simple cells from which a complex cell receives input. Experimental data are used to examine quantitatively predictions of the model. Responses of complex cells generally agree well with predictions of the disparity energy model. However, various types of deviations from the predictions also are found, including a highly elongated excitatory region beyond that supported by a single energy mechanism. Complex cells in the visual cortex appear to provide a next level of abstraction in encoding information for stereopsis based on the activity of a group of simple-type subunits. In addition to exhibiting narrow disparity tuning and position invariance, these cells seem to provide a partial solution to the stereo correspondence problem that arises in complex natural scenes. Based on their binocular response properties, these cells provide a substantial reduction in the complexity of the correspondence problem.

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Orientation-tuned surround suppression improves computational models of human visual cortex

Journal of Vision ◽

10.1167/15.12.1001 ◽

2015 ◽

Vol 15 (12) ◽

pp. 1001

Author(s):

Catherine Olsson ◽

Kendrick Kay ◽

Jonathan Winawer

Keyword(s):

Visual Cortex ◽

Computational Models ◽

Surround Suppression ◽

Human Visual Cortex

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Selective effects of aging on simple and complex cells in primary visual cortex of rhesus monkeys

Brain Research ◽

10.1016/j.brainres.2012.06.017 ◽

2012 ◽

Vol 1470 ◽

pp. 17-23 ◽

Cited By ~ 4

Author(s):

Zhen Liang ◽

Hongxin Li ◽

Yun Yang ◽

Guangxing Li ◽

Yong Tang ◽

...

Keyword(s):

Visual Cortex ◽

Primary Visual Cortex ◽

Rhesus Monkeys ◽

Complex Cells ◽

Simple And Complex Cells ◽

Effects Of Aging ◽

Selective Effects

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Self-Organization of Local Cortical Circuits and Cortical Orientation Maps: A Nonlinear Hebbian Model of the Visual Cortex with Adaptive Lateral Couplings

Zeitschrift für Naturforschung C ◽

10.1515/znc-2001-5-622 ◽

2001 ◽

Vol 56 (5-6) ◽

pp. 464-478 ◽

Cited By ~ 1

Author(s):

Thomas Burger ◽

Wolfgang Lang

Keyword(s):

Neural Network ◽

Visual Cortex ◽

Recurrent Neural Network ◽

Neural Network Model ◽

Self Organization ◽

Cortical Circuits ◽

Random Input ◽

Intracortical Circuits ◽

Orientation Maps ◽

Lateral Coupling

A nonlinear, recurrent neural network model of the visual cortex is presented. Orientation maps emerge from adaptable afferent as well as plastic local intracortical circuits driven by random input stimuli. Lateral coupling structures self-organize into DOG profiles under the influence of pronounced emerging cortical activity blobs. The model’s simplified architecture and features are modeled to largely mimik neurobiological findings.

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