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      Direct recordings of grid-like neuronal activity in human spatial navigation

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          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

          Grid cells in the entorhinal cortex appear to represent spatial location via a triangular coordinate system. Such cells, which have been identified in rats, bats, and monkeys, are believed to support a wide range of spatial behaviors. By recording neuronal activity from neurosurgical patients performing a virtual-navigation task we identified cells exhibiting grid-like spiking patterns in the human brain, suggesting that humans and simpler animals rely on homologous spatial-coding schemes.

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          Microstructure of a spatial map in the entorhinal cortex.

          Torkel Hafting, Marianne Fyhn, Sturla Molden … (2005)
          The ability to find one's way depends on neural algorithms that integrate information about place, distance and direction, but the implementation of these operations in cortical microcircuits is poorly understood. Here we show that the dorsocaudal medial entorhinal cortex (dMEC) contains a directionally oriented, topographically organized neural map of the spatial environment. Its key unit is the 'grid cell', which is activated whenever the animal's position coincides with any vertex of a regular grid of equilateral triangles spanning the surface of the environment. Grids of neighbouring cells share a common orientation and spacing, but their vertex locations (their phases) differ. The spacing and size of individual fields increase from dorsal to ventral dMEC. The map is anchored to external landmarks, but persists in their absence, suggesting that grid cells may be part of a generalized, path-integration-based map of the spatial environment.
          Article 0 comments Cited 1032 times – based on 0 reviews     Review now
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            Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering.

            R. Quian Quiroga, Z Nádasdy, Y. BEN-SHAUL (2004)
            This study introduces a new method for detecting and sorting spikes from multiunit recordings. The method combines the wavelet transform, which localizes distinctive spike features, with superparamagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions. Moreover, an improved method for setting amplitude thresholds for spike detection is proposed. We describe several criteria for implementation that render the algorithm unsupervised and fast. The algorithm is compared to other conventional methods using several simulated data sets whose characteristics closely resemble those of in vivo recordings. For these data sets, we found that the proposed algorithm outperformed conventional methods.
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              The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat.

              Regis O'Keefe, J Dostrovsky (1971)
              Article 0 comments Cited 442 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Joshua Jacobs
                Itzhak Fried
                Michael J. Kahana
                Journal
                Journal ID (nlm-journal-id): 9809671
                Journal ID (pubmed-jr-id): 21092
                Journal ID (nlm-ta): Nat Neurosci
                Journal ID (iso-abbrev): Nat. Neurosci.
                Title: Nature neuroscience
                ISSN (Print): 1097-6256
                ISSN (Electronic): 1546-1726
                Publication date Nihms-submitted: 25 July 2013
                Publication date (Electronic): 04 August 2013
                Publication date (Print): September 2013
                Publication date PMC-release: 01 March 2014
                Volume: 16
                Issue: 9
                Pages: 1188-1190
                Affiliations
                [1 ]School of Biomedical Engineering, Science & Health Systems, Drexel University, Philadelphia, PA 19104
                [2 ]Department of Psychology, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales, UK
                [3 ]Princeton Neuroscience Institute, Princeton University, NJ 08544
                [4 ]Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104
                [5 ]Department of Neurosurgery, David Geffen School of Medicine and Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095
                [6 ]Department of Neurology, Thomas Jefferson University, Philadelphia, PA 19107
                [7 ]Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA 19107
                [8 ]Functional Neurosurgery Unit, Tel-Aviv Medical Center and Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel
                Author notes
                [*]

                denotes equal contributions

                Article
                Manuscript ID: NIHMS496346
                DOI: 10.1038/nn.3466
                PMC ID: 3767317
                PubMed ID: 23912946
                SO-VID: 44be99d4-6a9f-4fd7-abac-f3b0a2774e5d
                License:

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                History
                Funding
                Funded by: National Institute of Neurological Disorders and Stroke : NINDS
                Award ID: R01 NS033221 || NS
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH061975 || MH
                Categories
                Subject: Article

                ScienceOpen disciplines: Neurosciences
                Data availability:
                ScienceOpen disciplines: Neurosciences

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