Literature DB >> 16807345

Multiple time scales of temporal response in pyramidal and fast spiking cortical neurons.

Giancarlo La Camera1, Alexander Rauch, David Thurbon, Hans-R Lüscher, Walter Senn, Stefano Fusi.   

Abstract

Neural dynamic processes correlated over several time scales are found in vivo, in stimulus-evoked as well as spontaneous activity, and are thought to affect the way sensory stimulation is processed. Despite their potential computational consequences, a systematic description of the presence of multiple time scales in single cortical neurons is lacking. In this study, we injected fast spiking and pyramidal (PYR) neurons in vitro with long-lasting episodes of step-like and noisy, in-vivo-like current. Several processes shaped the time course of the instantaneous spike frequency, which could be reduced to a small number (1-4) of phenomenological mechanisms, either reducing (adapting) or increasing (facilitating) the neuron's firing rate over time. The different adaptation/facilitation processes cover a wide range of time scales, ranging from initial adaptation (<10 ms, PYR neurons only), to fast adaptation (<300 ms), early facilitation (0.5-1 s, PYR only), and slow (or late) adaptation (order of seconds). These processes are characterized by broad distributions of their magnitudes and time constants across cells, showing that multiple time scales are at play in cortical neurons, even in response to stationary stimuli and in the presence of input fluctuations. These processes might be part of a cascade of processes responsible for the power-law behavior of adaptation observed in several preparations, and may have far-reaching computational consequences that have been recently described.

Mesh:

Year:  2006        PMID: 16807345     DOI: 10.1152/jn.00453.2006

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  47 in total

1.  Incorporating spike-rate adaptation into a rate code in mathematical and biological neurons.

Authors:  Bridget N Ralston; Lucas Q Flagg; Eric Faggin; John T Birmingham
Journal:  J Neurophysiol       Date:  2016-02-17       Impact factor: 2.714

2.  The dynamical response properties of neocortical neurons to temporally modulated noisy inputs in vitro.

Authors:  Harold Köndgen; Caroline Geisler; Stefano Fusi; Xiao-Jing Wang; Hans-Rudolf Lüscher; Michele Giugliano
Journal:  Cereb Cortex       Date:  2008-02-09       Impact factor: 5.357

Review 3.  Sensory adaptation.

Authors:  Barry Wark; Brian Nils Lundstrom; Adrienne Fairhall
Journal:  Curr Opin Neurobiol       Date:  2007-08-21       Impact factor: 6.627

4.  Temporal whitening by power-law adaptation in neocortical neurons.

Authors:  Christian Pozzorini; Richard Naud; Skander Mensi; Wulfram Gerstner
Journal:  Nat Neurosci       Date:  2013-06-09       Impact factor: 24.884

5.  Processing of hedonic and chemosensory features of taste in medial prefrontal and insular networks.

Authors:  Ahmad Jezzini; Luca Mazzucato; Giancarlo La Camera; Alfredo Fontanini
Journal:  J Neurosci       Date:  2013-11-27       Impact factor: 6.167

6.  Modeling multiple time scale firing rate adaptation in a neural network of local field potentials.

Authors:  Brian Nils Lundstrom
Journal:  J Comput Neurosci       Date:  2014-10-16       Impact factor: 1.621

7.  The impact of spike-frequency adaptation on balanced network dynamics.

Authors:  Victor J Barranca; Han Huang; Sida Li
Journal:  Cogn Neurodyn       Date:  2018-09-03       Impact factor: 5.082

8.  Network structure and input integration in competing firing rate models for decision-making.

Authors:  Victor J Barranca; Han Huang; Genji Kawakita
Journal:  J Comput Neurosci       Date:  2019-01-19       Impact factor: 1.621

9.  Dynamical correlation patterns and corresponding community structure in neural spontaneous activity at criticality.

Authors:  T Termsaithong; K Aihara
Journal:  Cogn Neurodyn       Date:  2013-04-11       Impact factor: 5.082

10.  Power-law dynamics in an auditory-nerve model can account for neural adaptation to sound-level statistics.

Authors:  Muhammad S A Zilany; Laurel H Carney
Journal:  J Neurosci       Date:  2010-08-04       Impact factor: 6.167

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