Orientation Selectivity of Synaptic Input to Neurons in Mouse and Cat Primary Visual Cortex

Tan, Andrew Y. Y.; Brown, Brandon M.; Scholl, Benjamin; Mohanty, Deepankar; Priebe, Nicholas J.

doi:10.1523/jneurosci.2039-11.2011

Cited by 117 publications

(173 citation statements)

References 74 publications

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“…4g, h). These results suggest that local connections contribute to the stimulus selectivity of L2/3 neurons by providing tuned excitation that modulates the membrane potential in a manner qualitatively similar to that observed in vivo [19][20][21][22][23] .…”

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confidence: 56%

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Functional organization of excitatory synaptic strength in primary visual cortex

Cossell

Iacaruso

Muir

et al. 2015

Nature

506

670

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The strength of synaptic connections fundamentally determines how neurons influence each other's firing. Excitatory connection amplitudes between pairs of cortical neurons vary over two orders of magnitude, comprising only very few strong connections among many weaker ones [1][2][3][4][5][6][7][8][9] . Although this highly skewed distribution of connection strengths is observed in diverse cortical areas 1-9 , its functional significance remains unknown: it is not clear how connection strength relates to neuronal response properties, nor how strong and weak inputs contribute to information processing in local microcircuits. Here we reveal that the strength of connections between layer 2/3 (L2/3) pyramidal neurons in mouse primary visual cortex (V1) obeys a simple rule-the few strong connections occur between neurons with most correlated responses, while only weak connections link neurons with uncorrelated responses. Moreover, we show that strong and reciprocal connections occur between cells with similar spatial receptive field structure. Although weak connections far outnumber strong connections, each neuron receives the majority of its local excitation from a small number of strong inputs provided by the few neurons with similar responses to visual features. By dominating recurrent excitation, these infrequent yet powerful inputs disproportionately contribute to feature preference and selectivity. Therefore, our results show that the apparently complex organization of excitatory connection strength reflects the Reprints and permissions information is available at www.nature.com/reprints.

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supporting

confidence: 56%

“…In mouse V1, a simple cell's subthreshold response to drifting grating stimuli [19][20][21][22][23] is characterized by two components that are determined by the angle and phase of the grating in relation to its RF (Fig. 4a).…”

mentioning

confidence: 99%

Functional organization of excitatory synaptic strength in primary visual cortex

Cossell

Iacaruso

Muir

et al. 2015

Nature

506

670

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“…Physiological procedures for mouse recordings were based on those previously described (Tan et al 2011). All our experiments were conducted with adult C57BL/6 mice (age 5-8 wk).…”

Section: Methodsmentioning

confidence: 99%

Binocular integration and disparity selectivity in mouse primary visual cortex

Scholl

Burge

Priebe

2013

Journal of Neurophysiology

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Scholl B, Burge J, Priebe NJ. Binocular integration and disparity selectivity in mouse primary visual cortex. J Neurophysiol 109: 3013-3024, 2013. First published March 20, 2013 doi:10.1152/jn.01021.2012.-Signals from the two eyes are first integrated in primary visual cortex (V1). In many mammals, this binocular integration is an important first step in the development of stereopsis, the perception of depth from disparity. Neurons in the binocular zone of mouse V1 receive inputs from both eyes, but it is unclear how that binocular information is integrated and whether this integration has a function similar to that found in other mammals. Using extracellular recordings, we demonstrate that mouse V1 neurons are tuned for binocular disparities, or spatial differences, between the inputs from each eye, thus extracting signals potentially useful for estimating depth. The disparities encoded by mouse V1 are significantly larger than those encoded by cat and primate. Interestingly, these larger disparities correspond to distances that are likely to be ecologically relevant in natural viewing, given the stereo-geometry of the mouse visual system. Across mammalian species, it appears that binocular integration is a common cortical computation used to extract information relevant for estimating depth. As such, it is a prime example of how the integration of multiple sensory signals is used to generate accurate estimates of properties in our environment. primary visual cortex; mouse binocularity; disparity tuning; ocular dominance TO ENABLE ACCURATE ESTIMATES of behaviorally relevant properties of the environment, sensory systems integrate information from multiple sources. For example, in the visual system, signals from the left and right eyes are integrated to provide information about depth. In mammals, left and right eye signals first converge in primary visual cortex (V1). The different vantage points of the eyes create local spatial offsets in the retinal images, offsets known as binocular disparities. Binocular disparity changes with the depths of objects in the environment. Neurons that encode retinal image information relevant for estimating binocular disparity therefore provide information relevant for binocular depth perception (Barlow et al. 1967;Blakemore 1969;Hubel and Wiesel 1973;Joshua 1970;Nikara et al. 1968;Pettigrew et al. 1968). Binocular disparity selectivity can be observed in individual neurons; some binocular stimuli elicit large increases in responses, while others reduce responses, relative to monocular stimulation alone (Hubel and Wiesel 1962;Ohzawa and Freeman 1986;Pettigrew et al. 1968).Disparity-selective binocular neurons have been reported in many animals, including carnivores and primates. Such neurons have not, however, been reported in rodents. In recent years, mice have become an increasingly important model for the study of visual processing and cortical plasticity. Genetic techniques are now available to dissect underlying circuitry and its emergence during development. Here, we rep...

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“…However, sharp selectivity is also observed in species (e.g., rat, mouse, or squirrel) whose V1 has no orientation map and neurons with very different POs are intermixed (Dräger, 1975;Bousfield, 1977;Métin et al, 1988;Girman et al, 1999;Ohki et al, 2005;Van Hooser et al, 2005;Niell and Stryker, 2010;Runyan et al, 2010;Bonin et al, 2011;Tan et al, 2011). How can neurons be sharply tuned to orientation if V1 has such a salt-and-pepper organization?…”

Section: Introductionmentioning

confidence: 99%

The Mechanism of Orientation Selectivity in Primary Visual Cortex without a Functional Map

Hansel

Vreeswijk²

2012

J. Neurosci.

130

139

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Neurons in primary visual cortex (V1) display substantial orientation selectivity even in species where V1 lacks an orientation map, such as in mice and rats. The mechanism underlying orientation selectivity in V1 with such a salt-and-pepper organization is unknown; it is unclear whether a connectivity that depends on feature similarity is required, or a random connectivity suffices. Here we argue for the latter. We study the response to a drifting grating of a network model of layer 2/3 with random recurrent connectivity and feedforward input from layer 4 neurons with random preferred orientations. We show that even though the total feedforward and total recurrent excitatory and inhibitory inputs all have a very weak orientation selectivity, strong selectivity emerges in the neuronal spike responses if the network operates in the balanced excitation/inhibition regime. This is because in this regime the (large) untuned components in the excitatory and inhibitory contributions approximately cancel. As a result the untuned part of the input into a neuron as well as its modulation with orientation and time all have a size comparable to the neuronal threshold. However, the tuning of the F0 and F1 components of the input are uncorrelated and the high-frequency fluctuations are not tuned. This is reflected in the subthreshold voltage response. Remarkably, due to the nonlinear voltage-firing rate transfer function, the preferred orientation of the F0 and F1 components of the spike response are highly correlated.

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Orientation Selectivity of Synaptic Input to Neurons in Mouse and Cat Primary Visual Cortex

Cited by 117 publications

References 74 publications

Functional organization of excitatory synaptic strength in primary visual cortex

Functional organization of excitatory synaptic strength in primary visual cortex

Binocular integration and disparity selectivity in mouse primary visual cortex

The Mechanism of Orientation Selectivity in Primary Visual Cortex without a Functional Map

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