A Neural Network Model of Tilt Aftereffects

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 87-87
Author(s):  
J A Bednar ◽  
R Miikkulainen
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Indirect Effects ◽  
Lateral Interactions ◽  
Tilt Aftereffect ◽  
Feature Detectors ◽  
Large Numbers ◽  
Computational Support ◽  
Orientation Maps ◽  
Short Time

RF-LISSOM, a self-organising model of laterally connected orientation maps in the primary visual cortex, was used to study the psychological phenomenon known as the tilt aftereffect. The model allows observation of activation and connection patterns between large numbers of neurons simultaneously, making it possible to relate higher-level phenomena to low-level events, which is difficult to do experimentally. In RF-LISSOM, the same self-organising processes that are responsible for the development of the orientation map and its lateral connections are shown to result in tilt aftereffects over short time scales in the adult. The results give computational support for the idea that direct tilt aftereffects arise from adaptive lateral interactions between feature detectors, as has long been surmised. They also suggest that indirect effects could result from the conservation of synaptic resources during this process. The model thus provides a unified computational explanation of self-organisation and both direct and indirect tilt aftereffects in the primary visual cortex.

Tilt Aftereffects in a Self-Organizing Model of the Primary Visual Cortex

2000 ◽  
Vol 12 (7) ◽  
pp. 1721-1740 ◽  
Author(s):  
James A. Bednar ◽  
Risto Miikkulainen
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Tilt Aftereffect ◽  
Simultaneous Observation ◽  
Feature Detectors ◽  
Large Numbers ◽  
Computational Support ◽  
High Level ◽  
Self Organizing

RF-LISSOM, a self-organizing model of laterally connected orientation maps in the primary visual cortex, was used to study the psychological phenomenon known as the tilt aftereffect. The same self-organizing processes that are responsible for the long-term development of the map are shown to result in tilt aftereffects over short timescales in the adult. The model permits simultaneous observation of large numbers of neurons and connections, making it possible to relate high-level phenomena to low-level events, which is difficult to do experimentally. The results give detailed computational support for the long-standing conjecture that the direct tilt aftereffect arises from adaptive lateral interactions between feature detectors. They also make a new prediction that the indirect effect results from the normalization of synaptic efficacies during this process. The model thus provides a unified computational explanation of self-organization and both the direct and indirect tilt aftereffect in the primary visual cortex.

Learning Invariance from Natural Images Inspired by Observations in the Primary Visual Cortex

2012 ◽  
Vol 24 (5) ◽  
pp. 1271-1296 ◽  
Author(s):  
Michael Teichmann ◽  
Jan Wiltschut ◽  
Fred Hamker
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Hebbian Learning ◽  
Natural Images ◽  
Complex Cells ◽  
Cell Responses ◽  
Feature Detectors ◽  
Learning Rules ◽  
Static Images ◽  
Good Interpretation

The human visual system has the remarkable ability to largely recognize objects invariant of their position, rotation, and scale. A good interpretation of neurobiological findings involves a computational model that simulates signal processing of the visual cortex. In part, this is likely achieved step by step from early to late areas of visual perception. While several algorithms have been proposed for learning feature detectors, only few studies at hand cover the issue of biologically plausible learning of such invariance. In this study, a set of Hebbian learning rules based on calcium dynamics and homeostatic regulations of single neurons is proposed. Their performance is verified within a simple model of the primary visual cortex to learn so-called complex cells, based on a sequence of static images. As a result, the learned complex-cell responses are largely invariant to phase and position.

scholarly journals Vision reconstructs the cellular composition of binocular circuitry during the critical period

2020 ◽  
Author(s):  
Liming Tan ◽  
Elaine Tring ◽  
Dario L. Ringach ◽  
S. Lawrence Zipursky ◽  
Joshua T. Trachtenberg
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Primary Visual Cortex ◽  
Critical Period ◽  
Cellular Composition ◽  
Feature Detectors ◽  
Layer 2 ◽  
Layer 4 ◽  
High Acuity ◽  
Longitudinal Imaging

AbstractHigh acuity binocularity is established in primary visual cortex during an early postnatal critical period. In contrast to current models for the developmental of binocular neurons, we find that the binocular network present at the onset of the critical period is dismantled and remade. Using longitudinal imaging of receptive field tuning (e.g. orientation selectivity) of thousands of layer 2/3 neurons through development, we show most binocular neurons present at critical-period onset are poorly tuned and rendered monocular. These are replenished by newly formed binocular neurons that are established by a vision-dependent recruitment of well-tuned ipsilateral inputs to contralateral monocular neurons with matched tuning properties. The binocular network in layer 4 is equally unstable but does not improve. Thus, vision instructs a new and more sharply tuned binocular network in layer 2/3 by exchanging one population of neurons for another and not by refining an extant network.One Sentence SummaryUnstable binocular circuitry is transformed by vision into a network of highly tuned complex feature detectors in the cortex.

Lateral interactions in primary visual cortex: a model bridging physiology and psychophysics

Science ◽  
1995 ◽  
Vol 269 (5232) ◽  
pp. 1877-1880 ◽  
Author(s):  
M Stemmler ◽  
M Usher ◽  
E Niebur
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Lateral Interactions

scholarly journals Optimizing the Representation of Orientation Preference Maps in Visual Cortex

2015 ◽  
Vol 27 (1) ◽  
pp. 32-41
Author(s):  
Nicholas J. Hughes ◽  
Geoffrey J. Goodhill
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Color Space ◽  
Orientation Preference ◽  
Color Mapping ◽  
Functional Brain ◽  
Standard Representation ◽  
Visual Impression ◽  
Orientation Maps ◽  
True Structure

The colorful representation of orientation preference maps in primary visual cortex has become iconic. However, the standard representation is misleading because it uses a color mapping to indicate orientations based on the HSV (hue, saturation, value) color space, for which important perceptual features such as brightness, and not just hue, vary among orientations. This means that some orientations stand out more than others, conveying a distorted visual impression. This is particularly problematic for visualizing subtle biases caused by slight overrepresentation of some orientations due to, for example, stripe rearing. We show that displaying orientation maps with a color mapping based on a slightly modified version of the HCL (hue, chroma, lightness) color space, so that primarily only hue varies between orientations, leads to a more balanced visual impression. This makes it easier to perceive the true structure of this seminal example of functional brain architecture.

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