scholarly journals Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity

2021 ◽  
Vol 17 (11) ◽  
pp. e1009566
Author(s):  
René Larisch ◽  
Lorenz Gönner ◽  
Michael Teichmann ◽  
Fred H. Hamker
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Gain Control ◽  
Metabolic Efficiency ◽  
Tuning Curves ◽  
Coding Efficiency ◽  
Spiking Network ◽  
Inhibitory Feedback ◽  
Efficient Code ◽  
Inhibitory Plasticity

Visual stimuli are represented by a highly efficient code in the primary visual cortex, but the development of this code is still unclear. Two distinct factors control coding efficiency: Representational efficiency, which is determined by neuronal tuning diversity, and metabolic efficiency, which is influenced by neuronal gain. How these determinants of coding efficiency are shaped during development, supported by excitatory and inhibitory plasticity, is only partially understood. We investigate a fully plastic spiking network of the primary visual cortex, building on phenomenological plasticity rules. Our results suggest that inhibitory plasticity is key to the emergence of tuning diversity and accurate input encoding. We show that inhibitory feedback (random and specific) increases the metabolic efficiency by implementing a gain control mechanism. Interestingly, this led to the spontaneous emergence of contrast-invariant tuning curves. Our findings highlight that (1) interneuron plasticity is key to the development of tuning diversity and (2) that efficient sensory representations are an emergent property of the resulting network.

scholarly journals Sensory coding and contrast invariance emerge from the control of plastic inhibition over excitatory connectivity

2020 ◽  
Author(s):  
René Larisch ◽  
Lorenz Gönner ◽  
Michael Teichmann ◽  
Fred H. Hamker
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Receptive Fields ◽  
Gain Control ◽  
Metabolic Efficiency ◽  
Tuning Curves ◽  
Coding Efficiency ◽  
Inhibitory Feedback ◽  
Efficient Code ◽  
Inhibitory Plasticity

Visual stimuli are represented by a highly efficient code in the primary visual cortex, but the development of this code is still unclear. Two distinct factors control coding efficiency: Representational efficiency, which is determined by neuronal tuning diversity, and metabolic efficiency, which is influenced by neuronal gain. How these determinants of coding efficiency are shaped during development, supported by excitatory and inhibitory plasticity, is only partially understood. We investigate a fully plastic spiking network of the primary visual cortex, building on phenomenological plasticity rules. Our results show that inhibitory plasticity is key to the emergence of tuning diversity and accurate input encoding. Additionally, inhibitory feedback increases the metabolic efficiency by implementing a gain control mechanism. Interestingly, this led to the spontaneous emergence of contrast-invariant tuning curves. Our findings highlight the role of interneuron plasticity during the development of receptive fields and in shaping sensory representations.

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.

A Selective Sparse Coding Model with Embedded Attention Mechanism

2011 ◽  
pp. 78-90
Author(s):  
Qingyong Li ◽  
Zhiping Shi ◽  
Zhongzhi Shi
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Sparse Coding ◽  
Coding Theory ◽  
Neural Representation ◽  
Natural Scenes ◽  
Processing Capacity ◽  
Uniform Sampling ◽  
Coding Efficiency ◽  
Non Uniform Sampling

Sparse coding theory demonstrates that the neurons in the primary visual cortex form a sparse representation of natural scenes in the viewpoint of statistics, but a typical scene contains many different patterns (corresponding to neurons in cortex) competing for neural representation because of the limited processing capacity of the visual system. We propose an attention-guided sparse coding model. This model includes two modules: the non-uniform sampling module simulating the process of retina and a data-driven attention module based on the response saliency. Our experiment results show that the model notably decreases the number of coefficients which may be activated, and retains the main vision information at the same time. It provides a way to improve the coding efficiency for sparse coding model and to achieve good performance in both population sparseness and lifetime sparseness.

Neural responses to relative speed in the primary visual cortex of rhesus monkey

2003 ◽  
Vol 20 (1) ◽  
pp. 77-84 ◽  
Author(s):  
AN CAO ◽  
PETER H. SCHILLER
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Relative Motion ◽  
Motion Parallax ◽  
Relative Speed ◽  
Neural Responses ◽  
Tuning Curves ◽  
V1 Neurons ◽  
Cortex Area ◽  
Ground Segmentation

Relative motion information, especially relative speed between different input patterns, is required for solving many complex tasks of the visual system, such as depth perception by motion parallax and motion-induced figure/ground segmentation. However, little is known about the neural substrate for processing relative speed information. To explore the neural mechanisms for relative speed, we recorded single-unit responses to relative motion in the primary visual cortex (area V1) of rhesus monkeys while presenting sets of random-dot arrays moving at different speeds. We found that most V1 neurons were sensitive to the existence of a discontinuity in speed, that is, they showed higher responses when relative motion was presented compared to homogenous field motion. Seventy percent of the neurons in our sample responded predominantly to relative rather than to absolute speed. Relative speed tuning curves were similar at different center–surround velocity combinations. These relative motion-sensitive neurons in macaque area V1 probably contribute to figure/ground segmentation and motion discontinuity detection.

scholarly journals Sub-Optimality of the Early Visual System Explained Through Biologically Plausible Plasticity

2021 ◽  
Vol 15 ◽  
Author(s):  
Tushar Chauhan ◽  
Timothée Masquelier ◽  
Benoit R. Cottereau
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Receptive Fields ◽  
Spike Timing ◽  
Orientation Tuning ◽  
Biologically Relevant ◽  
Tuning Curves ◽  
Stdp Rule ◽  
Early Visual Cortex ◽  
Minimisation Problem

The early visual cortex is the site of crucial pre-processing for more complex, biologically relevant computations that drive perception and, ultimately, behaviour. This pre-processing is often studied under the assumption that neural populations are optimised for the most efficient (in terms of energy, information, spikes, etc.) representation of natural statistics. Normative models such as Independent Component Analysis (ICA) and Sparse Coding (SC) consider the phenomenon as a generative, minimisation problem which they assume the early cortical populations have evolved to solve. However, measurements in monkey and cat suggest that receptive fields (RFs) in the primary visual cortex are often noisy, blobby, and symmetrical, making them sub-optimal for operations such as edge-detection. We propose that this suboptimality occurs because the RFs do not emerge through a global minimisation of generative error, but through locally operating biological mechanisms such as spike-timing dependent plasticity (STDP). Using a network endowed with an abstract, rank-based STDP rule, we show that the shape and orientation tuning of the converged units are remarkably close to single-cell measurements in the macaque primary visual cortex. We quantify this similarity using physiological parameters (frequency-normalised spread vectors), information theoretic measures [Kullback–Leibler (KL) divergence and Gini index], as well as simulations of a typical electrophysiology experiment designed to estimate orientation tuning curves. Taken together, our results suggest that compared to purely generative schemes, process-based biophysical models may offer a better description of the suboptimality observed in the early visual cortex.

scholarly journals Orientation Tuning of Input Conductance, Excitation, and Inhibition in Cat Primary Visual Cortex

2000 ◽  
Vol 84 (2) ◽  
pp. 909-926 ◽  
Author(s):  
Jeffrey S. Anderson ◽  
Matteo Carandini ◽  
David Ferster
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Stimulation ◽  
Gain Control ◽  
Orientation Tuning ◽  
Visual Responses ◽  
Stimulus Contrast ◽  
Simple Cells ◽  
Conductance Changes ◽  
Input Conductance

The input conductance of cells in the cat primary visual cortex (V1) has been shown recently to grow substantially during visual stimulation. Because increasing conductance can have a divisive effect on the synaptic input, theoretical proposals have ascribed to it specific functions. According to the veto model, conductance increases would serve to sharpen orientation tuning by increasing most at off-optimal orientations. According to the normalization model, conductance increases would control the cell's gain, by being independent of stimulus orientation and by growing with stimulus contrast. We set out to test these proposals and to determine the visual properties and possible synaptic origin of the conductance increases. We recorded the membrane potential of cat V1 cells while injecting steady currents and presenting drifting grating patterns of varying contrast and orientation. Input conductance grew with stimulus contrast by 20–300%, generally more in simple cells (40–300%) than in complex cells (20–120%), and in simple cells was strongly modulated in time. Conductance was invariably maximal for stimuli of the preferred orientation. Thus conductance changes contribute to a gain control mechanism, but the strength of this gain control does not depend uniquely on contrast. By assuming that the conductance changes are entirely synaptic, we further derived the excitatory and inhibitory synaptic conductances underlying the visual responses. In simple cells, these conductances were often arranged in push-pull: excitation increased when inhibition decreased and vice versa. Excitation and inhibition had similar preferred orientations and did not appear to differ in tuning width, suggesting that the intracortical synaptic inputs to simple cells of cat V1 originate from cells with similar orientation tuning. This finding is at odds with models where orientation tuning in simple cells is achieved by inhibition at off-optimal orientations or sharpened by inhibition that is more broadly tuned than excitation.

scholarly journals Influence of Contrast on Orientation and Temporal Frequency Tuning in Ferret Primary Visual Cortex

2004 ◽  
Vol 91 (6) ◽  
pp. 2797-2808 ◽  
Author(s):  
Henry J. Alitto ◽  
W. Martin Usrey
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Cortical Neurons ◽  
Frequency Tuning ◽  
Temporal Frequency ◽  
Orientation Tuning ◽  
Visual Response ◽  
Stimulus Contrast ◽  
Tuning Curves ◽  
Simple And Complex Cells

Neurons in primary visual cortex are highly sensitive to the contrast, orientation, and temporal frequency of a visual stimulus. These three stimulus properties can be varied independently of one another, raising the question of how they interact to influence neuronal responses. We recorded from individual neurons in ferret primary visual cortex to determine the influence of stimulus contrast on orientation tuning, temporal-frequency tuning, and latency to visual response. Results show that orientation-tuning bandwidth is not affected by contrast level. Thus neurons in ferret visual cortex display contrast-invariant orientation tuning. Stimulus contrast does, however, influence the structure of orientation-tuning curves as measures of circular variance vary inversely with contrast for both simple and complex cells. This change in circular variance depends, in part, on a contrast-dependent change in the ratio of null to preferred orientation responses. Stimulus contrast also has an influence on the temporal-frequency tuning of cortical neurons. Both simple and complex cells display a contrast-dependent rightward shift in their temporal frequency-tuning curves that results in an increase in the highest temporal frequency needed to produce a half-maximum response (TF50). Results show that the degree of the contrast-dependent increase in TF50 is similar for cortical neurons and neurons in the lateral geniculate nucleus (LGN) and indicate that subcortical mechanisms likely play a major role in establishing the degree of effect displayed by downstream neurons. Finally, results show that LGN and cortical neurons experience a contrast-dependent phase advance in their visual response. This phase advance is most pronounced for cortical neurons indicating a role for both subcortical and cortical mechanisms.

scholarly journals Gain control from beyond the classical receptive field in primate primary visual cortex

2003 ◽  
Vol 20 (3) ◽  
pp. 221-230 ◽  
Author(s):  
BEN S. WEBB ◽  
CHRIS J. TINSLEY ◽  
NICK E. BARRACLOUGH ◽  
AMANDA PARKER ◽  
ANDREW M. DERRINGTON
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Primary Visual Cortex ◽  
Gain Control ◽  
Salient Feature ◽  
Retinal Eccentricity ◽  
V1 Neurons ◽  
Contrast Gain ◽  
Classical Receptive Field ◽  
Extrastriate Areas

Gain control is a salient feature of information processing throughout the visual system. Heeger (1991, 1992) described a mechanism that could underpin gain control in primary visual cortex (V1). According to this model, a neuron's response is normalized by dividing its output by the sum of a population of neurons, which are selective for orientations covering a broad range. Gain control in this scheme is manifested as a change in the semisaturation constant (contrast gain) of a V1 neuron. Here we examine how flanking and annular gratings of the same or orthogonal orientation to that preferred by a neuron presented beyond the receptive field modulate gain in V1 neurons in anesthetized marmosets (Callithrix jacchus). To characterize how gain was modulated by surround stimuli, the Michaelis–Menten equation was fitted to response versus contrast functions obtained under each stimulus condition. The modulation of gain by surround stimuli was modelled best as a divisive reduction in response gain. Response gain varied with the orientation of surround stimuli, but was reduced most when the orientation of a large annular grating beyond the classical receptive field matched the preferred orientation of neurons. The strength of surround suppression did not vary significantly with retinal eccentricity or laminar distribution. In the marmoset, as in macaques (Angelucci et al., 2002a, b), gain control over the sort of distances reported here (up to 10 deg) may be mediated by feedback from extrastriate areas.

scholarly journals Learning prediction error neurons in a canonical interneuron circuit

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Loreen Hertäg ◽  
Henning Sprekeler
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Prediction Error ◽  
Sensory Information ◽  
Prediction Errors ◽  
Negative Prediction ◽  
Inhibitory Plasticity ◽  
The Brain ◽  
Shed Light ◽  
Model Circuit

Sensory systems constantly compare external sensory information with internally generated predictions. While neural hallmarks of prediction errors have been found throughout the brain, the circuit-level mechanisms that underlie their computation are still largely unknown. Here, we show that a well-orchestrated interplay of three interneuron types shapes the development and refinement of negative prediction-error neurons in a computational model of mouse primary visual cortex. By balancing excitation and inhibition in multiple pathways, experience-dependent inhibitory plasticity can generate different variants of prediction-error circuits, which can be distinguished by simulated optogenetic experiments. The experience-dependence of the model circuit is consistent with that of negative prediction-error circuits in layer 2/3 of mouse primary visual cortex. Our model makes a range of testable predictions that may shed light on the circuitry underlying the neural computation of prediction errors.

scholarly journals Learning Overcomplete Representations

2000 ◽  
Vol 12 (2) ◽  
pp. 337-365 ◽  
Author(s):  
Michael S. Lewicki ◽  
Terrence J. Sejnowski
Keyword(s):  
Visual Cortex ◽  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Primary Visual Cortex ◽  
Probabilistic Model ◽  
Unique Combination ◽  
Coding Efficiency ◽  
Matching Structure ◽  
Basis Vectors ◽  
Overcomplete Representations

In an overcomplete basis, the number of basis vectors is greater than the dimensionality of the input, and the representation of an input is not a unique combination of basis vectors. Overcomplete representations have been advocated because they have greater robustness in the presence of noise, can be sparser, and can have greater flexibility in matching structure in the data. Overcomplete codes have also been proposed as a model of some of the response properties of neurons in primary visual cortex. Previous work has focused on finding the best representation of a signal using a fixed overcomplete basis (or dictionary). We present an algorithm for learning an overcomplete basis by viewing it as probabilistic model of the observed data. We show that overcomplete bases can yield a better approximation of the underlying statistical distribution of the data and can thus lead to greater coding efficiency. This can be viewed as a generalization of the technique of independent component analysis and provides a method for Bayesian reconstruction of signals in the presence of noise and for blind source separation when there are more sources than mixtures.

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