Literature DB >> 9790189

Who reads temporal information contained across synchronized and oscillatory spike trains?

K MacLeod1, A Bäcker, G Laurent.   

Abstract

Our inferences about brain mechanisms underlying perception rely on whether it is possible for the brain to 'reconstruct' a stimulus from the information contained in the spike trains from many neurons. How the brain actually accomplishes this reconstruction remains largely unknown. Oscillatory and synchronized activities in the brain of mammals have been correlated with distinct behavioural states or the execution of complex cognitive tasks and are proposed to participate in the 'binding' of individual features into more complex percepts. But if synchronization is indeed relevant, what senses it? In insects, oscillatory synchronized activity in the early olfactory system seems to be necessary for fine odour discrimination and enables the encoding of information about a stimulus in spike times relative to the oscillatory 'clock. Here we study the decoding of these coherent oscillatory signals. We identify a population of neurons downstream from the odour-activated, synchronized neuronal assemblies. These downstream neurons show odour responses whose specificity is degraded when their inputs are desynchronized. This degradation of selectivity consists of the appearance of responses to new odours and a loss of discrimination of spike trains evoked by different odours. Such loss of information is never observed in the upstream neurons whose activity is desynchronized. These results indicate that information encoded in time across ensembles of neurons converges onto single neurons downstream in the pathway.

Mesh:

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Year:  1998        PMID: 9790189     DOI: 10.1038/27201

Source DB:  PubMed          Journal:  Nature        ISSN: 0028-0836            Impact factor:   49.962


  64 in total

1.  Odor space and olfactory processing: collective algorithms and neural implementation.

Authors:  J J Hopfield
Journal:  Proc Natl Acad Sci U S A       Date:  1999-10-26       Impact factor: 11.205

2.  Synchronous clusters in a noisy inhibitory neural network.

Authors:  P H Tiesinga; J V José
Journal:  J Comput Neurosci       Date:  2000 Jul-Aug       Impact factor: 1.621

3.  Neural coding with graded membrane potential changes and spikes.

Authors:  J Kretzberg; A K Warzecha; M Egelhaaf
Journal:  J Comput Neurosci       Date:  2001 Sep-Oct       Impact factor: 1.621

4.  Visual responses of crayfish ocular motoneurons: an information theoretical analysis.

Authors:  C S Miller; D H Johnson; J P Schroeter; L Myint; R M Glantz
Journal:  J Comput Neurosci       Date:  2003 Sep-Oct       Impact factor: 1.621

5.  Non-Euclidean properties of spike train metric spaces.

Authors:  Dmitriy Aronov; Jonathan D Victor
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2004-06-02

6.  Classification of odorants across layers in locust olfactory pathway.

Authors:  Pavel Sanda; Tiffany Kee; Nitin Gupta; Mark Stopfer; Maxim Bazhenov
Journal:  J Neurophysiol       Date:  2016-02-10       Impact factor: 2.714

Review 7.  Spike train metrics.

Authors:  Jonathan D Victor
Journal:  Curr Opin Neurobiol       Date:  2005-10       Impact factor: 6.627

8.  Target cell-specific modulation of neuronal activity by astrocytes.

Authors:  A S Kozlov; M C Angulo; E Audinat; S Charpak
Journal:  Proc Natl Acad Sci U S A       Date:  2006-06-16       Impact factor: 11.205

9.  Cortical discrimination of complex natural stimuli: can single neurons match behavior?

Authors:  Le Wang; Rajiv Narayan; Gilberto Graña; Maoz Shamir; Kamal Sen
Journal:  J Neurosci       Date:  2007-01-17       Impact factor: 6.167

10.  Encoding of mixtures in a simple olfactory system.

Authors:  Kai Shen; Sina Tootoonian; Gilles Laurent
Journal:  Neuron       Date:  2013-11-07       Impact factor: 17.173

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