Complex Visual Motion Representation in Mouse Area V1

Palagina, Ganna; Meyer, J.; Smirnakis, Stelios M.

doi:10.1523/jneurosci.0997-16.2017

Cited by 55 publications

(123 citation statements)

References 66 publications

(118 reference statements)

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“…Responses to the full set of plaid stimuli were highly selective; significantly more selective than predicted by a component model generated using all plaid and grating stimuli (Fig 2J; Therefore, probing visual cortex with a dense set of plaid stimuli reveals richer visual responses than when probed with a set of only 90˚plaids. Indeed, recent results suggest that using an expanded set of plaid stimuli evokes more pattern-cell responses in mouse V1 [31]. Consistent with this finding, our results show that using a dense set of plaids does not make responses to compound stimuli trivial to predict based on component responses.…”

Section: Responses To Plaid Stimuli Are Selective and Facilitatory Insupporting

confidence: 91%

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Specific excitatory connectivity for feature integration in mouse primary visual cortex

Muir

Molina-Luna

Roth

et al. 2016

Preprint

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Local 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 assuming feature binding connectivity. Unlike under the like-tolike 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 anesthetized 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 ...

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Section: Responses To Plaid Stimuli Are Selective and Facilitatory Insupporting

confidence: 91%

“…Recent reports have highlighted the facilitatory and selective nature of plaid responses in mouse V1 [29][30][31]. Many neurons in mouse V1 respond to plaid stimuli in accordance with a simple superimposition of their responses to the two underlying grating components (i.e.…”

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confidence: 99%

Specific excitatory connectivity for feature integration in mouse primary visual cortex

Muir

Molina-Luna

Roth

et al. 2016

Preprint

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“…The responses of LP cells were classified according to classical approach using partial correlations formula (Movshon et al, 1985;Casanova and Savard, 1996;Movshon and Newsome, 1996;Merabet et al, 1998;Palagina et al, 2017):…”

Section: Classification Of Pattern Motion and Component Motion Selectmentioning

confidence: 99%

Visual response characteristics in lateral and medial subdivisions of the rat pulvinar

Foik¹,

Scholl²,

Lean³

et al. 2020

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The pulvinar is a higher-order thalamic relay and a central component of the extrageniculate visual pathway, with input from the superior colliculus and visual cortex and output to all of visual cortex. Rodent pulvinar, more commonly called the lateral posterior nucleus (LP), consists of three highly-conserved subdivisions, and offers the advantage of simplicity in its study compared to more subdivided primate pulvinar. Little is known about receptive field properties of LP, let alone whether functional differences exist between different LP subdivisions, making it difficult to understand what visual information is relayed and what kinds of computations the pulvinar might support. Here, we characterized single-cell response properties in two V1 recipient subdivisions of rat pulvinar, the rostromedial (LPrm) and lateral (LPl), and found that a fourth of the cells were selective for orientation, compared to half in V1, and that LP tuning widths were significantly broader. Response latencies were also significantly longer and preferred size more than three times larger on average than in V1; the latter suggesting pulvinar as a source of spatial context to V1. Between subdivisons, LPl cells preferred higher temporal frequencies, whereas LPrm showed a greater degree of direction selectivity and pattern motion detection. Taken together with known differences in connectivity patterns, these results suggest two separate visual feature processing channels in the pulvinar, one in LPl related to higher speed processing which likely derives from superior colliculus input, and the other in LPrm for motion processing derived through input from visual cortex. Significance StatementThe pulvinar has a perplexing role in visual cognition as no clear link has been found between the functional properties of its neurons and behavioral deficits that arise when it is damaged. The pulvinar, called the lateral posterior nucleus (LP) in rats, is a higher order thalamic relay with input from the superior colliculus and visual cortex and output to all of visual cortex. By characterizing single-cell response properties in anatomically distinct subdivisions we found two separate visual feature processing channels in the pulvinar, one in lateral LP related to higher speed processing which likely derives from superior colliculus input, and the other in rostromedial LP for motion processing derived through input from visual cortex.

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“…In addition, strong orientation tuning and direction selectivity are already present in the lateral geniculate nucleus [33][34][35], in contrast to the weakly tuned LGN neurons found in primates [36]. Finally, in contrast to primate V1 [9,18], mouse V1 contains a significant fraction of neurons that exhibit tuned responses to global coherent motion found in RDKs [3] and plaid pattern motion ( [37,38], though see [39]).…”

Section: Introductionmentioning

confidence: 97%

Distributed and Retinotopically Asymmetric Processing of Coherent Motion in Mouse Visual Cortex

Sit¹,

Goard²

2019

Preprint

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Perception of visual motion is important for a range of ethological behaviors in mammals. In primates, specific higher visual cortical regions are specialized for processing of coherent visual motion. However, the distribution of motion processing among visual cortical areas in mice is unclear, despite the powerful genetic tools available for measuring population neural activity.Here, we used widefield and 2-photon calcium imaging of transgenic mice expressing a calcium indicator in excitatory neurons to measure mesoscale and cellular responses to coherent motion across the visual cortex. Imaging of primary visual cortex (V1) and several higher visual areas (HVAs) during presentation of natural movies and random dot kinematograms (RDKs) revealed heterogeneous responses to coherent motion. Although coherent motion responses were observed throughout visual cortex, particular HVAs in the putative dorsal stream (PM, AL, AM) exhibited stronger responses than ventral stream areas (LM and LI). Moreover, beyond the differences between visual areas, there was considerable heterogeneity within each visual area.Individual visual areas exhibited an asymmetry across the vertical retinotopic axis (visual elevation), such that neurons representing the inferior visual field exhibited greater responses to coherent motion. These results indicate that processing of visual motion in mouse cortex is distributed unevenly across visual areas and exhibits a spatial bias within areas, potentially to support processing of optic flow during spatial navigation.

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Complex Visual Motion Representation in Mouse Area V1

Cited by 55 publications

References 66 publications

Specific excitatory connectivity for feature integration in mouse primary visual cortex

Specific excitatory connectivity for feature integration in mouse primary visual cortex

Visual response characteristics in lateral and medial subdivisions of the rat pulvinar

Distributed and Retinotopically Asymmetric Processing of Coherent Motion in Mouse Visual Cortex

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