Fuzzy Bio Fuzzy Bio Fuzzy Bio Fuzzy Bio----interface: Can fuzzy set interface: Can fuzzy set interface: Can fuzzy set interface: Can fuzzy sets s s s be an interface with be an interface with be an interface with be an interface with brain? brain? brain? brain? IsaoHayashi Faculty of Informatics Kansai University
569-1095 Takatsuki Osaka JAPAN
Faculty of Informatics Kansai University
569-1095 Takatsuki Osaka JAPAN
SuguruNKudoh Faculty of Informatics Kansai University
569-1095 Takatsuki Osaka JAPAN
School of Science and Technology Kwansei Gakuin University
669-1337 Sanda Hyogo JAPAN
Faculty of Informatics Kansai University
569-1095 Takatsuki Osaka JAPAN
School of Science and Technology Kwansei Gakuin University
669-1337 Sanda Hyogo JAPAN
School of Science and Technology Kwansei Gakuin University
669-1337 Sanda Hyogo JAPAN
School of Science and Technology Kwansei Gakuin University
669-1337 Sanda Hyogo JAPAN
Fuzzy Bio Fuzzy Bio Fuzzy Bio Fuzzy Bio----interface: Can fuzzy set interface: Can fuzzy set interface: Can fuzzy set interface: Can fuzzy sets s s s be an interface with be an interface with be an interface with be an interface with brain? brain? brain? brain? 0002A619E2BD6B78917C05060C7BB649 GROBID - A machine learning software for extracting information from scholarly documents

connectives, that consist of both t-norms and t-conorms [9,10], in order to analyze those three electrodes (Figure 1). We have obtained the experimental result such that the parameter(s) of fuzzy connectives become infinity. Given this result, we conclude that a pulse at the 60th channel (60el) propagates to the spreading area:

(51el, 59el), (43el, 50el) and (35el, 42el); and that the logic of signals among the electrodes was shifted to the logical sum from the drastic product.

Consequently, the logic of signals among electrodes drastically changes from the strong AND-relation to the weak OR-relation when a crowd of the pulses was fired.

through a device such as near-infrared spectroscopy (NIRS) and electroencephalograph (EEG), and some discriminant model determines a control process. However, under the condition where spontaneous action-potentials and evoked-action potentials are contained in brain signal asynchronously, we need a model that serves as an interface between brain and machine for a better stable control in order to prevent runaway reaction of machine. This interface plays a very important role to secure the stability of outer computer and machine. The interface has two kinds of functions: (1) a decoding of the response action potentials to the control signal of outside machine and computer, and (2) an encoding of the sensor signal of the outside machine and computer to pattern of stimuli in brain and neuronal networks. Unfortunately, it is very difficult to identify such a function for the interface between machine and living brain and neuronal networks. Here we consider such an interface within the framework of fuzzy system. As a result, our study is supportive of this framework as a strong tool of the bio-interface. During the Japanese fuzzy boom in 1990's, fuzzy logic has been proven effective to translate human experience and sensitivity into control signals of machines. Tsukamoto[3] has argued a concept of fuzzy interface such that fuzzy sets is regarded as a useful tool to intermediate between language and mathematics. We believe that the framework of fuzzy system is essential for BCI and BMI, thus name this technology "fuzzy bio-interface." In this lecture, we introduce a fuzzy bio-interface between a culture dish of rat hippocampal neurons and the khepera robot. We propose a model to analyze logic of signals and connectivity of electrodes in a culture dish[4], and show the bio-robot hybrid we developed[5,6]. Rat hippocampal neurons are organized into complex networks in a culture dish with 64 planar microelectrodes. A multi-site recording system for extracellular action potentials is used in order to record their activities in living neuronal networks and to supply input from the outer world to the vitro living neural networks. The living neuronal networks are able to express several patterns independently, and such patterns represent fundamental mechanisms for intelligent information processing[7]. First, we discuss how to indicate the logicality and connectivity from living neuronal network in vitro. We follow the works of Bettencourt et al.[8] such that they classify the connectivity of action potentials of three electrodes on multi-site recording system according to their entropies and have discussed the characteristic of each classification. However, they only discuss the static aspects of connectivity relations among the electrodes but not the dynamics of such connectivity concerning how the strength of electrode connection changes when a spike is fired. To address this issue, we develop a new algorithm using parametric fuzzy
Figure 1 :Figure 1: Algorithm to Analyze Action Potentials in Cultured Neuronal Network

ACKNOLEDGEMENT ACKNOLEDGEMENT ACKNOLEDGEMENT ACKNOLEDGEMENT I would like to express my gratitude to my collaborators, Megumi Kiyotoki, Kansai University, Japan and Ai Kiyohara, Minori Tokuda, Kwansei Gakuin University, Japan. This work is partially supported by the Ministry of Education, Culture, Sports, Science, and Technology of Japan under Grant-in-Aid for Scientific Research 18500181, 19200018, and 18048043 and by the Organization for Research and Development of Innovative Science and Technology (ORDIST) of Kansai University.

The aim of his research is to elucidate relationship between dynamics of neuronal network and brain information processing. He analyses spatio-temporal pattern of electrical activity in rat hippocampal cells cultured on multi-electrode arrays or acute slice of basal ganglia. He is also developing Bio-robotics hybrid system in which a living neuronal network is connected to a robot body via control rules, corresponding to agenetically provided interfaces between a brain and a peripheral system. He believes that mind emerges from fluctuation of dynamics in hierarchized interactions between cells.

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