Literature DB >> 17952602

The power law of sensory adaptation: simulation by a model of excitability in spider mechanoreceptor neurons.

Andrew S French1, Päivi H Torkkeli.   

Abstract

The power law of sensory adaptation was introduced more than 50 years ago. It is characterized by action potential adaptation that follows fractional powers of time or frequency, rather than exponential decays and corresponding frequency responses. Power law adaptation describes the responses of a range of vertebrate and invertebrate sensory receptors to deterministic stimuli, such as steps or sinusoids, and to random (white noise) stimulation. Hypotheses about the physical basis of power law adaptation have existed since its discovery. Its cause remains enigmatic, but the site of power law adaptation has been located in the conversion of receptor potentials into action potentials in some preparations. Here, we used pseudorandom noise stimulation and direct spectral estimation to show that simulations containing only two voltage activated currents can reproduce the power law adaptation in two types of spider mechanoreceptors. Identical simulations were previously used to explain the different responses of these two types of sensory neurons to step inputs. We conclude that power law adaptation results during action potential encoding by nonlinear combination of a small number of activation and inactivation processes with different exponential time constants.

Mesh:

Year:  2007        PMID: 17952602     DOI: 10.1007/s10439-007-9392-9

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  12 in total

1.  Dynamics of excitability over extended timescales in cultured cortical neurons.

Authors:  Asaf Gal; Danny Eytan; Avner Wallach; Maya Sandler; Jackie Schiller; Shimon Marom
Journal:  J Neurosci       Date:  2010-12-01       Impact factor: 6.167

Review 2.  Contrast coding in the electrosensory system: parallels with visual computation.

Authors:  Stephen E Clarke; André Longtin; Leonard Maler
Journal:  Nat Rev Neurosci       Date:  2015-11-12       Impact factor: 34.870

3.  Skin relaxation predicts neural firing rate adaptation in SAI touch receptors.

Authors:  Aaron L Williams; Gregory J Gerling; Scott A Wellnitz; Sarah M Bourdon; Ellen A Lumpkin
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2010

4.  Speed-invariant encoding of looming object distance requires power law spike rate adaptation.

Authors:  Stephen E Clarke; Richard Naud; André Longtin; Leonard Maler
Journal:  Proc Natl Acad Sci U S A       Date:  2013-07-29       Impact factor: 11.205

5.  Fractals in the nervous system: conceptual implications for theoretical neuroscience.

Authors:  Gerhard Werner
Journal:  Front Physiol       Date:  2010-07-06       Impact factor: 4.566

6.  Characterization of the encoding properties of intraspinal mechanosensory neurons in the lamprey.

Authors:  Nicole Massarelli; Allan L Yau; Kathleen A Hoffman; Tim Kiemel; Eric D Tytell
Journal:  J Comp Physiol A Neuroethol Sens Neural Behav Physiol       Date:  2017-07-12       Impact factor: 1.836

7.  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

8.  A phenomenological model of the synapse between the inner hair cell and auditory nerve: long-term adaptation with power-law dynamics.

Authors:  Muhammad S A Zilany; Ian C Bruce; Paul C Nelson; Laurel H Carney
Journal:  J Acoust Soc Am       Date:  2009-11       Impact factor: 1.840

9.  Exploring the fundamental dynamics of error-based motor learning using a stationary predictive-saccade task.

Authors:  Aaron L Wong; Mark Shelhamer
Journal:  PLoS One       Date:  2011-09-23       Impact factor: 3.240

10.  Adaptive transition rates in excitable membranes.

Authors:  Shimon Marom
Journal:  Front Comput Neurosci       Date:  2009-02-10       Impact factor: 2.380

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