scholarly journals Different temporal dynamics of topological and projective geometrical perceptions in primary visual cortex: a TMS study

2011 ◽  
Vol 11 (11) ◽  
pp. 863-863 ◽  
Author(s):  
X. Du ◽  
K. Zhou ◽  
L. Chen
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Temporal Dynamics

scholarly journals Temporal dynamics of binocular integration in primary visual cortex

2019 ◽  
Vol 19 (12) ◽  
pp. 13 ◽  
Author(s):  
Michele A. Cox ◽  
Kacie Dougherty ◽  
Jacob A. Westerberg ◽  
Michelle S. Schall ◽  
Alexander Maier
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Temporal Dynamics

scholarly journals Spatial integration in mouse primary visual cortex

2013 ◽  
Vol 110 (4) ◽  
pp. 964-972 ◽  
Author(s):  
Agne Vaiceliunaite ◽  
Sinem Erisken ◽  
Florian Franzen ◽  
Steffen Katzner ◽  
Laura Busse
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Neural Circuits ◽  
Temporal Dynamics ◽  
Visual Saliency ◽  
Brain State ◽  
Inhibitory Interneurons ◽  
Spatial Integration ◽  
V1 Neurons ◽  
Contrast Normalization

Responses of many neurons in primary visual cortex (V1) are suppressed by stimuli exceeding the classical receptive field (RF), an important property that might underlie the computation of visual saliency. Traditionally, it has proven difficult to disentangle the underlying neural circuits, including feedforward, horizontal intracortical, and feedback connectivity. Since circuit-level analysis is particularly feasible in the mouse, we asked whether neural signatures of spatial integration in mouse V1 are similar to those of higher-order mammals and investigated the role of parvalbumin-expressing (PV+) inhibitory interneurons. Analogous to what is known from primates and carnivores, we demonstrate that, in awake mice, surround suppression is present in the majority of V1 neurons and is strongest in superficial cortical layers. Anesthesia with isoflurane-urethane, however, profoundly affects spatial integration: it reduces the laminar dependency, decreases overall suppression strength, and alters the temporal dynamics of responses. We show that these effects of brain state can be parsimoniously explained by assuming that anesthesia affects contrast normalization. Hence, the full impact of suppressive influences in mouse V1 cannot be studied under anesthesia with isoflurane-urethane. To assess the neural circuits of spatial integration, we targeted PV+ interneurons using optogenetics. Optogenetic depolarization of PV+ interneurons was associated with increased RF size and decreased suppression in the recorded population, similar to effects of lowering stimulus contrast, suggesting that PV+ interneurons contribute to spatial integration by affecting overall stimulus drive. We conclude that the mouse is a promising model for circuit-level mechanisms of spatial integration, which relies on the combined activity of different types of inhibitory interneurons.

scholarly journals Visual motion computation in recurrent neural networks

2017 ◽  
Author(s):  
Marius Pachitariu ◽  
Maneesh Sahani
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Information ◽  
Visual Motion ◽  
Hebbian Learning ◽  
Temporal Dynamics ◽  
Temporal Structure ◽  
Receptive Fields ◽  
Learning Rule ◽  
Connectivity Matrix

AbstractPopulations of neurons in primary visual cortex (V1) transform direct thalamic inputs into a cortical representation which acquires new spatio-temporal properties. One of these properties, motion selectivity, has not been strongly tied to putative neural mechanisms, and its origins remain poorly understood. Here we propose that motion selectivity is acquired through the recurrent mechanisms of a network of strongly connected neurons. We first show that a bank of V1 spatiotemporal receptive fields can be generated accurately by a network which receives only instantaneous inputs from the retina. The temporal structure of the receptive fields is generated by the long timescale dynamics associated with the high magnitude eigenvalues of the recurrent connectivity matrix. When these eigenvalues have complex parts, they generate receptive fields that are inseparable in time and space, such as those tuned to motion direction. We also show that the recurrent connectivity patterns can be learnt directly from the statistics of natural movies using a temporally-asymmetric Hebbian learning rule. Probed with drifting grating stimuli and moving bars, neurons in the model show patterns of responses analogous to those of direction-selective simple cells in primary visual cortex. These computations are enabled by a specific pattern of recurrent connections, that can be tested by combining connectome reconstructions with functional recordings.*Author summaryDynamic visual scenes provide our eyes with enormous quantities of visual information, particularly when the visual scene changes rapidly. Even at modest moving speeds, individual small objects quickly change their location causing single points in the scene to change their luminance equally fast. Furthermore, our own movements through the world add to the velocities of objects relative to our retinas, further increasing the speed at which visual inputs change. How can a biological system process efficiently such vast amounts of information, while keeping track of objects in the scene? Here we formulate and analyze a solution that is enabled by the temporal dynamics of networks of neurons.

Temporal dynamics of chromatic tuning in macaque primary visual cortex

Nature ◽  
10.1038/27666 ◽  
1998 ◽  
Vol 395 (6705) ◽  
pp. 896-900 ◽  
Author(s):  
Nicolas P. Cottaris ◽  
Russell L. De Valois
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Temporal Dynamics

scholarly journals Binocular Integration Manifests as a Transient Spiking Increase Followed by Selective Suppression in Primary Visual Cortex

2018 ◽  
Author(s):  
Michele A. Cox ◽  
Kacie Dougherty ◽  
Jacob A. Westerberg ◽  
Michelle S. Schall ◽  
Alexander Maier
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Information ◽  
Temporal Dynamics ◽  
Neuronal Population ◽  
Stimulus Onset ◽  
Monocular Stimulation ◽  
Binocular Stimulation ◽  
Behaving Monkeys ◽  
Open Question

AbstractResearch throughout the past decades revealed that neurons in primate primary visual cortex (V1) rapidly integrate the two eyes’ separate signals into a combined binocular response. The exact mechanisms giving underlying this binocular integration remain elusive. One open question is whether binocular integration occurs at a single stage of sensory processing or in a sequence of computational steps. To address this question, we examined the temporal dynamics of binocular integration across V1’s laminar microcircuit of awake behaving monkeys. We find that V1 processes binocular stimuli in a dynamic sequence that comprises at least two distinct phases: A transient phase, lasting 50-150ms from stimulus onset, in which neuronal population responses are significantly enhanced for binocular stimulation compared to monocular stimulation, followed by a sustained phase characterized by widespread suppression in which feature-specific computations emerge. In the sustained phase, incongruent binocular stimulation resulted in response reduction relative to monocular stimulation across the V1 population. By contrast, sustained responses for binocular congruent stimulation were either reduced or enhanced relative to monocular responses depending on the neurons’ selectivity for one or both eyes (i.e., ocularity). These results suggest that binocular integration in V1 occurs in at least two sequential steps, with an initial additive combination of the two eyes’ signals followed by the establishment of interocular concordance and discordance.Significance StatementOur two eyes provide two separate streams of visual information that are merged in the primary visual cortex (V1). Previous work showed that stimulating both eyes rather than one eye may either increase or decrease activity in V1, depending on the nature of the stimuli. Here we show that V1 binocular responses change over time, with an early phase of general excitation and followed by stimulus-dependent response suppression. These results provide important new insights into the neural machinery that supports the combination of the two eye’s perspectives into a single coherent view.

Figure–Ground Representation and Its Decay in Primary Visual Cortex

2012 ◽  
Vol 24 (4) ◽  
pp. 905-914 ◽  
Author(s):  
Lars Strother ◽  
Cheryl Lavell ◽  
Tutis Vilis
Keyword(s):  
Visual Cortex ◽  
Spatial Attention ◽  
Primary Visual Cortex ◽  
Sustained Attention ◽  
Temporal Dynamics ◽  
Background Suppression ◽  
Perceptual Memory ◽  
Cluttered Background ◽  
Static Background

We used fMRI to study figure–ground representation and its decay in primary visual cortex (V1). Human observers viewed a motion-defined figure that gradually became camouflaged by a cluttered background after it stopped moving. V1 showed positive fMRI responses corresponding to the moving figure and negative fMRI responses corresponding to the static background. This positive–negative delineation of V1 “figure” and “background” fMRI responses defined a retinotopically organized figure–ground representation that persisted after the figure stopped moving but eventually decayed. The temporal dynamics of V1 “figure” and “background” fMRI responses differed substantially. Positive “figure” responses continued to increase for several seconds after the figure stopped moving and remained elevated after the figure had disappeared. We propose that the sustained positive V1 “figure” fMRI responses reflected both persistent figure–ground representation and sustained attention to the location of the figure after its disappearance, as did subjects' reports of persistence. The decreasing “background” fMRI responses were relatively shorter-lived and less biased by spatial attention. Our results show that the transition from a vivid figure–ground percept to its disappearance corresponds to the concurrent decay of figure enhancement and background suppression in V1, both of which play a role in form-based perceptual memory.

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