The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs View Full Text


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Article Info

DATE

2008-11-15

AUTHORS

Michele Giugliano, Giancarlo La Camera, Stefano Fusi, Walter Senn

ABSTRACT

The response of a population of neurons to time-varying synaptic inputs can show a rich phenomenology, hardly predictable from the dynamical properties of the membrane’s inherent time constants. For example, a network of neurons in a state of spontaneous activity can respond significantly more rapidly than each single neuron taken individually. Under the assumption that the statistics of the synaptic input is the same for a population of similarly behaving neurons (mean field approximation), it is possible to greatly simplify the study of neural circuits, both in the case in which the statistics of the input are stationary (reviewed in La Camera et al. in Biol Cybern, 2008) and in the case in which they are time varying and unevenly distributed over the dendritic tree. Here, we review theoretical and experimental results on the single-neuron properties that are relevant for the dynamical collective behavior of a population of neurons. We focus on the response of integrate-and-fire neurons and real cortical neurons to long-lasting, noisy, in vivo-like stationary inputs and show how the theory can predict the observed rhythmic activity of cultures of neurons. We then show how cortical neurons adapt on multiple time scales in response to input with stationary statistics in vitro. Next, we review how it is possible to study the general response properties of a neural circuit to time-varying inputs by estimating the response of single neurons to noisy sinusoidal currents. Finally, we address the dendrite–soma interactions in cortical neurons leading to gain modulation and spike bursts, and show how these effects can be captured by a two-compartment integrate-and-fire neuron. Most of the experimental results reviewed in this article have been successfully reproduced by simple integrate-and-fire model neurons. More... »

PAGES

303-318

References to SciGraph publications

  • 1999-03. A new cellular mechanism for coupling inputs arriving at different cortical layers in NATURE
  • 2001-07. Effects of Neuromodulation in a Cortical Network Model of Object Working Memory Dominated by Recurrent Inhibition in JOURNAL OF COMPUTATIONAL NEUROSCIENCE
  • 2006-04-22. Predicting spike timing of neocortical pyramidal neurons by simple threshold models in JOURNAL OF COMPUTATIONAL NEUROSCIENCE
  • 2006-01-01. Emerging Network Activity in Dissociated Cultures of Neocortex: Novel Electrophysiological Protocols and Mathematical Modeling in ADVANCES IN NETWORK ELECTROPHYSIOLOGY
  • 2006-02-07. An extremely rich repertoire of bursting patterns during the development of cortical cultures in BMC NEUROSCIENCE
  • 2000-01. A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Analysis and an Application to Orientation Tuning in JOURNAL OF COMPUTATIONAL NEUROSCIENCE
  • 2000-11. The role of single neurons in information processing in NATURE NEUROSCIENCE
  • 2004-05-23. Computational subunits in thin dendrites of pyramidal cells in NATURE NEUROSCIENCE
  • 2003-09. The high-conductance state of neocortical neurons in vivo in NATURE REVIEWS NEUROSCIENCE
  • 2005-06. Dynamics of the Instantaneous Firing Rate in Response to Changes in Input Statistics in JOURNAL OF COMPUTATIONAL NEUROSCIENCE
  • 2008-03. Pyramidal neurons: dendritic structure and synaptic integration in NATURE REVIEWS NEUROSCIENCE
  • 2008-11-05. The response of cortical neurons to in vivo-like input current: theory and experiment in BIOLOGICAL CYBERNETICS
  • 2009-01-18. Dendritic encoding of sensory stimuli controlled by deep cortical interneurons in NATURE
  • 2001-08. Efficiency and ambiguity in an adaptive neural code in NATURE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00422-008-0270-9

    DOI

    http://dx.doi.org/10.1007/s00422-008-0270-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1037736423

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/19011920


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