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Spike Code Flow in Cultured Neuronal Networks.

Tamura S, Nishitani Y, Hosokawa C, Miyoshi T, Sawai H, Kamimura T, Yagi Y, Mizuno-Matsumoto Y, Chen YW - Comput Intell Neurosci (2016)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: NBL Technovator Co., Ltd., 631 Shindachimakino, Sennan 590-0522, Japan.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Maximum cross-correlation ΦN(C) in 8 neighbors (8N) and 20 neighbors (20N) for 14 major codes of Sample A. The frame width is 2 ms, and the bit width is between 0.6 and 2.0 ms.
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fig10: Maximum cross-correlation ΦN(C) in 8 neighbors (8N) and 20 neighbors (20N) for 14 major codes of Sample A. The frame width is 2 ms, and the bit width is between 0.6 and 2.0 ms.

Mentions: The results of calculating ΦN(C) are shown in Figure 9 for Samples A and B with changing bit width. We observe that codes with a bit width between 0.6 and 2.0 ms of “1011” and “1101” seemed to be flowing most actively. Figure 10 shows ΦN(C) for the other major codes with a bit width between 0.6 and 2.0 ms of Sample A. Because such values of the raw ΦN(C) depended on the duration (= length – 1) of the code, we further normalized it by the square of “code duration/3” so as to make the code “1011” standard, the duration of which from the beginning “1” to the ending “1” was code-length – 1 (= 3). Figure 11 shows such a normalized flow ΦN(C) for the major codes with a bit width between 0.6 and 2.0 ms in Sample A. We observed that these codes also flowed actively. We observed that the values of the normalized cross-correlations were almost flat. The jags of the curves were caused by normalization with the stepwise code length. Then, the ratios of the EShuf value and the Org value were calculated for each code, and p values were obtained for 14 major codes. These findings suggested that the flow of the Org codes was significantly higher than that of EShuf, Shuf, and Rand. Because the maximum values of 20N were sought from wider ranges of about 3 times (≒20/8) compared to 8N, 20N were also about 3 times larger than that of 8N. That is, statistically maximum values of random-like ΦN(C) from 20 points (20N) are larger than that of 8 points (8N). However, since variance of that of 20 points is also larger than that of 8 points, p value of 8N was far smaller than that of 20N, and it showed that the flow of 8N was more significant than 20N under the assumption that the pseudorandom codes were almost independent. Further, as a noting parameter, we have NF the time frame length or time difference calculating the cross-correlation. This should influence the value of cross-correlation according to the relationship with speed of spike transmission. That is, if the speed of spikes matches the distance (μm) to 8N electrodes divided by NF (ms), maximum cross-correlation of 8N becomes large, and vice versa in case of 20N. However, it is left to be examined in more detail.


Spike Code Flow in Cultured Neuronal Networks.

Tamura S, Nishitani Y, Hosokawa C, Miyoshi T, Sawai H, Kamimura T, Yagi Y, Mizuno-Matsumoto Y, Chen YW - Comput Intell Neurosci (2016)

Maximum cross-correlation ΦN(C) in 8 neighbors (8N) and 20 neighbors (20N) for 14 major codes of Sample A. The frame width is 2 ms, and the bit width is between 0.6 and 2.0 ms.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4863084&req=5

fig10: Maximum cross-correlation ΦN(C) in 8 neighbors (8N) and 20 neighbors (20N) for 14 major codes of Sample A. The frame width is 2 ms, and the bit width is between 0.6 and 2.0 ms.
Mentions: The results of calculating ΦN(C) are shown in Figure 9 for Samples A and B with changing bit width. We observe that codes with a bit width between 0.6 and 2.0 ms of “1011” and “1101” seemed to be flowing most actively. Figure 10 shows ΦN(C) for the other major codes with a bit width between 0.6 and 2.0 ms of Sample A. Because such values of the raw ΦN(C) depended on the duration (= length – 1) of the code, we further normalized it by the square of “code duration/3” so as to make the code “1011” standard, the duration of which from the beginning “1” to the ending “1” was code-length – 1 (= 3). Figure 11 shows such a normalized flow ΦN(C) for the major codes with a bit width between 0.6 and 2.0 ms in Sample A. We observed that these codes also flowed actively. We observed that the values of the normalized cross-correlations were almost flat. The jags of the curves were caused by normalization with the stepwise code length. Then, the ratios of the EShuf value and the Org value were calculated for each code, and p values were obtained for 14 major codes. These findings suggested that the flow of the Org codes was significantly higher than that of EShuf, Shuf, and Rand. Because the maximum values of 20N were sought from wider ranges of about 3 times (≒20/8) compared to 8N, 20N were also about 3 times larger than that of 8N. That is, statistically maximum values of random-like ΦN(C) from 20 points (20N) are larger than that of 8 points (8N). However, since variance of that of 20 points is also larger than that of 8 points, p value of 8N was far smaller than that of 20N, and it showed that the flow of 8N was more significant than 20N under the assumption that the pseudorandom codes were almost independent. Further, as a noting parameter, we have NF the time frame length or time difference calculating the cross-correlation. This should influence the value of cross-correlation according to the relationship with speed of spike transmission. That is, if the speed of spikes matches the distance (μm) to 8N electrodes divided by NF (ms), maximum cross-correlation of 8N becomes large, and vice versa in case of 20N. However, it is left to be examined in more detail.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: NBL Technovator Co., Ltd., 631 Shindachimakino, Sennan 590-0522, Japan.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus