Yoshi Nishitani

Detection of M-sequences from spike sequence in neuronal networks

In circuit theory, it is well known that a linear feedback shift register (LFSR) circuit generates pseudorandom bit sequences (PRBS), including an M-sequence with the maximum period of length. In this study, we tried to detect M-sequences known as a pseudorandom sequence generated by the LFSR circuit from time series patterns of stimulated action potentials. Stimulated action potentials were recorded from dissociated cultures of hippocampal neurons grown on a multielectrode array. We could find several M-sequences from a 3-stage LFSR circuit (M3). These results show the possibility of assembling LFSR circuits or its equivalent ones in a neuronal network. However, since the M3 pattern was composed of only four spike intervals, the possibility of an accidental detection was not zero. Then, we detected M-sequences from random spike sequences which were not generated from an LFSR circuit and compare the result with the number of M-sequences from the originally observed raster data. As a result, a significant difference was confirmed: a greater number of “0–1” reversed the 3-stage M-sequences occurred than would have accidentally be detected. This result suggests that some LFSR equivalent circuits are assembled in neuronal networks.

Simulation of Code Spectrum and Code Flow of Cultured Neuronal Networks

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.

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 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.

Base of brain intelligence: Information flow in cultured neuronal networks and its simulation on 2D mesh network

Its important for understanding brain intelligence to investigate how the signal/ information is flown in neuronal network. We observed spike trains obtained by one-shot electrical stimulation with 8 × 8 multi-electrodes in cultured neuronal networks. Each electrode is considered to collect spikes from several neurons. We then constructed code flow maps as movies of the electrode array to observe the code flow especially of “1101” and “1011.” To quantify the flow, we calculated the cross-correlations of the maximum direction of the codes with lengths less than 8. Normalized cross-correlations in the maximum direction were almost constant irrespective of code. Thus, the analysis suggested that the local codes for electrode flow maintained the code shape to some extent and conveyed information in the neural network. Then we made simulation of such code flow, and could estimate rough characteristics of neurons including refractory period and distribution of connection weights between neurons.

Effect of correlating adjacent neurons for identifying communications: Feasibility experiment in a cultured neuronal network

Neuronal networks have fluctuating characteristics, unlike the stable characteristics seen in computers. The underlying mechanisms that drive reliable communication among neuronal networks and their ability to perform intelligible tasks remain unknown. Recently, in an attempt to resolve this issue, we showed that stimulated neurons communicate via spikes that propagate temporally, in the form of spike trains. We named this phenomenon “spike wave propagation”. In these previous studies, using neural networks cultured from rat hippocampal neurons, we found that multiple neurons, e.g., 3 neurons, correlate to identify various spike wave propagations in a cultured neuronal network. Specifically, the number of classifiable neurons in the neuronal network increased through correlation of spike trains between current and adjacent neurons. Although we previously obtained similar findings through stimulation, here we report these observations on a physiological level. Considering that individual spike wave propagation corresponds to individual communication, a correlation between some adjacent neurons to improve the quality of communication classification in a neuronal network, similar to a diversity antenna, which is used to improve the quality of communication in artificial data communication systems, is suggested.

Spike code and information flow in cultured neuronal networks and its simulation on 2D mesh network

Its important to investigate natural intelligence how the signal/ information is flown in neuronal network. We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multi-electrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We then constructed code flow maps as movies of the electrode array to observe the code flow especially of “1101” and “1011.” To quantify the flow, we calculated the cross-correlations of the maximum direction of the codes with lengths less than 8. Normalized cross-correlations in the maximum direction were almost constant irrespective of code. Thus, the analysis suggested that the local codes for electrode flow maintained the code shape to some extent and conveyed information in the neural network. Then we made simulation of such code flow.