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Shin H, Ogando MB, Abdeladim L, Durand S, Belski H, Cabasco H, Loefler H, Bawany A, Hardcastle B, Wilkes J, Nguyen K, Suarez L, Johnson T, Han W, Ouellette B, Grasso C, Swapp J, Ha V, Young A, Caldejon S, Williford A, Groblewski P, Olsen S, Kiselycznyk C, Lecoq J, Adesnik H. Recurrent pattern completion drives the neocortical representation of sensory inference. bioRxiv 2023:2023.06.05.543698. [PMID: 37333175 PMCID: PMC10274729 DOI: 10.1101/2023.06.05.543698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
When sensory information is incomplete or ambiguous, the brain relies on prior expectations to infer perceptual objects. Despite the centrality of this process to perception, the neural mechanism of sensory inference is not known. Illusory contours (ICs) are key tools to study sensory inference because they contain edges or objects that are implied only by their spatial context. Using cellular resolution, mesoscale two-photon calcium imaging and multi-Neuropixels recordings in the mouse visual cortex, we identified a sparse subset of neurons in the primary visual cortex (V1) and higher visual areas that respond emergently to ICs. We found that these highly selective 'IC-encoders' mediate the neural representation of IC inference. Strikingly, selective activation of these neurons using two-photon holographic optogenetics was sufficient to recreate IC representation in the rest of the V1 network, in the absence of any visual stimulus. This outlines a model in which primary sensory cortex facilitates sensory inference by selectively strengthening input patterns that match prior expectations through local, recurrent circuitry. Our data thus suggest a clear computational purpose for recurrence in the generation of holistic percepts under sensory ambiguity. More generally, selective reinforcement of top-down predictions by pattern-completing recurrent circuits in lower sensory cortices may constitute a key step in sensory inference.
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Affiliation(s)
- Hyeyoung Shin
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
- Present Address: School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Mora B Ogando
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Lamiae Abdeladim
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | | | - Hannah Belski
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | | | - Henry Loefler
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | - Ahad Bawany
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | | | - Josh Wilkes
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | | | - Lucas Suarez
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | - Tye Johnson
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | - Warren Han
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | - Ben Ouellette
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | - Conor Grasso
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | - Jackie Swapp
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | - Vivian Ha
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | - Ahrial Young
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | | | - Ali Williford
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | | | - Shawn Olsen
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | | | - Jerome Lecoq
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | - Hillel Adesnik
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
- The Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
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2
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Yao S, Wang Q, Hirokawa KE, Ouellette B, Ahmed R, Bomben J, Brouner K, Casal L, Caldejon S, Cho A, Dotson NI, Daigle TL, Egdorf T, Enstrom R, Gary A, Gelfand E, Gorham M, Griffin F, Gu H, Hancock N, Howard R, Kuan L, Lambert S, Lee EK, Luviano J, Mace K, Maxwell M, Mortrud MT, Naeemi M, Nayan C, Ngo NK, Nguyen T, North K, Ransford S, Ruiz A, Seid S, Swapp J, Taormina MJ, Wakeman W, Zhou T, Nicovich PR, Williford A, Potekhina L, McGraw M, Ng L, Groblewski PA, Tasic B, Mihalas S, Harris JA, Cetin A, Zeng H. A whole-brain monosynaptic input connectome to neuron classes in mouse visual cortex. Nat Neurosci 2023; 26:350-364. [PMID: 36550293 PMCID: PMC10039800 DOI: 10.1038/s41593-022-01219-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 10/27/2022] [Indexed: 12/24/2022]
Abstract
Identification of structural connections between neurons is a prerequisite to understanding brain function. Here we developed a pipeline to systematically map brain-wide monosynaptic input connections to genetically defined neuronal populations using an optimized rabies tracing system. We used mouse visual cortex as the exemplar system and revealed quantitative target-specific, layer-specific and cell-class-specific differences in its presynaptic connectomes. The retrograde connectivity indicates the presence of ventral and dorsal visual streams and further reveals topographically organized and continuously varying subnetworks mediated by different higher visual areas. The visual cortex hierarchy can be derived from intracortical feedforward and feedback pathways mediated by upper-layer and lower-layer input neurons. We also identify a new role for layer 6 neurons in mediating reciprocal interhemispheric connections. This study expands our knowledge of the visual system connectomes and demonstrates that the pipeline can be scaled up to dissect connectivity of different cell populations across the mouse brain.
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Affiliation(s)
- Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA, USA.
| | - Quanxin Wang
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Karla E Hirokawa
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | | | | | | | | | - Linzy Casal
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Andy Cho
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Tom Egdorf
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Amanda Gary
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Hong Gu
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Leonard Kuan
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Kyla Mace
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | | | - Kat North
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | | | | | - Sam Seid
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jackie Swapp
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Thomas Zhou
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Philip R Nicovich
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | | | | | - Medea McGraw
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | - Ali Cetin
- Allen Institute for Brain Science, Seattle, WA, USA
- CNC Program, Stanford University, Palo Alto, CA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA.
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3
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Siegle JH, Ledochowitsch P, Jia X, Millman DJ, Ocker GK, Caldejon S, Casal L, Cho A, Denman DJ, Durand S, Groblewski PA, Heller G, Kato I, Kivikas S, Lecoq J, Nayan C, Ngo K, Nicovich PR, North K, Ramirez TK, Swapp J, Waughman X, Williford A, Olsen SR, Koch C, Buice MA, de Vries SEJ. Reconciling functional differences in populations of neurons recorded with two-photon imaging and electrophysiology. eLife 2021; 10:e69068. [PMID: 34270411 PMCID: PMC8285106 DOI: 10.7554/elife.69068] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 07/02/2021] [Indexed: 11/20/2022] Open
Abstract
Extracellular electrophysiology and two-photon calcium imaging are widely used methods for measuring physiological activity with single-cell resolution across large populations of cortical neurons. While each of these two modalities has distinct advantages and disadvantages, neither provides complete, unbiased information about the underlying neural population. Here, we compare evoked responses in visual cortex recorded in awake mice under highly standardized conditions using either imaging of genetically expressed GCaMP6f or electrophysiology with silicon probes. Across all stimulus conditions tested, we observe a larger fraction of responsive neurons in electrophysiology and higher stimulus selectivity in calcium imaging, which was partially reconciled by applying a spikes-to-calcium forward model to the electrophysiology data. However, the forward model could only reconcile differences in responsiveness when restricted to neurons with low contamination and an event rate above a minimum threshold. This work established how the biases of these two modalities impact functional metrics that are fundamental for characterizing sensory-evoked responses.
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Affiliation(s)
| | | | - Xiaoxuan Jia
- MindScope Program, Allen InstituteSeattleUnited States
| | | | | | | | - Linzy Casal
- MindScope Program, Allen InstituteSeattleUnited States
| | - Andy Cho
- MindScope Program, Allen InstituteSeattleUnited States
| | - Daniel J Denman
- Allen Institute for Brain Science, Allen InstituteSeattleUnited States
| | | | | | - Gregg Heller
- MindScope Program, Allen InstituteSeattleUnited States
| | - India Kato
- MindScope Program, Allen InstituteSeattleUnited States
| | - Sara Kivikas
- MindScope Program, Allen InstituteSeattleUnited States
| | - Jérôme Lecoq
- MindScope Program, Allen InstituteSeattleUnited States
| | - Chelsea Nayan
- MindScope Program, Allen InstituteSeattleUnited States
| | - Kiet Ngo
- Allen Institute for Brain Science, Allen InstituteSeattleUnited States
| | - Philip R Nicovich
- Allen Institute for Brain Science, Allen InstituteSeattleUnited States
| | - Kat North
- MindScope Program, Allen InstituteSeattleUnited States
| | | | - Jackie Swapp
- MindScope Program, Allen InstituteSeattleUnited States
| | - Xana Waughman
- MindScope Program, Allen InstituteSeattleUnited States
| | - Ali Williford
- MindScope Program, Allen InstituteSeattleUnited States
| | - Shawn R Olsen
- MindScope Program, Allen InstituteSeattleUnited States
| | - Christof Koch
- MindScope Program, Allen InstituteSeattleUnited States
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Siegle JH, Jia X, Durand S, Gale S, Bennett C, Graddis N, Heller G, Ramirez TK, Choi H, Luviano JA, Groblewski PA, Ahmed R, Arkhipov A, Bernard A, Billeh YN, Brown D, Buice MA, Cain N, Caldejon S, Casal L, Cho A, Chvilicek M, Cox TC, Dai K, Denman DJ, de Vries SEJ, Dietzman R, Esposito L, Farrell C, Feng D, Galbraith J, Garrett M, Gelfand EC, Hancock N, Harris JA, Howard R, Hu B, Hytnen R, Iyer R, Jessett E, Johnson K, Kato I, Kiggins J, Lambert S, Lecoq J, Ledochowitsch P, Lee JH, Leon A, Li Y, Liang E, Long F, Mace K, Melchior J, Millman D, Mollenkopf T, Nayan C, Ng L, Ngo K, Nguyen T, Nicovich PR, North K, Ocker GK, Ollerenshaw D, Oliver M, Pachitariu M, Perkins J, Reding M, Reid D, Robertson M, Ronellenfitch K, Seid S, Slaughterbeck C, Stoecklin M, Sullivan D, Sutton B, Swapp J, Thompson C, Turner K, Wakeman W, Whitesell JD, Williams D, Williford A, Young R, Zeng H, Naylor S, Phillips JW, Reid RC, Mihalas S, Olsen SR, Koch C. Survey of spiking in the mouse visual system reveals functional hierarchy. Nature 2021; 592:86-92. [PMID: 33473216 PMCID: PMC10399640 DOI: 10.1038/s41586-020-03171-x] [Citation(s) in RCA: 148] [Impact Index Per Article: 49.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 12/09/2020] [Indexed: 12/14/2022]
Abstract
The anatomy of the mammalian visual system, from the retina to the neocortex, is organized hierarchically1. However, direct observation of cellular-level functional interactions across this hierarchy is lacking due to the challenge of simultaneously recording activity across numerous regions. Here we describe a large, open dataset-part of the Allen Brain Observatory2-that surveys spiking from tens of thousands of units in six cortical and two thalamic regions in the brains of mice responding to a battery of visual stimuli. Using cross-correlation analysis, we reveal that the organization of inter-area functional connectivity during visual stimulation mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas3. We find that four classical hierarchical measures-response latency, receptive-field size, phase-locking to drifting gratings and response decay timescale-are all correlated with the hierarchy. Moreover, recordings obtained during a visual task reveal that the correlation between neural activity and behavioural choice also increases along the hierarchy. Our study provides a foundation for understanding coding and signal propagation across hierarchically organized cortical and thalamic visual areas.
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Affiliation(s)
| | - Xiaoxuan Jia
- Allen Institute for Brain Science, Seattle, WA, USA.
| | | | - Sam Gale
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Nile Graddis
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Hannah Choi
- Allen Institute for Brain Science, Seattle, WA, USA.,Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | | | | | | | | | - Amy Bernard
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Dillan Brown
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Nicolas Cain
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Linzy Casal
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Andrew Cho
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Timothy C Cox
- University of Missouri-Kansas City School of Dentistry, Kansas City, MO, USA
| | - Kael Dai
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Daniel J Denman
- Allen Institute for Brain Science, Seattle, WA, USA.,The University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | | | | | | | | | - David Feng
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | | | - Brian Hu
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ross Hytnen
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - India Kato
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Jerome Lecoq
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Arielle Leon
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Yang Li
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Fuhui Long
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kyla Mace
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kiet Ngo
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Kat North
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Jed Perkins
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - David Reid
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Sam Seid
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Ben Sutton
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jackie Swapp
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | | | - Rob Young
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Sarah Naylor
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Shawn R Olsen
- Allen Institute for Brain Science, Seattle, WA, USA.
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5
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Groblewski PA, Ollerenshaw DR, Kiggins JT, Garrett ME, Mochizuki C, Casal L, Cross S, Mace K, Swapp J, Manavi S, Williams D, Mihalas S, Olsen SR. Characterization of Learning, Motivation, and Visual Perception in Five Transgenic Mouse Lines Expressing GCaMP in Distinct Cell Populations. Front Behav Neurosci 2020; 14:104. [PMID: 32655383 PMCID: PMC7324787 DOI: 10.3389/fnbeh.2020.00104] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 05/25/2020] [Indexed: 01/01/2023] Open
Abstract
To study the mechanisms of perception and cognition, neural measurements must be made during behavior. A goal of the Allen Brain Observatory is to map the activity of distinct cortical cell classes underlying visual and behavioral processing. Here we describe standardized methodology for training head-fixed mice on a visual change detection task, and we use our paradigm to characterize learning and behavior of five GCaMP6-expressing transgenic lines. We used automated training procedures to facilitate comparisons across mice. Training times varied, but most transgenic mice learned the behavioral task. Motivation levels also varied across mice. To compare mice in similar motivational states we subdivided sessions into over-, under-, and optimally motivated periods. When motivated, the pattern of perceptual decisions were highly correlated across transgenic lines, although overall performance (d-prime) was lower in one line labeling somatostatin inhibitory cells. These results provide important context for using these mice to map neural activity underlying perception and behavior.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | - Shawn R. Olsen
- Allen Institute for Brain Science, Seattle, WA, United States
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