| 1. |
- Brasselet, Romain, et al.
(author)
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Isometric coding of spiking haptic signals by peripheral somatosensory neurons
- 2011
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In: Advances in Computational Intelligence. - Berlin : Springer Berlin/Heidelberg. - 9783642215018 ; , s. 528-536
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Conference paper (peer-reviewed)abstract
- We study how primary tactile afferents encode relevant contact features to mediate early processing of haptic information. In this paper, we apply metrical information theory to perform temporal decoding of human microneurography data. First, we enrich the theory by deriving a novel spike train metrics inspired by neuronal computation. This spike train metrics can be interpreted biologically and its behaviour is not influenced by spontaneous activity, which decreases the ability of other spike metrics to separate input patterns. Second, we employ our metrical information tools to demonstrate that primary spiking signals allow a putative neural decoder to go beyond stimulus discrimination. They transmit information about geometrical properties of the input space. We show that first-spike latencies are enough to guarantee maximum information transmission of tactile stimuli. However, entire primary spike trains are necessary to encode isometric representations of the stimulus space, a likely basis for generalisation in haptic perception.
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| 2. |
- Brasselet, Romain, et al.
(author)
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Quantifying neurotransmission reliability through metrics-based information analysis
- 2011
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In: Neural Computation. - 0899-7667 .- 1530-888X. ; 23:4, s. 852-881
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Journal article (peer-reviewed)abstract
- 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.
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