Literature DB >> 10714266

Response features determining spike times.

B J Richmond1, M W Oram, M C Wiener.   

Abstract

Interpreting messages encoded in single neuronal responses requires knowing which features of the responses carry information. That the number of spikes is an important part of the code has long been obvious. In recent years, it has been shown that modulation of the firing rate with about 25 ms precision carries information that is not available from the total number of spikes across the whole response. It has been proposed that patterns of exactly timed (1 ms precision) spikes, such as repeating triplets or quadruplets, might carry information that is not available from knowing about spike count and rate modulation. A model using the spike count distribution, the low-pass filtered PSTH (bandwidth below 30 Hz), and, to a small degree, the interspike interval distribution predicts the numbers and types of exactly-timed triplets and quadruplets that are indistinguishable from those found in the data. From this it can be concluded that the coarse (< 30 Hz) sequential correlation structure over time gives rise to the exactly timed patterns present in the recorded spike trains. Because the coarse temporal structure predicts the fine temporal structure, the information carried by the fine temporal structure must be completely redundant with that carried by the coarse structure. Thus, the existence of precisely timed spike patterns carrying stimulus-related information does not imply control of spike timing at precise time scales.

Mesh:

Year:  1999        PMID: 10714266      PMCID: PMC2565320          DOI: 10.1155/NP.1999.133

Source DB:  PubMed          Journal:  Neural Plast        ISSN: 1687-5443            Impact factor:   3.599


  8 in total

Review 1.  The temporal resolution of neural codes: does response latency have a unique role?

Authors:  M W Oram; D Xiao; B Dritschel; K R Payne
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2002-08-29       Impact factor: 6.237

2.  Temporal characteristics of the predictive synchronous firing modeled by spike-timing-dependent plasticity.

Authors:  Katsunori Kitano; Tomoki Fukai
Journal:  Learn Mem       Date:  2004 May-Jun       Impact factor: 2.460

3.  Precise spatiotemporal patterns among visual cortical areas and their relation to visual stimulus processing.

Authors:  Inbal Ayzenshtat; Elhanan Meirovithz; Hadar Edelman; Uri Werner-Reiss; Elie Bienenstock; Moshe Abeles; Hamutal Slovin
Journal:  J Neurosci       Date:  2010-08-18       Impact factor: 6.167

4.  Feature extraction from spike trains with Bayesian binning: 'latency is where the signal starts'.

Authors:  Dominik Endres; Mike Oram
Journal:  J Comput Neurosci       Date:  2009-05-16       Impact factor: 1.621

Review 5.  Data-driven significance estimation for precise spike correlation.

Authors:  Sonja Grün
Journal:  J Neurophysiol       Date:  2009-01-07       Impact factor: 2.714

6.  Topological analysis of population activity in visual cortex.

Authors:  Gurjeet Singh; Facundo Memoli; Tigran Ishkhanov; Guillermo Sapiro; Gunnar Carlsson; Dario L Ringach
Journal:  J Vis       Date:  2008-06-30       Impact factor: 2.240

7.  The effect of neural noise on spike time precision in a detailed CA3 neuron model.

Authors:  Eduard Kuriscak; Petr Marsalek; Julius Stroffek; Zdenek Wünsch
Journal:  Comput Math Methods Med       Date:  2012-06-24       Impact factor: 2.238

8.  Beyond traditional approaches to understanding the functional role of neuromodulators in sensory cortices.

Authors:  Jean-Marc Edeline
Journal:  Front Behav Neurosci       Date:  2012-07-30       Impact factor: 3.558

  8 in total

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