Spike Code Flow in Cultured Neuronal Networks

We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then...

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Veröffentlicht in:Computational Intelligence and Neuroscience 2016-01, Vol.2016 (2016), p.478-488
Hauptverfasser: Tamura, Shinichi, Nishitani, Yoshi, Hosokawa, Chie, Miyoshi, Tomomitsu, Sawai, Hajime, Kamimura, Takuya, Yagi, Yasushi, Mizuno-Matsumoto, Yuko, Chen, Yen-Wei
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container_end_page 488
container_issue 2016
container_start_page 478
container_title Computational Intelligence and Neuroscience
container_volume 2016
creator Tamura, Shinichi
Nishitani, Yoshi
Hosokawa, Chie
Miyoshi, Tomomitsu
Sawai, Hajime
Kamimura, Takuya
Yagi, Yasushi
Mizuno-Matsumoto, Yuko
Chen, Yen-Wei
description We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of “1101” and “1011,” which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the “maximum cross-correlations” among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network.
doi_str_mv 10.1155/2016/7267691
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Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of “1101” and “1011,” which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the “maximum cross-correlations” among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. 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subjects Accuracy
Action Potentials - physiology
Animals
Arrays
Cell Culture Techniques
Codes
Communication
Constants
Electric Stimulation
Electrodes
Embryo, Mammalian
Hippocampus - cytology
Intelligence
Models, Neurological
Nerve Net - physiology
Neural circuitry
Neural networks
Neurons
Neurons - physiology
Physiological aspects
Pseudorandom sequences
Rats
Rats, Wistar
Spikes
Wave propagation
title Spike Code Flow in Cultured Neuronal Networks
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