4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      High-density electrode recordings reveal strong and specific connections between retinal ganglion cells and midbrain neurons

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The superior colliculus is a midbrain structure that plays important roles in visually guided behaviors in mammals. Neurons in the superior colliculus receive inputs from retinal ganglion cells but how these inputs are integrated in vivo is unknown. Here, we discovered that high-density electrodes simultaneously capture the activity of retinal axons and their postsynaptic target neurons in the superior colliculus, in vivo. We show that retinal ganglion cell axons in the mouse provide a single cell precise representation of the retina as input to superior colliculus. This isomorphic mapping builds the scaffold for precise retinotopic wiring and functionally specific connection strength. Our methods are broadly applicable, which we demonstrate by recording retinal inputs in the optic tectum in zebra finches. We find common wiring rules in mice and zebra finches that provide a precise representation of the visual world encoded in retinal ganglion cells connections to neurons in retinorecipient areas.

          Abstract

          The superior colliculus receives visual information from retinal ganglion cells, but it remains unclear how this information is organized and integrated in vivo. Here the authors describe how high-density electrodes can simultaneously capture the activity of incoming axons and target neurons in the superior colliculus, and demonstrate isomorphic mapping and strong and specific connections in mice and zebrafinches.

          Related collections

          Most cited references111

          • Record: found
          • Abstract: found
          • Article: not found

          DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

          Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Fully integrated silicon probes for high-density recording of neural activity

            Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions in both superficial and deep structures. Existing extracellular probes record neural activity with excellent spatial and temporal (sub-millisecond) resolution, but from only a few dozen neurons per shank. Optical Ca2+ imaging offers more coverage but lacks the temporal resolution needed to distinguish individual spikes reliably and does not measure local field potentials. Until now, no technology compatible with use in unrestrained animals has combined high spatiotemporal resolution with large volume coverage. Here we design, fabricate and test a new silicon probe known as Neuropixels to meet this need. Each probe has 384 recording channels that can programmably address 960 complementary metal–oxide–semiconductor (CMOS) processing-compatible low-impedance TiN sites that tile a single 10-mm long, 70 × 20-μm cross-section shank. The 6 × 9-mm probe base is fabricated with the shank on a single chip. Voltage signals are filtered, amplified, multiplexed and digitized on the base, allowing the direct transmission of noise-free digital data from the probe. The combination of dense recording sites and high channel count yielded well-isolated spiking activity from hundreds of neurons per probe implanted in mice and rats. Using two probes, more than 700 well-isolated single neurons were recorded simultaneously from five brain structures in an awake mouse. The fully integrated functionality and small size of Neuropixels probes allowed large populations of neurons from several brain structures to be recorded in freely moving animals. This combination of high-performance electrode technology and scalable chip fabrication methods opens a path towards recording of brain-wide neural activity during behaviour.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The log-dynamic brain: how skewed distributions affect network operations.

              We often assume that the variables of functional and structural brain parameters - such as synaptic weights, the firing rates of individual neurons, the synchronous discharge of neural populations, the number of synaptic contacts between neurons and the size of dendritic boutons - have a bell-shaped distribution. However, at many physiological and anatomical levels in the brain, the distribution of numerous parameters is in fact strongly skewed with a heavy tail, suggesting that skewed (typically lognormal) distributions are fundamental to structural and functional brain organization. This insight not only has implications for how we should collect and analyse data, it may also help us to understand how the different levels of skewed distributions - from synapses to cognition - are related to each other.
                Bookmark

                Author and article information

                Contributors
                jens.kremkow@charite.de
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                5 September 2022
                5 September 2022
                2022
                : 13
                : 5218
                Affiliations
                [1 ]GRID grid.6363.0, ISNI 0000 0001 2218 4662, Neuroscience Research Center, , Charité-Universitätsmedizin Berlin, ; Charitéplatz 1, 10117 Berlin, Germany
                [2 ]GRID grid.455089.5, Bernstein Center for Computational Neuroscience Berlin, ; Philippstraße 13, 10115 Berlin, Germany
                [3 ]GRID grid.7468.d, ISNI 0000 0001 2248 7639, Institute for Theoretical Biology, , Humboldt-Universität zu Berlin, ; Philippstraße 13, 10115 Berlin, Germany
                [4 ]GRID grid.510949.0, Einstein Center for Neurosciences Berlin, ; Charitéplatz 1, 10117 Berlin, Germany
                [5 ]GRID grid.419542.f, ISNI 0000 0001 0705 4990, Max Planck Institute for Ornithology, ; Eberhard-Gwinner Straße, 82319 Seewiesen, Germany
                [6 ]Max Planck Institute for Biological Intelligence (in foundation), Eberhard-Gwinner Straße, 82319 Seewiesen, Germany
                Author information
                http://orcid.org/0000-0001-6895-7405
                http://orcid.org/0000-0003-0892-506X
                http://orcid.org/0000-0003-2769-2740
                http://orcid.org/0000-0002-5839-7026
                http://orcid.org/0000-0002-7518-0566
                http://orcid.org/0000-0001-7077-4528
                Article
                32775
                10.1038/s41467-022-32775-2
                9445019
                36064789
                1ca77873-7bcc-44ef-81d7-4e4103288cd2
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 September 2021
                : 16 August 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);
                Award ID: VA 742/2
                Award ID: 327654276 – SFB 1315
                Award ID: KR 4062/4–1
                Award Recipient :
                Funded by: ERC-2017-StG - 757459 MIDNIGHT
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                visual system,neural circuits
                Uncategorized
                visual system, neural circuits

                Comments

                Comment on this article