Sparse coding models demonstrate some non-classical receptive field effects
TL;DR: The results suggest that a sparse coding model can explain many of the nonlinear effects in V1 cells, and is therefore a reasonable candidate for a functional model of striate cortex.
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Abstract: Non-classical receptive field (nCRF) effects include several response properties in V1 neurons not explained by a linear-nonlinear (LN) receptive field model, but instead requiring significant interactions between V1 neurons Using a sparse coding model [1,2] and bar and grating stimuli, simulated physiology experiments were carried out that replicated several nCRF phenomena reported previously in neurophysiology experiments These include: end-stopping [3] (Fig (Fig1),1), contrast invariance of orientation tuning [4] (Fig (Fig2),2), radius, orientation, and contrast tunings of surround suppression [5,6] (Fig (Fig3,3, ,4,4, ,5)5) The results suggest that a sparse coding model can explain many of the nonlinear effects in V1 cells, and is therefore a reasonable candidate for a functional model of striate cortex
Figure 1
End-stopping Comparison with a LN model
Figure 2
Contrast invariance of orientation tuning
Figure 3
Surround suppression at different contrasts
Figure 4
Orientation tuning of surround suppression
Figure 5
Surround orientation influences contrast tuning
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Citations
Visual Nonclassical Receptive Field Effects Emerge from Sparse Coding in a Dynamical System
TL;DR: The results show that the sparse coding hypothesis, when coupled with a biophysically plausible implementation, can provide a unified high-level functional interpretation to many response properties that have generally been viewed through distinct mechanistic or phenomenological models.
20 Years of Learning About Vision: Questions Answered, Questions Unanswered, and Questions Not Yet Asked
Bruno A. Olshausen
- 01 Jan 2013
TL;DR: It is argued that the biggest mysteries about how visual systems work are likely to be ones the authors are not currently aware of, and that bearing this in mind is important as it encourages a more exploratory, as opposed to strictly hypothesis-driven, approach.
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Biologically realistic excitatory and inhibitory cell properties emerge from a sparse coding network
TL;DR: It is demonstrated that many of the aforementioned excitatory cell and inhibitory cell properties emerge naturally from a network that implements sparse coding, and that the network exhibits balanced excitation and inhibition, as a result of the receptive field structure.
•Dissertation
Sparse coding models of neural response in the primary visual cortex
Mengchen Zhu
- 14 May 2015
2013 Special Issue: Configurable hardware integrate and fire neurons for sparse approximation
TL;DR: A Hopfield-Network-like system of integrate and fire neurons is proposed as a solution, using the Locally Competitive Algorithm (LCA) to solve an overcomplete L1 sparse approximation problem, which compares favorably with state-of-the-art digital solutions, and analog solutions using a non-spiking approach.
References
Sparse Coding with an Overcomplete Basis Set: A Strategy Employed by V1 ?
TL;DR: These deviations from linearity provide a potential explanation for the weak forms of non-linearity observed in the response properties of cortical simple cells, and they further make predictions about the expected interactions among units in response to naturalistic stimuli.
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Sparse coding via thresholding and local competition in neural circuits
TL;DR: A locally competitive algorithm (LCA) is described that solves a collection of sparse coding principles minimizing a weighted combination of mean-squared error and a coefficient cost function to produce coefficients with sparsity levels comparable to the most popular centralized sparse coding algorithms while being readily suited for neural implementation.
Generation of end-inhibition in the visual cortex via interlaminar connections
Jürgen Bolz,Charles D. Gilbert +1 more
TL;DR: The functional role of the layer 6 to layer 4 projection is determined by reversible inactivation of layer 6 using the inhibitory transmitter γ-aminobutyric acid (GABA) and cells in layer 4 lost end-inhibition.
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The effects of contrast on visual orientation and spatial frequency discrimination: a comparison of single cells and behavior
TL;DR: In this article, the effects of contrast on human psychophysical orientation and spatial frequency discrimination thresholds and on the responses of individual neurons in the cat's striate cortex have been compared, and it was shown that, on average, the discrimination of orientation or spatial frequency improves with contrast at low contrasts more than at higher contrasts.
Selectivity and spatial distribution of signals from the receptive field surround in macaque V1 neurons
TL;DR: The surround has complex effects on responses from the classical receptive field and it is suggested that the underlying mechanism of this complexity may involve interactions between relatively simple center and surround mechanisms.