Sample Report.html

<h1>Grid Cells: Brain, Neuron (Summary Report)</h1>
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<blockquote>
<p>Showing top (10) relevant results from PubMed Central 
Articles dating from range 2000-2010 
Most commonly referenced words in abstract: list here </p>
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<h3>A Theory of How Columns in the Neocortex Enable Learning the Structure of the World</h3>
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<li>Author(s): Hawkins J, Ahmad S, Cui Y</li>
<li>Date: 2017 Oct 25</li>
<li>DOI: 10.3389/fncir.2017.00081</li>
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<p>Abstract: 'The neocortex is complex. Within its 2.5 mm thickness are dozens of cell types, numerous layers, and intricate connectivity patterns. The connections between cells suggest a columnar flow of information across layers as well as a laminar flow within some layers. Fortunately, this complex circuitry is remarkably preserved in all regions, suggesting that a canonical circuit consisting of columns and layers underlies everything the neocortex does. Understanding the function of the canonical circuit is a key goal of neuroscience.'

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<h3>Tracking the Same Neurons across Multiple Days in Ca2+ Imaging Data</h3>
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<li>Author(s): Sheintuch L, Rubin A, Brande-Eilat N, Geva N, Sadeh N, Pinchasof O, Ziv Y</li>
<li>Date: 2017 Oct 24</li>
<li>DOI: 10.1016/j.celrep.2017.10.013</li>
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<p>Abstract: 'Ca', ' imaging techniques permit time-lapse recordings of neuronal activity from large populations over weeks. However, without identifying the same neurons across imaging sessions (cell registration), longitudinal analysis of the neural code is restricted to population-level statistics. Accurate cell registration becomes challenging with increased numbers of cells, sessions, and inter-session intervals. Current cell registration practices, whether manual or automatic, do not quantitatively evaluate registration accuracy, possibly leading to data misinterpretation. We developed a probabilistic method that automatically registers cells across multiple sessions and estimates the registration confidence for each registered cell. Using large-scale Ca', ' imaging data recorded over weeks from the hippocampus and cortex of freely behaving mice, we show that our method performs more accurate registration than previously used routines, yielding estimated error rates &lt;5%, and that the registration is scalable for many sessions. Thus, our method allows reliable longitudinal analysis of the same neurons over long time periods.'

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<h3>Parvalbumin and Somatostatin Interneurons Control Different Space-Coding Networks in the Medial Entorhinal Cortex</h3>
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<li>Author(s): Miao C, Cao Q, Moser MB, Moser EI</li>
<li>Date: 2017 Oct 19</li>
<li>DOI: 10.1016/j.cell.2017.08.050</li>
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<p>Abstract: 'The medial entorhinal cortex (MEC) contains several discrete classes of GABAergic interneurons, but their specific contributions to spatial pattern formation in this area remain elusive. We employed a pharmacogenetic approach to silence either parvalbumin (PV)- or somatostatin (SOM)-expressing interneurons while MEC cells were recorded in freely moving mice. PV-cell silencing antagonized the hexagonally patterned spatial selectivity of grid cells, especially in layer II of MEC. The impairment was accompanied by reduced speed modulation in colocalized speed cells. Silencing SOM cells, in contrast, had no impact on grid cells or speed cells but instead decreased the spatial selectivity of cells with discrete aperiodic firing fields. Border cells and head direction cells were not affected by either intervention. The findings point to distinct roles for PV and SOM interneurons in the local dynamics underlying periodic and aperiodic firing in spatially modulated cells of the MEC.'

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<h3>Predictive Place-Cell Sequences for Goal-Finding Emerge from Goal Memory and the Cognitive Map: A Computational Model</h3>
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<li>Author(s): Gönner L, Vitay J, Hamker FH</li>
<li>Date: 2017 Oct 12</li>
<li>DOI: 10.3389/fncom.2017.00084</li>
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<p>Abstract: "By their remarkable spatial selectivity, hippocampal place cells have qualified as a model system for studying neural coding in relation to behavior (O'Keefe and Nadel, ", '; Burgess, ', '). Place cells fire when the animal traverses a certain location known as the place field, accompanied by 4–8 Hz theta oscillations in the local field potential (LFP). However, during states of slow-wave sleep and awake resting, hippocampal activity displays brief periods of fast (150–250 Hz) oscillations termed sharp wave-ripple episodes (SWRs). According to the “two-stage” model of memory, SWR events are involved in memory consolidation, facilitating the transfer of labile hippocampal memory traces to neocortical areas (Marr, ', '; Buzsáki, ', '). During these events, place cell activity displays sequential patterns termed forward replay and reverse replay: Time-compressed, and sometimes time-reversed, replicas of place cell activity during previous runs (Skaggs and McNaughton, ', '; Kudrimoti et al., ', '; Diba and Buzsáki, ', '), potentially reflecting the recall of spatial experiences stored in the hippocampus during behavior (Jensen and Lisman, ', ').'

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<h3>Anatomical and Electrophysiological Clustering of Superficial Medial Entorhinal Cortex Interneurons</h3>
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<li>Author(s): Martínez JJ, Rahsepar B, White JA</li>
<li>Date: 2017 Oct 16</li>
<li>DOI: 10.1523/ENEURO.0263-16.2017</li>
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<p>Abstract: 'Local GABAergic interneurons regulate the activity of spatially-modulated principal cells in the medial entorhinal cortex (MEC), mediating stellate-to-stellate connectivity and possibly enabling grid formation via recurrent inhibitory circuitry. Despite the important role interneurons seem to play in the MEC cortical circuit, the combination of low cell counts and functional diversity has made systematic electrophysiological studies of these neurons difficult. For these reasons, there remains a paucity of knowledge on the electrophysiological profiles of superficial MEC interneuron populations. Taking advantage of glutamic acid decarboxylase 2 (GAD2)-IRES-tdTomato and PV-tdTomato transgenic mice, we targeted GABAergic interneurons for whole-cell patch-clamp recordings and characterized their passive membrane features, basic input/output properties and action potential (AP) shape. These electrophysiologically characterized cells were then anatomically reconstructed, with emphasis on axonal projections and pial depth. K-means clustering of interneuron anatomical and electrophysiological data optimally classified a population of 106 interneurons into four distinct clusters. The first cluster is comprised of layer 2- and 3-projecting, slow-firing interneurons. The second cluster is comprised largely of PV+ fast-firing interneurons that project mainly to layers 2 and 3. The third cluster contains layer 1- and 2-projecting interneurons, and the fourth cluster is made up of layer 1-projecting horizontal interneurons. These results, among others, will provide greater understanding of the electrophysiological characteristics of MEC interneurons, help guide future in vivo studies, and may aid in uncovering the mechanism of grid field formation.'