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      neuroConstruct: A Tool for Modeling Networks of Neurons in 3D Space

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      Author(s): 1 , 1 , 1 , ∗
      Publication date PMC-release:
      Journal: Neuron
      Publisher: Cell Press
      Keywords: SYSNEURO

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          Summary

          Conductance-based neuronal network models can help us understand how synaptic and cellular mechanisms underlie brain function. However, these complex models are difficult to develop and are inaccessible to most neuroscientists. Moreover, even the most biologically realistic network models disregard many 3D anatomical features of the brain. Here, we describe a new software application, neuroConstruct, that facilitates the creation, visualization, and analysis of networks of multicompartmental neurons in 3D space. A graphical user interface allows model generation and modification without programming. Models within neuroConstruct are based on new simulator-independent NeuroML standards, allowing automatic generation of code for NEURON or GENESIS simulators. neuroConstruct was tested by reproducing published models and its simulator independence verified by comparing the same model on two simulators. We show how more anatomically realistic network models can be created and their properties compared with experimental measurements by extending a published 1D cerebellar granule cell layer model to 3D.

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          Neuronal circuits of the neocortex.

          Rodney J. Douglas, Kevan A C Martin (2004)
          We explore the extent to which neocortical circuits generalize, i.e., to what extent can neocortical neurons and the circuits they form be considered as canonical? We find that, as has long been suspected by cortical neuroanatomists, the same basic laminar and tangential organization of the excitatory neurons of the neocortex is evident wherever it has been sought. Similarly, the inhibitory neurons show characteristic morphology and patterns of connections throughout the neocortex. We offer a simple model of cortical processing that is consistent with the major features of cortical circuits: The superficial layer neurons within local patches of cortex, and within areas, cooperate to explore all possible interpretations of different cortical input and cooperatively select an interpretation consistent with their various cortical and subcortical inputs.
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            Large-scale recording of neuronal ensembles.

            György Buzsáki (2004)
            How does the brain orchestrate perceptions, thoughts and actions from the spiking activity of its neurons? Early single-neuron recording research treated spike pattern variability as noise that needed to be averaged out to reveal the brain's representation of invariant input. Another view is that variability of spikes is centrally coordinated and that this brain-generated ensemble pattern in cortical structures is itself a potential source of cognition. Large-scale recordings from neuronal ensembles now offer the opportunity to test these competing theoretical frameworks. Currently, wire and micro-machined silicon electrode arrays can record from large numbers of neurons and monitor local neural circuits at work. Achieving the full potential of massively parallel neuronal recordings, however, will require further development of the neuron-electrode interface, automated and efficient spike-sorting algorithms for effective isolation and identification of single neurons, and new mathematical insights for the analysis of network properties.
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              Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex.

              Kenichi Ohki, Sooyoung Chung, Yeang Ch'ng … (2005)
              Neurons in the cerebral cortex are organized into anatomical columns, with ensembles of cells arranged from the surface to the white matter. Within a column, neurons often share functional properties, such as selectivity for stimulus orientation; columns with distinct properties, such as different preferred orientations, tile the cortical surface in orderly patterns. This functional architecture was discovered with the relatively sparse sampling of microelectrode recordings. Optical imaging of membrane voltage or metabolic activity elucidated the overall geometry of functional maps, but is averaged over many cells (resolution >100 microm). Consequently, the purity of functional domains and the precision of the borders between them could not be resolved. Here, we labelled thousands of neurons of the visual cortex with a calcium-sensitive indicator in vivo. We then imaged the activity of neuronal populations at single-cell resolution with two-photon microscopy up to a depth of 400 microm. In rat primary visual cortex, neurons had robust orientation selectivity but there was no discernible local structure; neighbouring neurons often responded to different orientations. In area 18 of cat visual cortex, functional maps were organized at a fine scale. Neurons with opposite preferences for stimulus direction were segregated with extraordinary spatial precision in three dimensions, with columnar borders one to two cells wide. These results indicate that cortical maps can be built with single-cell precision.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Neuron
                Title: Neuron
                Publisher: Cell Press
                ISSN (Print): 0896-6273
                ISSN (Electronic): 1097-4199
                Publication date PMC-release: 19 April 2007
                Publication date (Print): 19 April 2007
                Volume: 54
                Issue: 2
                Pages: 219-235
                Affiliations
                [1 ]Department of Physiology, University College London, Gower Street, London WC1E 6BT, United Kingdom
                Author notes
                [∗ ]Corresponding author a.silver@ 123456ucl.ac.uk
                Article
                Publisher ID: NEURON2880
                DOI: 10.1016/j.neuron.2007.03.025
                PMC ID: 1885959
                PubMed ID: 17442244
                SO-VID: 841c4b99-efad-4b9b-90f8-5577954eb48b
                Copyright © © 2007 ELL & Excerpta Medica.
                License:

                This document may be redistributed and reused, subject to certain conditions.

                History
                Date received : 20 November 2006
                Date revision received : 9 February 2007
                Date accepted : 26 March 2007
                Categories
                Subject: Neurotechnique

                ScienceOpen disciplines: Neurosciences
                Keywords: sysneuro
                Data availability:
                ScienceOpen disciplines: Neurosciences
                Keywords: sysneuro

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