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Quantifying neurotransmission reliability through metrics-based information analysis
Université Pierre et Marie Curie, Paris.
Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB), Physiology.
Université Pierre et Marie Curie, Paris.
2011 (English)In: Neural Computation, ISSN 0899-7667, E-ISSN 1530-888X, Vol. 23, no 4, 852-881 p.Article in journal (Refereed) Published
Abstract [en]

We set forth an information-theoretical measure to quantify neurotransmission reliability while taking into full account the metrical properties of the spike train space. This parametric information analysis relies on similarity measures induced by the metrical relations between neural responses as spikes flow in. Thus, in order to assess the entropy, the conditional entropy, and the overall information transfer, this method does not require any a priori decoding algorithm to partition the space into equivalence classes. It therefore allows the optimal parameters of a class of distances to be determined with respect to information transmission. To validate the proposed information-theoretical approach, we study precise temporal decoding of human somatosensory signals recorded using microneurography experiments. For this analysis, we employ a similarity measure based on the Victor-Purpura spike train metrics. We show that with appropriate parameters of this distance, the relative spike times of the mechanoreceptors? responses convey enough information to perform optimal discrimination?defined as maximum metrical information and zero conditional entropy?of 81 distinct stimuli within 40 ms of the first afferent spike. The proposed information-theoretical measure proves to be a suitable generalization of Shannon mutual information in order to consider the metrics of temporal codes explicitly. It allows neurotransmission reliability to be assessed in the presence of large spike train spaces (e.g., neural population codes) with high temporal precision.

Place, publisher, year, edition, pages
2011. Vol. 23, no 4, 852-881 p.
National Category
Neurosciences
Identifiers
URN: urn:nbn:se:umu:diva-41342DOI: 10.1162/NECO_a_00099PubMedID: 21222522OAI: oai:DiVA.org:umu-41342DiVA: diva2:405705
Available from: 2011-03-23 Created: 2011-03-23 Last updated: 2017-12-11Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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