Limits...
Simulation of Code Spectrum and Code Flow of Cultured Neuronal Networks.

Tamura S, Nishitani Y, Hosokawa C, Miyoshi T, Sawai H - Comput Intell Neurosci (2016)

Bottom Line: That is, we may consider the spectrum curve as a "signature" of its associated neuronal network that is dependent on the characteristics of neurons and network configuration, including the weight distribution.That is, the proposed neural network model may adequately reflect the behavior of a cultured neuronal network.Furthermore, such prospect is discussed that code analysis will provide a base of communication within a neural network that will also create a base of natural intelligence.

View Article: PubMed Central - PubMed

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

ABSTRACT
It has been shown that, in cultured neuronal networks on a multielectrode, pseudorandom-like sequences (codes) are detected, and they flow with some spatial decay constant. Each cultured neuronal network is characterized by a specific spectrum curve. That is, we may consider the spectrum curve as a "signature" of its associated neuronal network that is dependent on the characteristics of neurons and network configuration, including the weight distribution. In the present study, we used an integrate-and-fire model of neurons with intrinsic and instantaneous fluctuations of characteristics for performing a simulation of a code spectrum from multielectrodes on a 2D mesh neural network. We showed that it is possible to estimate the characteristics of neurons such as the distribution of number of neurons around each electrode and their refractory periods. Although this process is a reverse problem and theoretically the solutions are not sufficiently guaranteed, the parameters seem to be consistent with those of neurons. That is, the proposed neural network model may adequately reflect the behavior of a cultured neuronal network. Furthermore, such prospect is discussed that code analysis will provide a base of communication within a neural network that will also create a base of natural intelligence.

No MeSH data available.


Related in: MedlinePlus

Expanded spectrum components up to E16. (a) Code spectrum components E2–E9 for a0 = 7 ms and c = 2. (b) Expansion to E9–E16 of (a). (c) Code spectrum components E2–E9 for a0 = 8 ms and c = 2.5. (d) Expansion to E9–E16 of (c).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4863095&req=5

fig10: Expanded spectrum components up to E16. (a) Code spectrum components E2–E9 for a0 = 7 ms and c = 2. (b) Expansion to E9–E16 of (a). (c) Code spectrum components E2–E9 for a0 = 8 ms and c = 2.5. (d) Expansion to E9–E16 of (c).

Mentions: In some cases, the best estimation of the probability distribution of Em had a large value at E9, suggesting that there are more than nine neurons around the electrode. Therefore, we increased the number m to 16. Figure 10 shows the expanded code spectral components up to E16. Although E2–E9 have various shapes, E9–E16 have a similar shape. When the best fit process included a large number of parameters, the computation time was long. Therefore, it may be reasonable to regard E9 as the representative component of E9–E16 to decrease the computation time.


Simulation of Code Spectrum and Code Flow of Cultured Neuronal Networks.

Tamura S, Nishitani Y, Hosokawa C, Miyoshi T, Sawai H - Comput Intell Neurosci (2016)

Expanded spectrum components up to E16. (a) Code spectrum components E2–E9 for a0 = 7 ms and c = 2. (b) Expansion to E9–E16 of (a). (c) Code spectrum components E2–E9 for a0 = 8 ms and c = 2.5. (d) Expansion to E9–E16 of (c).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4863095&req=5

fig10: Expanded spectrum components up to E16. (a) Code spectrum components E2–E9 for a0 = 7 ms and c = 2. (b) Expansion to E9–E16 of (a). (c) Code spectrum components E2–E9 for a0 = 8 ms and c = 2.5. (d) Expansion to E9–E16 of (c).
Mentions: In some cases, the best estimation of the probability distribution of Em had a large value at E9, suggesting that there are more than nine neurons around the electrode. Therefore, we increased the number m to 16. Figure 10 shows the expanded code spectral components up to E16. Although E2–E9 have various shapes, E9–E16 have a similar shape. When the best fit process included a large number of parameters, the computation time was long. Therefore, it may be reasonable to regard E9 as the representative component of E9–E16 to decrease the computation time.

Bottom Line: That is, we may consider the spectrum curve as a "signature" of its associated neuronal network that is dependent on the characteristics of neurons and network configuration, including the weight distribution.That is, the proposed neural network model may adequately reflect the behavior of a cultured neuronal network.Furthermore, such prospect is discussed that code analysis will provide a base of communication within a neural network that will also create a base of natural intelligence.

View Article: PubMed Central - PubMed

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

ABSTRACT
It has been shown that, in cultured neuronal networks on a multielectrode, pseudorandom-like sequences (codes) are detected, and they flow with some spatial decay constant. Each cultured neuronal network is characterized by a specific spectrum curve. That is, we may consider the spectrum curve as a "signature" of its associated neuronal network that is dependent on the characteristics of neurons and network configuration, including the weight distribution. In the present study, we used an integrate-and-fire model of neurons with intrinsic and instantaneous fluctuations of characteristics for performing a simulation of a code spectrum from multielectrodes on a 2D mesh neural network. We showed that it is possible to estimate the characteristics of neurons such as the distribution of number of neurons around each electrode and their refractory periods. Although this process is a reverse problem and theoretically the solutions are not sufficiently guaranteed, the parameters seem to be consistent with those of neurons. That is, the proposed neural network model may adequately reflect the behavior of a cultured neuronal network. Furthermore, such prospect is discussed that code analysis will provide a base of communication within a neural network that will also create a base of natural intelligence.

No MeSH data available.


Related in: MedlinePlus