scholarly journals Variations in photoreceptor throughput to mouse visual cortex and the unique effects on tuning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
I. Rhim ◽  
G. Coello-Reyes ◽  
I. Nauhaus
Keyword(s):  
Visual Cortex ◽  
Light Adaptation ◽  
Frequency Tuning ◽  
Receptive Fields ◽  
Stimulus Dimension ◽  
In Vivo Studies ◽  
Cone Opsin ◽  
Spatio Temporal ◽  
Mouse Visual Cortex

AbstractVisual input to primary visual cortex (V1) depends on highly adaptive filtering in the retina. In turn, isolation of V1 computations requires experimental control of retinal adaptation to infer its spatio-temporal-chromatic output. Here, we measure the balance of input to mouse V1, in the anesthetized setup, from the three main photoreceptor opsins—M-opsin, S-opsin, and rhodopsin—as a function of two stimulus dimensions. The first dimension is the level of light adaptation within the mesopic range, which governs the balance of rod and cone inputs to cortex. The second stimulus dimension is retinotopic position, which governs the balance of S- and M-cone opsin input due to the opsin expression gradient in the retina. The fitted model predicts opsin input under arbitrary lighting environments, which provides a much-needed handle on in-vivo studies of the mouse visual system. We use it here to reveal that V1 is rod-mediated in common laboratory settings yet cone-mediated in natural daylight. Next, we compare functional properties of V1 under rod and cone-mediated inputs. The results show that cone-mediated V1 responds to 2.5-fold higher temporal frequencies than rod-mediated V1. Furthermore, cone-mediated V1 has smaller receptive fields, yet similar spatial frequency tuning. V1 responses in rod-deficient (Gnat1−/−) mice confirm that the effects are due to differences in photoreceptor opsin contribution.

scholarly journals Variations in photoreceptor throughput to mouse visual cortex and the unique effects on tuning

2020 ◽  
Author(s):  
I. Rhim ◽  
G. Coello-Reyes ◽  
I. Nauhaus
Keyword(s):  
Visual Cortex ◽  
Visual Field ◽  
Adaptive Filtering ◽  
Frequency Tuning ◽  
Cortical Circuits ◽  
Spatial Frequency Tuning ◽  
Upper Visual Field ◽  
Spatio Temporal ◽  
Mouse Visual Cortex ◽  
Common Laboratory

ABSTRACTVisual input to primary visual cortex (V1) depends on highly adaptive filtering in the retina. In turn, isolation of V1 computations to study cortical circuits requires control over retinal adaption and its corresponding spatio-temporal-chromatic output. Here, we first measure the balance of input to V1 from the three main photoreceptor opsins – M-opsin, S-opsin, and rhodopsin – as a function of light adaption and retinotopy. Results show that V1 is rod-mediated in common laboratory settings, yet cone-mediated in natural daylight, as evidenced by exclusive sensitivity to UV wavelengths via cone S-opsin in the upper visual field. Next, we show that cone-mediated V1 responds to 2.5-fold higher temporal frequencies than rod-mediated V1. Furthermore, cone-mediated V1 has smaller RFs, yet similar spatial frequency tuning. V1 responses in rod-deficient (Gnat1−/−) mice confirm that the effects are due to differences in photoreceptor contribution. This study provides foundation for using mouse V1 to study cortical circuits.

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.

scholarly journals Emergent orientation selectivity from random networks in mouse visual cortex

2018 ◽  
Author(s):  
J.J. Pattadkal ◽  
G. Mato ◽  
C. van Vreeswijk ◽  
N. J. Priebe ◽  
D. Hansel
Keyword(s):  
Visual Cortex ◽  
Calcium Imaging ◽  
Receptive Fields ◽  
Orientation Selectivity ◽  
Random Networks ◽  
Two Dimensional ◽  
V1 Neurons ◽  
Mouse Visual Cortex ◽  
Whole Cell Recordings ◽  
Random Connectivity

SummaryWe study the connectivity principles underlying the emergence of orientation selectivity in primary visual cortex (V1) of mammals lacking an orientation map. We present a computational model in which random connectivity gives rise to orientation selectivity that matches experimental observations. It predicts that mouse V1 neurons should exhibit intricate receptive fields in the two-dimensional frequency domain, causing shift in orientation preferences with spatial frequency. We find evidence for these features in mouse V1 using calcium imaging and intracellular whole cell recordings.

scholarly journals In vivo STED microscopy visualizes PSD95 sub-structures and morphological changes over several hours in the mouse visual cortex

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Waja Wegner ◽  
Alexander C. Mott ◽  
Seth G. N. Grant ◽  
Heinz Steffens ◽  
Katrin I. Willig
Keyword(s):  
Visual Cortex ◽  
Morphological Changes ◽  
Sted Microscopy ◽  
Mouse Visual Cortex

scholarly journals A neural circuit for gamma-band coherence across the retinotopic map in mouse visual cortex

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Richard Hakim ◽  
Kiarash Shamardani ◽  
Hillel Adesnik
Keyword(s):  
Visual Cortex ◽  
Neural Circuit ◽  
Brain Slices ◽  
Pyramidal Cells ◽  
Gamma Oscillations ◽  
Gamma Band ◽  
Neuron Activity ◽  
Mouse Visual Cortex ◽  
Retinotopic Map

Cortical gamma oscillations have been implicated in a variety of cognitive, behavioral, and circuit-level phenomena. However, the circuit mechanisms of gamma-band generation and synchronization across cortical space remain uncertain. Using optogenetic patterned illumination in acute brain slices of mouse visual cortex, we define a circuit composed of layer 2/3 (L2/3) pyramidal cells and somatostatin (SOM) interneurons that phase-locks ensembles across the retinotopic map. The network oscillations generated here emerge from non-periodic stimuli, and are stimulus size-dependent, coherent across cortical space, narrow band (30 Hz), and depend on SOM neuron but not parvalbumin (PV) neuron activity; similar to visually induced gamma oscillations observed in vivo. Gamma oscillations generated in separate cortical locations exhibited high coherence as far apart as 850 μm, and lateral gamma entrainment depended on SOM neuron activity. These data identify a circuit that is sufficient to mediate long-range gamma-band coherence in the primary visual cortex.

scholarly journals Sensory-driven and spontaneous gamma oscillations engage distinct cortical circuitry

2016 ◽  
Vol 115 (4) ◽  
pp. 1821-1835 ◽  
Author(s):  
Cristin G. Welle ◽  
Diego Contreras
Keyword(s):  
Visual Cortex ◽  
Visual Stimulation ◽  
Oscillation Amplitude ◽  
Gamma Oscillations ◽  
Gamma Oscillation ◽  
Inhibitory Neurons ◽  
Sensory Representation ◽  
Cortical Circuitry ◽  
Mouse Visual Cortex

Gamma oscillations are a robust component of sensory responses but are also part of the background spontaneous activity of the brain. To determine whether the properties of gamma oscillations in cortex are specific to their mechanism of generation, we compared in mouse visual cortex in vivo the laminar geometry and single-neuron rhythmicity of oscillations produced during sensory representation with those occurring spontaneously in the absence of stimulation. In mouse visual cortex under anesthesia (isoflurane and xylazine), visual stimulation triggered oscillations mainly between 20 and 50 Hz, which, because of their similar functional significance to gamma oscillations in higher mammals, we define here as gamma range. Sensory representation in visual cortex specifically increased gamma oscillation amplitude in the supragranular (L2/3) and granular (L4) layers and strongly entrained putative excitatory and inhibitory neurons in infragranular layers, while spontaneous gamma oscillations were distributed evenly through the cortical depth and primarily entrained putative inhibitory neurons in the infragranular (L5/6) cortical layers. The difference in laminar distribution of gamma oscillations during the two different conditions may result from differences in the source of excitatory input to the cortex. In addition, modulation of superficial gamma oscillation amplitude did not result in a corresponding change in deep-layer oscillations, suggesting that superficial and deep layers of cortex may utilize independent but related networks for gamma generation. These results demonstrate that stimulus-driven gamma oscillations engage cortical circuitry in a manner distinct from spontaneous oscillations and suggest multiple networks for the generation of gamma oscillations in cortex.

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