Literature DB >> 10695762

How to measure the information gained from one symbol.

M R DeWeese1, M Meister.   

Abstract

Information theory provides a powerful framework to analyse how neurons represent sensory stimuli or other behavioural variables. A recurring question regards the amount of information conveyed by a specific neuronal response. Here we show that the commonly used definition for this quantity has a serious flaw: the information accumulated during subsequent observations of neural activity fails to combine additively. Additivity is a highly desirable property, both on theoretical grounds and for the practical purpose of analysing population codes. We propose an alternative measure for the information per observation and prove that this is the only definition that satisfies additivity. The old and the new definitions measure very different aspects of the neural code, which is illustrated with visual responses from a motion-sensitive neuron in the primate cortex. Our analysis allows additional interpretation of several published results, which suggests that the neurons studied are operating far from their information capacity.

Mesh:

Year:  1999        PMID: 10695762

Source DB:  PubMed          Journal:  Network        ISSN: 0954-898X            Impact factor:   1.273


  37 in total

1.  Auditory cortical neurons convey maximal stimulus-specific information at their best frequency.

Authors:  Nathan Montgomery; Michael Wehr
Journal:  J Neurosci       Date:  2010-10-06       Impact factor: 6.167

2.  Assessing the encoding of stimulus attributes with rapid sequences of stimulus events.

Authors:  M Eger; R Eckhorn
Journal:  J Comput Neurosci       Date:  2002 Nov-Dec       Impact factor: 1.621

3.  Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet.

Authors:  Edmund T Rolls
Journal:  Front Comput Neurosci       Date:  2012-06-19       Impact factor: 2.380

4.  Complex spike activity in the oculomotor vermis of the cerebellum: a vectorial error signal for saccade motor learning?

Authors:  Robijanto Soetedjo; Yoshiko Kojima; Albert F Fuchs
Journal:  J Neurophysiol       Date:  2008-07-23       Impact factor: 2.714

Review 5.  Synergy, redundancy, and multivariate information measures: an experimentalist's perspective.

Authors:  Nicholas Timme; Wesley Alford; Benjamin Flecker; John M Beggs
Journal:  J Comput Neurosci       Date:  2013-07-03       Impact factor: 1.621

Review 6.  A Tutorial for Information Theory in Neuroscience.

Authors:  Nicholas M Timme; Christopher Lapish
Journal:  eNeuro       Date:  2018-09-11

7.  Semantic information, autonomous agency and non-equilibrium statistical physics.

Authors:  Artemy Kolchinsky; David H Wolpert
Journal:  Interface Focus       Date:  2018-10-19       Impact factor: 3.906

8.  Proportional spike-timing precision and firing reliability underlie efficient temporal processing of periodicity and envelope shape cues.

Authors:  Y Zheng; M A Escabí
Journal:  J Neurophysiol       Date:  2013-05-01       Impact factor: 2.714

9.  Decoding stimulus duration from neural responses in the auditory midbrain.

Authors:  Brandon Aubie; Riziq Sayegh; Thane Fremouw; Ellen Covey; Paul A Faure
Journal:  J Neurophysiol       Date:  2014-08-13       Impact factor: 2.714

10.  Decomposing information into copying versus transformation.

Authors:  Artemy Kolchinsky; Bernat Corominas-Murtra
Journal:  J R Soc Interface       Date:  2020-01-22       Impact factor: 4.118

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