A survey of spiking activity reveals a functional hierarchy of mouse corticothalamic visual areas

Jh, Siegle; Jia, Xiaoxuan; Durand, Séverine; Gale, Sam; Bennett, Corbett; Graddis, Nile; Heller, Greggory; Tk, Ramirez; Choi, Hannah; Ja, Luviano; Pa, Groblewski; Ahmed, Ruweida; Arkhipov, Anton; Bernard, Amy; Billeh, Yazan N.; Brown, Dillan; Ma, Buice; Cain, Nicholas; Caldejon, Shiella; Casal, Linzy; A, Cho; Chvilicek, M; Tc, Cox; Dai, Kerong; Dj, Denman; Sej, de Vries; Dietzman, Roald; Esposito, Luke; Farrell, Colin; Feng, David; Galbraith, John; Garrett, Marina; Ec, Gelfand; Hancock, Nicole; Harris, Justin A.; Howard, Robert; Hu, Biao; Hytnen, Ross; Iyer, Ramakrishnan; Jessett, Erika; Johnson, Kenneth O.; Kato, India; Kiggins, Justin; Lambert, Sophie; Lecoq, Jérôme; Ledochowitsch, Peter; Jh, Lee; Leon, Arielle; Y, Li; Liang, Elizabeth; Long, Fuhui; Mace, Kyla; Melchior, Jose; Millman, Daniel; Mollenkopf, Tyler; Nayan, Chelsea; Ng, Leong L.; K, Ngo; Nguyen, Thuyanh V.; Pr, Nicovich; North, Kat; Gk, Ocker; Ollerenshaw, Doug; Oliver, Michael; Pachitariu, Marius; Perkins, Jed; Reding, Melissa; Reid, David; Robertson, Miranda; Ronellenfitch, Kara; Seid, Sam; Slaughterbeck, Cliff; Stoecklin, Michelle; Sullivan, David T.; Sutton, Ben; Swapp, Jackie; Thompson, Carol L.; Turner, Kristen M.; Wakeman, Wayne; Jd, Whitesell; Williams, Derric; Williford, Ali; Young, Rob; Zeng, Hongkui; Naylor, Sarah A.; Jw, Phillips; Rc, Reid; Mihalaş, Ştefan; Olsen,; Koch, Christof

doi:10.1101/805010

Cited by 74 publications

(123 citation statements)

References 92 publications

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“…We quantified this variability by the signal-to-noise ratio (SNR) (Figure 1f), and found a mean SNR of 0.11 for the example session, and 0.13 ± 0.01 (s.e.m., n=6) across recordings, similar to the single-trial SNR previously reported for natural image stimuli 38 . We also compared the single-neuron, single-trial signal variance from our two-photon datasets to that from electrophysiological recordings (0.11 vs 0.12) and found that there was little loss of information due to our recording method ( Figure S1a,b) 46 . The aligned, population-averaged tuning curves had a mean half-width at half-max of 14.1 • (Figure S1d ral response vectors for different orientations decayed smoothly as a function of the difference in orientation (Figure S1g,h).…”

Section: Resultsmentioning

confidence: 99%

High precision coding in visual cortex

Stringer

Michaelos

Pachitariu

2019

Preprint

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111

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Single neurons in visual cortex provide unreliable measurements of visual features due to their high trialto-trial variability. It is not known if this "noise" extends its effects over large neural populations to impair the global encoding of stimuli. We recorded simultaneously from ∼20,000 neurons in mouse primary visual cortex (V1) and found that the neural populations had discrimination thresholds of ∼0.34 • in an orientation decoding task. These thresholds were nearly 100 times smaller than those reported behaviorally in mice. The discrepancy between neural and behavioral discrimination could not be explained by the types of stimuli we used, by behavioral states or by the sequential nature of perceptual learning tasks. Furthermore, higherorder visual areas lateral to V1 could be decoded equally well. These results imply that the limits of sensory perception in mice are not set by neural noise in sensory cortex, but by the limitations of downstream decoders.

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Section: Resultsmentioning

confidence: 99%

High precision coding in visual cortex

Stringer

Michaelos

Pachitariu

2019

Preprint

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111

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“…Superior colliculus receptive fields in rat have been reported with diameters ranging from 10° to 60°. Larger receptive field sizes in LP neurons have also been reported in mice (Allen et al, 2016;Bennett et al, 2019;Siegle et al, 2019).…”

Section: Single Cell Response Characteristics In Rat Pulvinarmentioning

confidence: 64%

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

Foik¹,

Scholl²,

Lean³

et al. 2020

Preprint

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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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“…3F, 3G, and S3 respectively). For reference, data from in vivo extracellular Neuropixels recordings from awake mice within a standard pipeline (Siegle et al, 2019) are included in Fig. 3F (data available by the Allen Institute, brain-map.org/explore/circuits/visual-coding-neuropixels) and are used throughout the manuscript as a benchmark (examples in Fig.…”

Section: Creating and Testing Thalamocortical Connectivitymentioning

confidence: 99%

“…The latter variant can be run on a single CPU, making it broadly accessible to all neuroscientists. Both variants are constrained by experimental measurements and reproduce multiple observations from in-house Neuropixels high-density electrical recordings in vivo (Siegle et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…As an open public resource, these models will be useful for making direct predictions as well as complementing other experimental and modeling endeavors. The in vivo extracellular recordings used for comparison are recorded from a standardized pipeline that is freely available from the Allen Institute (Siegle et al, 2019;brain-map.org/explore/circuits/visual-codingneuropixels).…”

Section: Introductionmentioning

confidence: 99%

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Systematic Integration of Structural and Functional Data into Multi-Scale Models of Mouse Primary Visual Cortex

Billeh

Cai

Gratiy

et al. 2019

Preprint

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Structural rules underlying functional properties of cortical circuits are poorly understood. To explore these rules systematically, we integrated information from extensive literature curation and large-scale experimental surveys into a data-driven, biologically realistic model of the mouse primary visual cortex. The model was constructed at two levels of granularity, using either biophysically-detailed or pointneurons, with identical network connectivity. Both variants were compared to each other and to experimental recordings of neural activity during presentation of visual stimuli to awake mice. While constructing and tuning these networks to recapitulate experimental data, we identified a set of rules governing cell-class specific connectivity and synaptic strengths. These structural constraints constitute hypotheses that can be tested experimentally. Despite their distinct single cell abstraction, spatially extended or point-models, both perform similarly at the level of firing rate distributions. All data and models are freely available as a resource for the community.

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A survey of spiking activity reveals a functional hierarchy of mouse corticothalamic visual areas

Cited by 74 publications

References 92 publications

High precision coding in visual cortex

High precision coding in visual cortex

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

Systematic Integration of Structural and Functional Data into Multi-Scale Models of Mouse Primary Visual Cortex

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