Synchronization of Neuronal Responses in Primary Visual Cortex of Monkeys Viewing Natural Images

2008 ◽  
Vol 100 (3) ◽  
pp. 1523-1532 ◽  
Author(s):  
Pedro Maldonado ◽  
Cecilia Babul ◽  
Wolf Singer ◽  
Eugenio Rodriguez ◽  
Denise Berger ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Eye Movements ◽  
Primary Visual Cortex ◽  
Discharge Rate ◽  
Spike Timing ◽  
Feature Binding ◽  
Natural Scenes ◽  
Event Analysis ◽  
Scene Segmentation ◽  
Discharge Rates

When inspecting visual scenes, primates perform on average four saccadic eye movements per second, which implies that scene segmentation, feature binding, and identification of image components is accomplished in <200 ms. Thus individual neurons can contribute only a small number of discharges for these complex computations, suggesting that information is encoded not only in the discharge rate but also in the timing of action potentials. While monkeys inspected natural scenes we registered, with multielectrodes from primary visual cortex, the discharges of simultaneously recorded neurons. Relating these signals to eye movements revealed that discharge rates peaked around 90 ms after fixation onset and then decreased to near baseline levels within 200 ms. Unitary event analysis revealed that preceding this increase in firing there was an episode of enhanced response synchronization during which discharges of spatially distributed cells coincided within 5-ms windows significantly more often than predicted by the discharge rates. This episode started 30 ms after fixation onset and ended by the time discharge rates had reached their maximum. When the animals scanned a blank screen a small change in firing rate, but no excess synchronization, was observed. The short latency of the stimulation-related synchronization phenomena suggests a fast-acting mechanism for the coordination of spike timing that may contribute to the basic operations of scene segmentation.

scholarly journals Postnatal Development of Onset Transient Responses in Macaque V1 and V2 Neurons

2008 ◽  
Vol 100 (3) ◽  
pp. 1476-1487 ◽  
Author(s):  
Bin Zhang ◽  
Earl L. Smith ◽  
Yuzo M. Chino
Keyword(s):  
Visual Cortex ◽  
Eye Movements ◽  
Primary Visual Cortex ◽  
Cortical Neurons ◽  
Newborn Infants ◽  
Neuronal Firing ◽  
Transient Responses ◽  
Visual Cortical ◽  
Better Than

Vision of newborn infants is limited by immaturities in their visual brain. In adult primates, the transient onset discharges of visual cortical neurons are thought to be intimately involved with capturing the rapid succession of brief images in visual scenes. Here we sought to determine the responsiveness and quality of transient responses in individual neurons of the primary visual cortex (V1) and visual area 2 (V2) of infant monkeys. We show that the transient component of neuronal firing to 640-ms stationary gratings was as robust and as reliable as in adults only 2 wk after birth, whereas the sustained component was more sluggish in infants than in adults. Thus the cortical circuitry supporting onset transient responses is functionally mature near birth, and our findings predict that neonates, known for their “impoverished vision,” are capable of initiating relatively mature fixating eye movements and of performing in detection of simple objects far better than traditionally thought.

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.

scholarly journals Local field potential phase and spike timing convey information about different visual features in primary visual cortex

2011 ◽  
Vol 12 (S1) ◽  
Author(s):  
Alberto Mazzoni ◽  
Christoph Kayser ◽  
Yusuke Murayama ◽  
Juan Martinez ◽  
Rodrigo Quian Quiroga ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Local Field ◽  
Local Field Potential ◽  
Field Potential ◽  
Spike Timing ◽  
Visual Features

scholarly journals An intrinsic neural eye tracker in primary visual cortex

2018 ◽  
Author(s):  
Adam P. Morris ◽  
Bart Krekelberg
Keyword(s):  
Visual Cortex ◽  
Eye Movements ◽  
Primary Visual Cortex ◽  
Visual Information ◽  
Moving Objects ◽  
Receptive Fields ◽  
Gaze Direction ◽  
Eye Tracker ◽  
Electrode Arrays ◽  
Full Field

SummaryHumans and other primates rely on eye movements to explore visual scenes and to track moving objects. As a result, the image that is projected onto the retina – and propagated throughout the visual cortical hierarchy – is almost constantly changing and makes little sense without taking into account the momentary direction of gaze. How is this achieved in the visual system? Here we show that in primary visual cortex (V1), the earliest stage of cortical vision, neural representations carry an embedded “eye tracker” that signals the direction of gaze associated with each image. Using chronically implanted multi-electrode arrays, we recorded the activity of neurons in V1 during tasks requiring fast (exploratory) and slow (pursuit) eye movements. Neurons were stimulated with flickering, full-field luminance noise at all times. As in previous studies 1-4, we observed neurons that were sensitive to gaze direction during fixation, despite comparable stimulation of their receptive fields. We trained a decoder to translate neural activity into metric estimates of (stationary) gaze direction. This decoded signal not only tracked the eye accurately during fixation, but also during fast and slow eye movements, even though the decoder had not been exposed to data from these behavioural states. Moreover, this signal lagged the real eye by approximately the time it took for new visual information to travel from the retina to cortex. Using simulations, we show that this V1 eye position signal could be used to take into account the sensory consequences of eye movements and map the fleeting positions of objects on the retina onto their stable position in the world.

scholarly journals Orientation tuning depends on spatial frequency in mouse visual cortex

2016 ◽  
Author(s):  
Inbal Ayzenshtat ◽  
Jesse Jackson ◽  
Rafael Yuste
Keyword(s):  
Visual Cortex ◽  
Spatial Frequency ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Receptive Fields ◽  
Orientation Selectivity ◽  
Natural Scenes ◽  
Response Properties ◽  
Layer 2 ◽  
Mouse Visual Cortex

AbstractThe response properties of neurons to sensory stimuli have been used to identify their receptive fields and functionally map sensory systems. In primary visual cortex, most neurons are selective to a particular orientation and spatial frequency of the visual stimulus. Using two-photon calcium imaging of neuronal populations from the primary visual cortex of mice, we have characterized the response properties of neurons to various orientations and spatial frequencies. Surprisingly, we found that the orientation selectivity of neurons actually depends on the spatial frequency of the stimulus. This dependence can be easily explained if one assumed spatially asymmetric Gabor-type receptive fields. We propose that receptive fields of neurons in layer 2/3 of visual cortex are indeed spatially asymmetric, and that this asymmetry could be used effectively by the visual system to encode natural scenes.Significance StatementIn this manuscript we demonstrate that the orientation selectivity of neurons in primary visual cortex of mouse is highly dependent on the stimulus SF. This dependence is realized quantitatively in a decrease in the selectivity strength of cells in non-optimum SF, and more importantly, it is also evident qualitatively in a shift in the preferred orientation of cells in non-optimum SF. We show that a receptive-field model of a 2D asymmetric Gabor, rather than a symmetric one, can explain this surprising observation. Therefore, we propose that the receptive fields of neurons in layer 2/3 of mouse visual cortex are spatially asymmetric and this asymmetry could be used effectively by the visual system to encode natural scenes.Highlights–Orientation selectivity is dependent on spatial frequency.–Asymmetric Gabor model can explain this dependence.

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 Specific excitatory connectivity for feature integration in mouse primary visual cortex

2016 ◽  
Author(s):  
Dylan R Muir ◽  
Patricia Molina-Luna ◽  
Morgane M Roth ◽  
Fritjof Helmchen ◽  
Björn M Kampa
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Processing ◽  
Visual Stimuli ◽  
Visual Representations ◽  
Feature Binding ◽  
Local Network ◽  
Visual Responses ◽  
Visual Properties ◽  
The Brain

AbstractLocal excitatory connections in mouse primary visual cortex (V1) are stronger and more prevalent between neurons that share similar functional response features. However, the details of how functional rules for local connectivity shape neuronal responses in V1 remain unknown. We hypothesised that complex responses to visual stimuli may arise as a consequence of rules for selective excitatory connectivity within the local network in the superficial layers of mouse V1. In mouse V1 many neurons respond to overlapping grating stimuli (plaid stimuli) with highly selective and facilitatory responses, which are not simply predicted by responses to single gratings presented alone. This complexity is surprising, since excitatory neurons in V1 are considered to be mainly tuned to single preferred orientations. Here we examined the consequences for visual processing of two alternative connectivity schemes: in the first case, local connections are aligned with visual properties inherited from feedforward input (a ‘like-to-like’ scheme specifically connecting neurons that share similar preferred orientations); in the second case, local connections group neurons into excitatory subnetworks that combine and amplify multiple feedforward visual properties (a ‘feature binding’ scheme). By comparing predictions from large scale computational models with in vivo recordings of visual representations in mouse V1, we found that responses to plaid stimuli were best explained by a assuming ‘feature binding’ connectivity. Unlike under the ‘like-to-like’ scheme, selective amplification within feature-binding excitatory subnetworks replicated experimentally observed facilitatory responses to plaid stimuli; explained selective plaid responses not predicted by grating selectivity; and was consistent with broad anatomical selectivity observed in mouse V1. Our results show that visual feature binding can occur through local recurrent mechanisms without requiring feedforward convergence, and that such a mechanism is consistent with visual responses and cortical anatomy in mouse V1.Author summaryThe brain is a highly complex structure, with abundant connectivity between nearby neurons in the neocortex, the outermost and evolutionarily most recent part of the brain. Although the network architecture of the neocortex can appear disordered, connections between neurons seem to follow certain rules. These rules most likely determine how information flows through the neural circuits of the brain, but the relationship between particular connectivity rules and the function of the cortical network is not known. We built models of visual cortex in the mouse, assuming distinct rules for connectivity, and examined how the various rules changed the way the models responded to visual stimuli. We also recorded responses to visual stimuli of populations of neurons in anaesthetised mice, and compared these responses with our model predictions. We found that connections in neocortex probably follow a connectivity rule that groups together neurons that differ in simple visual properties, to build more complex representations of visual stimuli. This finding is surprising because primary visual cortex is assumed to support mainly simple visual representations. We show that including specific rules for non-random connectivity in cortical models, and precisely measuring those rules in cortical tissue, is essential to understanding how information is processed by the brain.

Neural representation of the bottom-up saliency map of natural scenes in human primary visual cortex

2013 ◽  
Vol 13 (9) ◽  
pp. 233-233
Author(s):  
C. Chen ◽  
X. Zhang ◽  
T. Zhou ◽  
Y. Wang ◽  
F. Fang
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Saliency Map ◽  
Neural Representation ◽  
Natural Scenes ◽  
Bottom Up
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