scholarly journals Computing Optical Flow in the Primate Visual System

1989 ◽  
Vol 1 (1) ◽  
pp. 92-103 ◽  
Author(s):  
H. Taichi Wang ◽  
Bimal Mathur ◽  
Christof Koch
Keyword(s):  
Visual System ◽  
Optical Flow ◽  
Difficult Problem ◽  
Population Coding ◽  
Spatial Integration ◽  
Local Motion ◽  
Temporal Area ◽  
Second Stage ◽  
Vector Sum ◽  
Cooperative Algorithm

Computing motion on the basis of the time-varying image intensity is a difficult problem for both artificial and biological vision systems. We show how gradient models, a well-known class of motion algorithms, can be implemented within the magnocellular pathway of the primate's visual system. Our cooperative algorithm computes optical flow in two steps. In the first stage, assumed to be located in primary visual cortex, local motion is measured while spatial integration occurs in the second stage, assumed to be located in the middle temporal area (MT). The final optical flow is extracted in this second stage using population coding, such that the velocity is represented by the vector sum of neurons coding for motion in different directions. Our theory, relating the single-cell to the perceptual level, accounts for a number of psychophysical and electrophysiological observations and illusions.

scholarly journals Computing motion in the primate's visual system

1989 ◽  
Vol 146 (1) ◽  
pp. 115-139
Author(s):  
C. Koch ◽  
H. T. Wang ◽  
B. Mathur
Keyword(s):  
Flow Field ◽  
Visual System ◽  
Optical Flow ◽  
Visual Motion ◽  
Population Coding ◽  
Spatial Integration ◽  
Local Motion ◽  
Temporal Area ◽  
Second Stage ◽  
Optical Flow Field

Computing motion on the basis of the time-varying image intensity is a difficult problem for both artificial and biological vision systems. We will show how one well-known gradient-based computer algorithm for estimating visual motion can be implemented within the primate's visual system. This relaxation algorithm computes the optical flow field by minimizing a variational functional of a form commonly encountered in early vision, and is performed in two steps. In the first stage, local motion is computed, while in the second stage spatial integration occurs. Neurons in the second stage represent the optical flow field via a population-coding scheme, such that the vector sum of all neurons at each location codes for the direction and magnitude of the velocity at that location. The resulting network maps onto the magnocellular pathway of the primate visual system, in particular onto cells in the primary visual cortex (V1) as well as onto cells in the middle temporal area (MT). Our algorithm mimics a number of psychophysical phenomena and illusions (perception of coherent plaids, motion capture, motion coherence) as well as electrophysiological recordings. Thus, a single unifying principle ‘the final optical flow should be as smooth as possible’ (except at isolated motion discontinuities) explains a large number of phenomena and links single-cell behavior with perception and computational theory.

Improving motion detection via anodal transcranial direct current stimulation

2020 ◽  
Vol 38 (5) ◽  
pp. 395-405
Author(s):  
Luca Battaglini ◽  
Federica Mena ◽  
Clara Casco
Keyword(s):  
Direct Current ◽  
Signal To Noise Ratio ◽  
Individual Performance ◽  
Coherent Motion ◽  
Motion Processing ◽  
Global Motion ◽  
Local Motion ◽  
Processing Efficiency ◽  
Temporal Area ◽  
Direct Current Stimulation

Background: To study motion perception, a stimulus consisting of a field of small, moving dots is often used. Generally, some of the dots coherently move in the same direction (signal) while the rest move randomly (noise). A percept of global coherent motion (CM) results when many different local motion signals are combined. CM computation is a complex process that requires the integrity of the middle-temporal area (MT/V5) and there is evidence that increasing the number of dots presented in the stimulus makes such computation more efficient. Objective: In this study, we explored whether anodal direct current stimulation (tDCS) over MT/V5 would increase individual performance in a CM task at a low signal-to-noise ratio (SNR, i.e. low percentage of coherent dots) and with a target consisting of a large number of moving dots (high dot numerosity, e.g. >250 dots) with respect to low dot numerosity (<60 dots), indicating that tDCS favour the integration of local motion signal into a single global percept (global motion). Method: Participants were asked to perform a CM detection task (two-interval forced-choice, 2IFC) while they received anodal, cathodal, or sham stimulation on three different days. Results: Our findings showed no effect of cathodal tDCS with respect to the sham condition. Instead, anodal tDCS improves performance, but mostly when dot numerosity is high (>400 dots) to promote efficient global motion processing. Conclusions: The present study suggests that tDCS may be used under appropriate stimulus conditions (low SNR and high dot numerosity) to boost the global motion processing efficiency, and may be useful to empower clinical protocols to treat visual deficits.

A model for the estimate of local image velocity by cells in the visual cortex

1990 ◽  
Vol 239 (1295) ◽  
pp. 129-161 ◽  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Frequency Tuning ◽  
Temporal Frequency ◽  
Spatial Integration ◽  
Temporal Area ◽  
Image Velocity ◽  
Tuning Curves ◽  
Spatio Temporal ◽  
Local Image

Some computational theories of motion perception assume that the first stage en route to this perception is the local estimate of image velocity. However, this assumption is not supported by data from the primary visual cortex. Its motion sensitive cells are not selective to velocity, but rather are directionally selective and tuned to spatio-temporal frequen­cies. Accordingly, physiologically based theories start with filters selec­tive to oriented spatio-temporal frequencies. This paper shows that computational and physiological theories do not necessarily conflict, because such filters may, as a population, compute velocity locally. To prove this point, we show how to combine the outputs of a class of frequency tuned filters to detect local image velocity. Furthermore, we show that the combination of filters may simulate ‘Pattern’ cells in the middle temporal area (MT), whereas each filter simulates primary visual cortex cells. These simulations include three properties of the primary cortex. First, the spatio-temporal frequency tuning curves of the in­dividual filters display approximate space-time separability. Secondly, their direction-of-motion tuning curves depend on the distribution of orientations of the components of the Fourier decomposition and speed of the stimulus. Thirdly, the filters show facilitation and suppression for responses to apparent motions in the preferred and null directions, respect­ively. It is suggested that the MT’s role is not to solve the aperture problem, but to estimate velocities from primary cortex information. The spatial integration that accounts for motion coherence may be postponed to a later cortical stage.

scholarly journals Unique spatial integration in mouse primary visual cortex and higher visual areas

2019 ◽  
Author(s):  
Kevin A. Murgas ◽  
Ashley M. Wilson ◽  
Valerie Michael ◽  
Lindsey L. Glickfeld
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Spatial Information ◽  
Spatial Scales ◽  
Spatial Integration ◽  
Global Features ◽  
Wide Range ◽  
Visual Areas ◽  
Higher Visual Areas

AbstractNeurons in the visual system integrate over a wide range of spatial scales. This diversity is thought to enable both local and global computations. To understand how spatial information is encoded across the mouse visual system, we use two-photon imaging to measure receptive fields in primary visual cortex (V1) and three downstream higher visual areas (HVAs): LM (lateromedial), AL (anterolateral) and PM (posteromedial). We find significantly larger receptive field sizes and less surround suppression in PM than in V1 or the other HVAs. Unlike other visual features studied in this system, specialization of spatial integration in PM cannot be explained by specific projections from V1 to the HVAs. Instead, our data suggests that distinct connectivity within PM may support the area’s unique ability to encode global features of the visual scene, whereas V1, LM and AL may be more specialized for processing local features.

scholarly journals Attentional Modulation of Visual Spatial Integration: Psychophysical Evidence Supported by Population Coding Modeling

2019 ◽  
Vol 31 (9) ◽  
pp. 1329-1342
Author(s):  
Alessandro Grillini ◽  
Remco J. Renken ◽  
Frans W. Cornelissen
Keyword(s):  
Selective Attention ◽  
Population Coding ◽  
Spatial Integration ◽  
Visual Spatial ◽  
Neural Populations ◽  
Feature Based ◽  
Flow Through ◽  
Opposing Effects ◽  
Neuronal Encoding ◽  
The Brain

Two prominent strategies that the human visual system uses to reduce incoming information are spatial integration and selective attention. Whereas spatial integration summarizes and combines information over the visual field, selective attention can single it out for scrutiny. The way in which these well-known mechanisms—with rather opposing effects—interact remains largely unknown. To address this, we had observers perform a gaze-contingent search task that nudged them to deploy either spatial or feature-based attention to maximize performance. We found that, depending on the type of attention employed, visual spatial integration strength changed either in a strong and localized or a more modest and global manner compared with a baseline condition. Population code modeling revealed that a single mechanism can account for both observations: Attention acts beyond the neuronal encoding stage to tune the spatial integration weights of neural populations. Our study shows how attention and integration interact to optimize the information flow through the brain.

Population coding of motion patterns in the early visual system

2001 ◽  
Vol 187 (7) ◽  
pp. 549-558 ◽  
Author(s):  
Stefan Wilke ◽  
Andreas Thiel ◽  
Christian Eurich ◽  
Martin Greschner ◽  
Markus Bongard ◽  
...  
Keyword(s):  
Visual System ◽  
Population Coding ◽  
Motion Patterns ◽  
Early Visual System

Direct and Indirect Cooperation between Temporal and Parietal Networks for Invariant Visual Recognition

1992 ◽  
Vol 4 (1) ◽  
pp. 35-57 ◽  
Author(s):  
Isabelle Otto ◽  
Philippe Grandguillaume ◽  
Latifa Boutkhil ◽  
Yves Burnod ◽  
Emmanuel GuigonBurnod
Keyword(s):  
Receptive Field ◽  
Visual System ◽  
Visual Recognition ◽  
Single Cells ◽  
Central Zone ◽  
Object Identification ◽  
Population Coding ◽  
Significant Shift ◽  
Learning Session ◽  
Population Vector

A new type of biologically inspired multilayered network is proposed to model the properties of the primate visual system with respect to invariant visual recognition (IVR). This model is based on 10 major neurobiological and psychological constraints. The first five constraints shape the architecture and properties of the network. 1. The network model has a Y-like double-branched multilayered architecture, with one input (the retina) and two parallel outputs, the “What” and the “Where,” which model, respectively, the temporal pathway, specialized for “object” identification, and the parietal pathway specialized for “spatial” localization. 2. Four processing layers are sufficient to model the main functional steps of primate visual system that transform the retinal information into prototypes (object-centered reference frame) in the “What” branch and into an oculomotor command in the “Where” branch. 3. The distribution of receptive field sizes within and between the two functional pathways provides an appropriate tradeoff between discrimination and invariant recognition capabilities. 4. The two outputs are represented by a population coding: the ocular command is computed as a population vector in the “Where” branch and the prototypes are coded in a “semidistributed” way in the “What” branch. In the intermediate associative steps, processing units learn to associate prototypes (through feedback connections) to component features (through feedforward ones). 5. The basic processing units of the network do not model single cells but model the local neuronal circuits that combine different information flows organized in separate cortical layers. Such a biologically constrained model shows shift-invariant and size-invariant capabilities that resemble those of humans (psychological constraints): 6. During the Learning session, a set of patterns (26 capital letters and 2 geometric figures) are presented to the network: a single presentation of each pattern in one position (at the center) and with one size is sufficient to learn the corresponding prototypes (internal representations). These patterns are thus presented in widely varying new sizes and positions during the Recognition session: 7. The “What” branch of the network succeeds in immediate recognition for patterns presented in the central zone of the retina with the learned size. 8. The recognition by the “What” branch is resistant to changes in size within a limited range of variation related to the distribution of receptive field (RF) sizes in the successive processing steps of this pathway. 9. Even when ocular movements are not allowed, the recognition capabilities of the “What” branch are unaffected by changing positions around the learned one. This significant shift-invariance of the “What” branch is also related to the distribution of RF sizes. 10. When varying both sizes and locations, the “What” and the “Where” branches cooperate for recognition: the location coding in the “Where” branch can command, under the control of the “What” branch, an ocular movement efficient to reset peripheral patterns toward the central zone of the retina until successful recognition. This model results in predictions about anatomical connections and physiological interactions between temporal and parietal cortices.

scholarly journals Enhanced effects from tiny flexible in-wall blips and shear flow

2015 ◽  
Vol 772 ◽  
pp. 16-41 ◽  
Author(s):  
Luisa Pruessner ◽  
Frank Smith
Keyword(s):  
Boundary Layer ◽  
Interactive Effect ◽  
Fluid Motion ◽  
Unsteady Motion ◽  
Local Motion ◽  
Second Stage ◽  
Order Of Magnitude ◽  
High Reynolds ◽  
Parameter Values ◽  
Nonlinear Solutions

Fluid motion at high Reynolds number over a flexible in-wall blip (a compliant bump or dip in an otherwise fixed wall) is considered theoretically for a very short blip buried low inside a boundary layer. Only the near-wall shear of the oncoming flow affects the local motion past the tiny blip. Slowly evolving features are examined first to allow for variations in the incident flow. Linear and nonlinear solutions show that at certain parameter values (eigenvalues) intensifications occur in which the interactive effect on flow and blip shape is larger by an order of magnitude than at most parameter values. Similar findings apply to the boundary layer with several tiny blips present or to channel flows with blips of almost any length. These intensifications lead on to fully nonlinear unsteady motion as a second stage, after some delay, thus combining with finite-time breakups to form a distinct path into transition of the flow.

Local and Global Representations of Velocity: Transparency, Opponency, and Global Direction Perception

Perception ◽  
1997 ◽  
Vol 26 (8) ◽  
pp. 995-1010 ◽  
Author(s):  
Oliver Braddick
Keyword(s):  
Visual Motion ◽  
Multiple Scales ◽  
Human Subjects ◽  
Motion Processing ◽  
Spatial Integration ◽  
Local Motion ◽  
Directional Information ◽  
Object Based ◽  
Transparent Display ◽  
Winner Take All

Human subjects can perceive global motion or motions in displays containing diverse local motions, implying representation of velocity at multiple scales. The phenomena of flexible global direction judgments, and especially of motion transparency, also raise the issue of whether the representation of velocity at any one scale is single-valued or multi-valued. A new performance-based measure of transparency confirms that the visual system represents directional information for each component of a transparent display. However, results with the locally paired random-dot display introduced by Qian et al, show that representations of multiple velocities do not coexist at the finest spatial scale of motion analysis. Functionally distinct scales of motion processing may be associated with (i) local motion detectors which show a strong winner-take-all interaction; (ii) spatial integration of local signals to disambiguate velocity; (iii) selection of reliable velocity signals as proposed in the model of Nowlan and Sejnowski; (iv) object-based or surface-based representations that are not necessarily organised in a fixed spatial matrix. These possibilities are discussed in relation to the neurobiological organisation of the visual motion pathway.

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