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Celii B, Papadopoulos S, Ding Z, Fahey PG, Wang E, Papadopoulos C, Kunin AB, Patel S, Bae JA, Bodor AL, Brittain D, Buchanan J, Bumbarger DJ, Castro MA, Cobos E, Dorkenwald S, Elabbady L, Halageri A, Jia Z, Jordan C, Kapner D, Kemnitz N, Kinn S, Lee K, Li K, Lu R, Macrina T, Mahalingam G, Mitchell E, Mondal SS, Mu S, Nehoran B, Popovych S, Schneider-Mizell CM, Silversmith W, Takeno M, Torres R, Turner NL, Wong W, Wu J, Yu SC, Yin W, Xenes D, Kitchell LM, Rivlin PK, Rose VA, Bishop CA, Wester B, Froudarakis E, Walker EY, Sinz F, Seung HS, Collman F, da Costa NM, Reid RC, Pitkow X, Tolias AS, Reimer J. NEURD: automated proofreading and feature extraction for connectomics. bioRxiv 2024:2023.03.14.532674. [PMID: 36993282 PMCID: PMC10055177 DOI: 10.1101/2023.03.14.532674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes each meshed neuron into a compact and extensively-annotated graph representation. With these feature-rich graphs, we implement workflows to automate a variety of tasks that would otherwise require extensive manual effort, such as state of the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and computation of other features. These features enable many downstream analyses of neural morphology and connectivity, making these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.
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Ding Z, Fahey PG, Papadopoulos S, Wang EY, Celii B, Papadopoulos C, Kunin AB, Chang A, Fu J, Ding Z, Patel S, Ponder K, Muhammad T, Bae JA, Bodor AL, Brittain D, Buchanan J, Bumbarger DJ, Castro MA, Cobos E, Dorkenwald S, Elabbady L, Halageri A, Jia Z, Jordan C, Kapner D, Kemnitz N, Kinn S, Lee K, Li K, Lu R, Macrina T, Mahalingam G, Mitchell E, Mondal SS, Mu S, Nehoran B, Popovych S, Schneider-Mizell CM, Silversmith W, Takeno M, Torres R, Turner NL, Wong W, Wu J, Yin W, Yu SC, Froudarakis E, Sinz F, Seung HS, Collman F, da Costa NM, Reid RC, Walker EY, Pitkow X, Reimer J, Tolias AS. Functional connectomics reveals general wiring rule in mouse visual cortex. bioRxiv 2023:2023.03.13.531369. [PMID: 36993398 PMCID: PMC10054929 DOI: 10.1101/2023.03.13.531369] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
To understand how the brain computes, it is important to unravel the relationship between circuit connectivity and function. Previous research has shown that excitatory neurons in layer 2/3 of the primary visual cortex of mice with similar response properties are more likely to form connections. However, technical challenges of combining synaptic connectivity and functional measurements have limited these studies to few, highly local connections. Utilizing the millimeter scale and nanometer resolution of the MICrONS dataset, we studied the connectivity-function relationship in excitatory neurons of the mouse visual cortex across interlaminar and interarea projections, assessing connection selectivity at the coarse axon trajectory and fine synaptic formation levels. A digital twin model of this mouse, that accurately predicted responses to arbitrary video stimuli, enabled a comprehensive characterization of the function of neurons. We found that neurons with highly correlated responses to natural videos tended to be connected with each other, not only within the same cortical area but also across multiple layers and visual areas, including feedforward and feedback connections, whereas we did not find that orientation preference predicted connectivity. The digital twin model separated each neuron's tuning into a feature component (what the neuron responds to) and a spatial component (where the neuron's receptive field is located). We show that the feature, but not the spatial component, predicted which neurons were connected at the fine synaptic scale. Together, our results demonstrate the "like-to-like" connectivity rule generalizes to multiple connection types, and the rich MICrONS dataset is suitable to further refine a mechanistic understanding of circuit structure and function.
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Affiliation(s)
- Zhuokun Ding
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Paul G Fahey
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Stelios Papadopoulos
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Eric Y Wang
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Brendan Celii
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Christos Papadopoulos
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Alexander B Kunin
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Department of Mathematics, Creighton University, Omaha, USA
| | - Andersen Chang
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Jiakun Fu
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Zhiwei Ding
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Saumil Patel
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Kayla Ponder
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Taliah Muhammad
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, USA
| | | | | | | | | | - Manuel A Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Erick Cobos
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | | | - Akhilesh Halageri
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Chris Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Dan Kapner
- Allen Institute for Brain Science, Seattle, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Sam Kinn
- Allen Institute for Brain Science, Seattle, USA
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, USA
| | - Kai Li
- Computer Science Department, Princeton University, Princeton, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | | | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Shanka Subhra Mondal
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | | | | | - Marc Takeno
- Allen Institute for Brain Science, Seattle, USA
| | | | - Nicholas L Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - William Wong
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Wenjing Yin
- Allen Institute for Brain Science, Seattle, USA
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Emmanouil Froudarakis
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Department of Basic Sciences, Faculty of Medicine, University of Crete, Heraklion, Greece
| | - Fabian Sinz
- Institute for Bioinformatics and Medical Informatics, University Tübingen, Tübingen, Germany
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellas, Heraklion, Greece
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | | | - R Clay Reid
- Allen Institute for Brain Science, Seattle, USA
| | - Edgar Y Walker
- Department of Physiology and Biophysics, University of Washington, Seattle, USA
- Computational Neuroscience Center, University of Washington, Seattle, USA
| | - Xaq Pitkow
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, USA
| | - Jacob Reimer
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Andreas S Tolias
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, USA
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3
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Shen S, Jiang X, Scala F, Fu J, Fahey P, Kobak D, Tan Z, Zhou N, Reimer J, Sinz F, Tolias AS. Distinct organization of two cortico-cortical feedback pathways. Nat Commun 2022; 13:6389. [PMID: 36302912 PMCID: PMC9613627 DOI: 10.1038/s41467-022-33883-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [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: 03/10/2020] [Accepted: 10/06/2022] [Indexed: 12/25/2022] Open
Abstract
Neocortical feedback is critical for attention, prediction, and learning. To mechanically understand its function requires deciphering its cell-type wiring. Recent studies revealed that feedback between primary motor to primary somatosensory areas in mice is disinhibitory, targeting vasoactive intestinal peptide-expressing interneurons, in addition to pyramidal cells. It is unknown whether this circuit motif represents a general cortico-cortical feedback organizing principle. Here we show that in contrast to this wiring rule, feedback between higher-order lateromedial visual area to primary visual cortex preferentially activates somatostatin-expressing interneurons. Functionally, both feedback circuits temporally sharpen feed-forward excitation eliciting a transient increase-followed by a prolonged decrease-in pyramidal cell activity under sustained feed-forward input. However, under feed-forward transient input, the primary motor to primary somatosensory cortex feedback facilitates bursting while lateromedial area to primary visual cortex feedback increases time precision. Our findings argue for multiple cortico-cortical feedback motifs implementing different dynamic non-linear operations.
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Affiliation(s)
- Shan Shen
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Xiaolong Jiang
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA
| | - Federico Scala
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Jiakun Fu
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Paul Fahey
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Dmitry Kobak
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
| | - Zhenghuan Tan
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Na Zhou
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Reimer
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Fabian Sinz
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Bernstein Center for Computational Neuroscience, University of Tübingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
| | - Andreas S Tolias
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
- Department of Electrical and Computational Engineering, Rice University, Houston, TX, USA.
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4
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Henninger J, Krahe R, Sinz F, Benda J. Tracking activity patterns of a multispecies community of gymnotiform weakly electric fish in their neotropical habitat without tagging. J Exp Biol 2020; 223:jeb206342. [PMID: 31937524 DOI: 10.1242/jeb.206342] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 01/06/2020] [Indexed: 01/06/2023]
Abstract
Field studies on freely behaving animals commonly require tagging and often are focused on single species. Weakly electric fish generate a species- and individual-specific electric organ discharge (EOD) and therefore provide a unique opportunity for individual tracking without tagging. Here, we present and test tracking algorithms based on recordings with submerged electrode arrays. Harmonic structures extracted from power spectra provide fish identity. Localization of fish based on weighted averages of their EOD amplitudes is found to be more robust than fitting a dipole model. We apply these techniques to monitor a community of three species, Apteronotus rostratus, Eigenmannia humboldtii and Sternopygus dariensis, in their natural habitat in Darién, Panama. We found consistent upstream movements after sunset followed by downstream movements in the second half of the night. Extrapolations of these movements and estimates of fish density obtained from additional transect data suggest that some fish cover at least several hundreds of meters of the stream per night. Most fish, including E. humboldtii, were traversing the electrode array solitarily. From in situ measurements of the decay of the EOD amplitude with distance of individual animals, we estimated that fish can detect conspecifics at distances of up to 2 m. Our recordings also emphasize the complexity of natural electrosensory scenes resulting from the interactions of the EODs of different species. Electrode arrays thus provide an unprecedented window into the so-far hidden nocturnal activities of multispecies communities of weakly electric fish at an unmatched level of detail.
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Affiliation(s)
- Jörg Henninger
- Institut für Neurobiologie, Eberhard Karls Universität, 72076 Tübingen, Germany
| | - Rüdiger Krahe
- Institut für Biologie, Humboldt-Universität zu Berlin, Philippstr. 13, 10115 Berlin, Germany
- McGill University, Department of Biology, 1205 Ave. Docteur Penfield, Montreal, Quebec, Canada, H3A 1B1
| | - Fabian Sinz
- Bernstein Center for Computational Neuroscience, Eberhard Karls Universität, 72076 Tübingen, Germany
- Institut für Informatik, Eberhard Karls Univzersität, 72076 Tübingen, Germany
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jan Benda
- Institut für Neurobiologie, Eberhard Karls Universität, 72076 Tübingen, Germany
- Bernstein Center for Computational Neuroscience, Eberhard Karls Universität, 72076 Tübingen, Germany
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5
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Jiang X, Shen S, Sinz F, Reimer J, Cadwell CR, Berens P, Ecker AS, Patel S, Denfield GH, Froudarakis E, Li S, Walker E, Tolias AS. Response to Comment on “Principles of connectivity among morphologically defined cell types in adult neocortex”. Science 2016; 353:1108. [DOI: 10.1126/science.aaf6102] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Accepted: 08/03/2016] [Indexed: 11/02/2022]
Affiliation(s)
- Xiaolong Jiang
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Shan Shen
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Fabian Sinz
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Cathryn R. Cadwell
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Philipp Berens
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Bernstein Centre for Computational Neuroscience, Tübingen, Germany
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- Werner Reichardt Center for Integrative Neuroscience and Institute of Theoretical Physics, University of Tübingen, Tübingen, Germany
| | - Alexander S. Ecker
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Bernstein Centre for Computational Neuroscience, Tübingen, Germany
- Werner Reichardt Center for Integrative Neuroscience and Institute of Theoretical Physics, University of Tübingen, Tübingen, Germany
| | - Saumil Patel
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - George H. Denfield
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | | | - Shuang Li
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Edgar Walker
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Andreas S. Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Bernstein Centre for Computational Neuroscience, Tübingen, Germany
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Jiang X, Shen S, Cadwell CR, Berens P, Sinz F, Ecker AS, Patel S, Tolias AS. Principles of connectivity among morphologically defined cell types in adult neocortex. Science 2015; 350:aac9462. [PMID: 26612957 DOI: 10.1126/science.aac9462] [Citation(s) in RCA: 519] [Impact Index Per Article: 57.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Since the work of Ramón y Cajal in the late 19th and early 20th centuries, neuroscientists have speculated that a complete understanding of neuronal cell types and their connections is key to explaining complex brain functions. However, a complete census of the constituent cell types and their wiring diagram in mature neocortex remains elusive. By combining octuple whole-cell recordings with an optimized avidin-biotin-peroxidase staining technique, we carried out a morphological and electrophysiological census of neuronal types in layers 1, 2/3, and 5 of mature neocortex and mapped the connectivity between more than 11,000 pairs of identified neurons. We categorized 15 types of interneurons, and each exhibited a characteristic pattern of connectivity with other interneuron types and pyramidal cells. The essential connectivity structure of the neocortical microcircuit could be captured by only a few connectivity motifs.
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Affiliation(s)
- Xiaolong Jiang
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
| | - Shan Shen
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Cathryn R Cadwell
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Philipp Berens
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA. Bernstein Centre for Computational Neuroscience, Tübingen, Germany. Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany. Werner Reichardt Center for Integrative Neuroscience and Institute of Theoretical Physics, University of Tübingen, Tübingen, Germany
| | - Fabian Sinz
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Alexander S Ecker
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA. Bernstein Centre for Computational Neuroscience, Tübingen, Germany. Werner Reichardt Center for Integrative Neuroscience and Institute of Theoretical Physics, University of Tübingen, Tübingen, Germany. Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Saumil Patel
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA. Bernstein Centre for Computational Neuroscience, Tübingen, Germany.
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Abstract
Extracting complementary features in parallel pathways is a widely used strategy for a robust representation of sensory signals. Weakly electric fish offer the rare opportunity to study complementary encoding of social signals in all of its electrosensory pathways. Electrosensory information is conveyed in three parallel pathways: two receptor types of the tuberous (active) system and one receptor type of the ampullary (passive) system. Modulations of the fish's own electric field are sensed by these receptors and used in navigation, prey detection, and communication. We studied the neuronal representation of electric communication signals (called chirps) in the ampullary and the two tuberous pathways of Eigenmannia virescens. We first characterized different kinds of chirps observed in behavioral experiments. Since Eigenmannia chirps simultaneously drive all three types of receptors, we studied their responses in in vivo electrophysiological recordings. Our results demonstrate that different electroreceptor types encode different aspects of the stimuli and each appears best suited to convey information about a certain chirp type. A decoding analysis of single neurons and small populations shows that this specialization leads to a complementary representation of information in the tuberous and ampullary receptors. This suggests that a potential readout mechanism should combine information provided by the parallel processing streams to improve chirp detectability.
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Affiliation(s)
- Anna Stöckl
- Department Biology II, Ludwig-Maximilians-Universität München, Munich, Germany; Department of Biology, Lund University, Lund, Sweden
| | - Fabian Sinz
- Institut für Neurobiologie, Eberhardt Karls Universität Tübingen, Tübingen, Germany; and Bernstein Center for Computational Neuroscience, Tübingen, Germany
| | - Jan Benda
- Department Biology II, Ludwig-Maximilians-Universität München, Munich, Germany; Institut für Neurobiologie, Eberhardt Karls Universität Tübingen, Tübingen, Germany; and
| | - Jan Grewe
- Department Biology II, Ludwig-Maximilians-Universität München, Munich, Germany; Institut für Neurobiologie, Eberhardt Karls Universität Tübingen, Tübingen, Germany; and
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8
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Sinz F, Lies JP, Gerwinn S, Bethge M. Natter: APythonNatural Image Statistics Toolbox. J Stat Softw 2014. [DOI: 10.18637/jss.v061.i05] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
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9
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Abstract
Divisive normalization in primary visual cortex has been linked to adaptation to natural image statistics in accordance to Barlow's redundancy reduction hypothesis. Using recent advances in natural image modeling, we show that the previously studied static model of divisive normalization is rather inefficient in reducing local contrast correlations, but that a simple temporal contrast adaptation mechanism of the half-saturation constant can substantially increase its efficiency. Our findings reveal the experimentally observed temporal dynamics of divisive normalization to be critical for redundancy reduction. The redundancy reduction hypothesis postulates that neural representations adapt to sensory input statistics such that their responses become as statistically independent as possible. Based on this hypothesis, many properties of early visual neurons—like orientation selectivity or divisive normalization—have been linked to natural image statistics. Divisive normalization, in particular, models a widely observed neural response property: The divisive inhibition of a single neuron by a pool of others. This mechanism has been shown to reduce the redundancy among neural responses to typical contrast dependencies in natural images. Here, we show that the standard model of divisive normalization achieves substantially less redundancy reduction than a theoretically optimal mechanism called radial factorization. On the other hand, we find that radial factorization is inconsistent with existing neurophysiological observations. As a solution we suggest a new physiologically plausible modification of the standard model which accounts for the dynamics of the visual input by adapting to local contrasts during fixations. In this way the dynamic version of the standard model achieves almost optimal redundancy reduction performance. Our results imply that the dynamics of natural viewing conditions are critical for testing the role of divisive normalization for redundancy reduction.
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Affiliation(s)
- Fabian Sinz
- Department for Neuroethology, University Tübingen, Tübingen, Germany.
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11
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Abstract
Orientation selectivity is the most striking feature of simple cell coding in V1 that has been shown to emerge from the reduction of higher-order correlations in natural images in a large variety of statistical image models. The most parsimonious one among these models is linear Independent Component Analysis (ICA), whereas second-order decorrelation transformations such as Principal Component Analysis (PCA) do not yield oriented filters. Because of this finding, it has been suggested that the emergence of orientation selectivity may be explained by higher-order redundancy reduction. To assess the tenability of this hypothesis, it is an important empirical question how much more redundancy can be removed with ICA in comparison to PCA or other second-order decorrelation methods. Although some previous studies have concluded that the amount of higher-order correlation in natural images is generally insignificant, other studies reported an extra gain for ICA of more than 100%. A consistent conclusion about the role of higher-order correlations in natural images can be reached only by the development of reliable quantitative evaluation methods. Here, we present a very careful and comprehensive analysis using three evaluation criteria related to redundancy reduction: In addition to the multi-information and the average log-loss, we compute complete rate-distortion curves for ICA in comparison with PCA. Without exception, we find that the advantage of the ICA filters is small. At the same time, we show that a simple spherically symmetric distribution with only two parameters can fit the data significantly better than the probabilistic model underlying ICA. This finding suggests that, although the amount of higher-order correlation in natural images can in fact be significant, the feature of orientation selectivity does not yield a large contribution to redundancy reduction within the linear filter bank models of V1 simple cells.
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Affiliation(s)
- Jan Eichhorn
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Fabian Sinz
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Matthias Bethge
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
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