A Sparse Generative Model of V1 Simple Cells with Intrinsic Plasticity

2008 ◽  
Vol 20 (5) ◽  
pp. 1261-1284 ◽  
Author(s):  
Cornelius Weber ◽  
Jochen Triesch
Keyword(s):  
Dynamic Environment ◽  
Generative Model ◽  
Intrinsic Excitability ◽  
Natural Image ◽  
Tilt Aftereffect ◽  
Simple Cells ◽  
Feature Detectors ◽  
Edge Detectors ◽  
Intrinsic Plasticity ◽  
Slow Timescale

Current models for learning feature detectors work on two timescales: on a fast timescale, the internal neurons' activations adapt to the current stimulus; on a slow timescale, the weights adapt to the statistics of the set of stimuli. Here we explore the adaptation of a neuron's intrinsic excitability, termed intrinsic plasticity, which occurs on a separate timescale. Here, a neuron maintains homeostasis of an exponentially distributed firing rate in a dynamic environment. We exploit this in the context of a generative model to impose sparse coding. With natural image input, localized edge detectors emerge as models of V1 simple cells. An intermediate timescale for the intrinsic plasticity parameters allows modeling aftereffects. In the tilt aftereffect, after a viewer adapts to a grid of a certain orientation, grids of a nearby orientation will be perceived as tilted away from the adapted orientation. Our results show that adapting the neurons' gain-parameter but not the threshold-parameter accounts for this effect. It occurs because neurons coding for the adapting stimulus attenuate their gain, while others increase it. Despite its simplicity and low maintenance, the intrinsic plasticity model accounts for more experimental details than previous models without this mechanism.

A Psychophysical Investigation of the Behavioural Relevance of Neurophysiological Feature Detectors (S Cells)

Perception ◽  
1982 ◽  
Vol 11 (1) ◽  
pp. 19-23 ◽  
Author(s):  
Gerda Smets
Keyword(s):  
Interaction Effect ◽  
Pure Tone ◽  
Oblique Effect ◽  
Auditory Stimulation ◽  
Experimental Session ◽  
Simple Cells ◽  
Feature Detectors ◽  
Monocular Stimulation ◽  
Class 1 ◽  
Perceptual Variable

The aim of the study was to establish whether monaural auditory stimulation (a nonretinal perceptual variable) affects the class 1 oblique effect (a behavioural manifestation of simple cells). The left or right monaural stimulus was a pure tone, 1000 Hz, 70 dB(A), delivered continuously throughout the experimental session. The left or right monocular stimulus was a thin red phosphorescent bar the orientation of which was manipulated. In order to determine the oblique effect differential orientation thresholds for principal meridians were compared to those for oblique orientations. The results, indicating an interaction effect of the monaural and monocular stimulation on the magnitude of the oblique effect, are a further demonstration that the oblique effect is not as simple as some theories (derived from extrapolation from neurophysiological findings) would imply.

A model for simple cells as optimal edge detectors

1991 ◽  
Vol 66 (2) ◽  
pp. 177-183
Author(s):  
W. McIlhagga
Keyword(s):  
Simple Cells ◽  
Edge Detectors

scholarly journals Detecting object boundaries in natural images requires ‘incitatory’ cell-cell interactions

2018 ◽  
Author(s):  
Gabriel C. Mel ◽  
Chaithanya A. Ramachandra ◽  
Bartlett W. Mel
Keyword(s):  
Visual Cortex ◽  
Learning Rule ◽  
Cell Interactions ◽  
Simple Cell ◽  
Natural Image ◽  
Linear Filter ◽  
Natural Classification ◽  
Simple Cells ◽  
Boundary Cell ◽  
Cell Cell

AbstractDetecting object boundaries is crucial for recognition, but how the process unfolds in visual cortex remains unknown. To study the problem faced by a hypothetical boundary cell, and to predict how cortical circuitry could produce a boundary cell from a population of conventional “simple cells”, we labeled 30,000 natural image patches and used Bayes’ rule to determine how a simple cell should influence a nearby boundary cell depending on its relative offset in receptive field position and orientation. We identified three basic types of cell-cell interactions: rising and falling interactions with a range of slopes and saturation rates, as well as non-monotonic (bump-shaped) interactions with varying modes and amplitudes. Using simple models we show that a ubiquitous cortical circuit motif consisting of direct excitation and indirect inhibition – a compound effect we call “incitation” – can produce the entire spectrum of simple cell-boundary cell interactions found in our dataset. Moreover, we show that the synaptic weights that parameterize an incitation circuit can be learned by a simple (1-layer) learning rule. We conclude that incitatory interconnections are a generally useful computing mechanism that the cortex may exploit to help solve difficult natural classification problems.Significance statementSimple cells in primary visual cortex (V1) respond to oriented edges, and have long been supposed to detect object boundaries, yet the prevailing model of a simple cell – a divisively normalized linear filter – is a surprisingly poor natural boundary detector. To understand why, we analyzed image statistics on and off object boundaries, allowing us to characterize the neural-style computations needed to perform well at this difficult natural classification task. We show that a simple circuit motif known to exist in V1 is capable of extracting high-quality boundary probability signals from local populations of simple cells. Our findings suggest a new, more general way of conceptualizing cell-cell interconnections in the cortex.

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.

scholarly journals The Two-Dimensional Gabor Function Adapted to Natural Image Statistics: A Model of Simple-Cell Receptive Fields and Sparse Structure in Images

2017 ◽  
Vol 29 (10) ◽  
pp. 2769-2799 ◽  
Author(s):  
P. N. Loxley
Keyword(s):  
Receptive Field ◽  
Spatial Frequency ◽  
Generative Model ◽  
Natural Image ◽  
Two Dimensional ◽  
Natural Image Statistics ◽  
Image Statistics ◽  
Gabor Function ◽  
Gabor Functions ◽  
Probabilistic Generative Model

The two-dimensional Gabor function is adapted to natural image statistics, leading to a tractable probabilistic generative model that can be used to model simple cell receptive field profiles, or generate basis functions for sparse coding applications. Learning is found to be most pronounced in three Gabor function parameters representing the size and spatial frequency of the two-dimensional Gabor function and characterized by a nonuniform probability distribution with heavy tails. All three parameters are found to be strongly correlated, resulting in a basis of multiscale Gabor functions with similar aspect ratios and size-dependent spatial frequencies. A key finding is that the distribution of receptive-field sizes is scale invariant over a wide range of values, so there is no characteristic receptive field size selected by natural image statistics. The Gabor function aspect ratio is found to be approximately conserved by the learning rules and is therefore not well determined by natural image statistics. This allows for three distinct solutions: a basis of Gabor functions with sharp orientation resolution at the expense of spatial-frequency resolution, a basis of Gabor functions with sharp spatial-frequency resolution at the expense of orientation resolution, or a basis with unit aspect ratio. Arbitrary mixtures of all three cases are also possible. Two parameters controlling the shape of the marginal distributions in a probabilistic generative model fully account for all three solutions. The best-performing probabilistic generative model for sparse coding applications is found to be a gaussian copula with Pareto marginal probability density functions.

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.

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