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https://ceur-ws.org/Vol-710/talkHayashi.pdf
Fuzzy Bio-
Bio-interface: Can fuzzy sets
sets be an interface with brain?
Isao Hayashi † and Suguru N. Kudoh ‡
† Faculty of Informatics, Kansai University, Takatsuki, Osaka 569-1095, JAPAN
‡ School of Science and Technology, Kwansei Gakuin University, Sanda, Hyogo 669-1337, JAPAN
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
ABSTRACT framework of fuzzy system is essential for BCI
Recently, many attractive brain-computer and BMI, thus name this technology “fuzzy
interface and brain-machine interface have been bio-interface.”
proposed[1,2]. The outer computer and machine In this lecture, we introduce a fuzzy
are controlled by brain action potentials detected bio-interface between a culture dish of rat
through a device such as near-infrared hippocampal neurons and the khepera robot. We
spectroscopy (NIRS) and electroencephalograph propose a model to analyze logic of signals and
(EEG), and some discriminant model determines connectivity of electrodes in a culture dish[4], and
a control process. However, under the condition show the bio-robot hybrid we developed[5,6]. Rat
where spontaneous action-potentials and hippocampal neurons are organized into complex
evoked-action potentials are contained in brain networks in a culture dish with 64 planar
signal asynchronously, we need a model that microelectrodes. A multi-site recording system for
serves as an interface between brain and machine extracellular action potentials is used in order to
for a better stable control in order to prevent record their activities in living neuronal networks
runaway reaction of machine. This interface plays and to supply input from the outer world to the
a very important role to secure the stability of vitro living neural networks. The living neuronal
outer computer and machine. The interface has networks are able to express several patterns
independently, and such patterns represent connectives, that consist of both t-norms and
fundamental mechanisms for intelligent t-conorms[9,10], in order to analyze those three
information processing[7]. electrodes (Figure 1). We have obtained the
First, we discuss how to indicate the experimental result such that the parameter(s) of
logicality and connectivity from living neuronal fuzzy connectives become infinity. Given this
network in vitro. We follow the works of result, we conclude that a pulse at the 60th
Bettencourt et al.[8] such that they classify the channel (60el) propagates to the spreading area:
connectivity of action potentials of three (51el, 59el), (43el, 50el) and (35el, 42el); and that
electrodes on multi-site recording system the logic of signals among the electrodes was
according to their entropies and have discussed shifted to the logical sum from the drastic product.
the characteristic of each classification. However, Consequently, the logic of signals among
they only discuss the static aspects of connectivity electrodes drastically changes from the strong
relations among the electrodes but not the AND-relation to the weak OR-relation when a
dynamics of such connectivity concerning how the crowd of the pulses was fired.
strength of electrode connection changes when a
spike is fired. To address this issue, we develop a
new algorithm using parametric fuzzy
Figure 1: Algorithm to Analyze Action Potentials in Cultured Neuronal Network
Next, to control a robot, several
characteristics of the living neuronal networks
are represented as fuzzy IF-THEN rules. There
are many works of robots that are controlled by
the responses from living neuronal
networks[11-15]. Unfortunately, they have not
yet achieved a certain task that experimenter
desired. We show a robot system that controlled
by a living neuronal network through the fuzzy
bio-interface in order to achieve such a task
(Figure 2). This fuzzy bio-interface consists of two
sets of fuzzy IF-THEN rules: (1) to translate
sensor signals of robot into stimuli for the living
neuronal network, and (2) to control (i.e. to
determine the action of) robot based on the
responses from the living neuronal network. We
estimated the learning of living neuronal
networks with an example of straight running
with neuro-robot hybrid. Among 20 trials, the
robot completed the task 16 times, and it crashed
on the wall and stopped there 4 times. In this
result, we may conclude that the logic of signals
among living neuronal networks represented as Figure 2: Living Neuronal Network and Robot
fuzzy IF-THEN rules for the fuzzy bio-interface is
rather efficient and effective comparing to the
other similar works. In such works, the success
rate of 80% is considered extremely high. Ensemble Adaptation to Represent Velocity
of an Artificial Actuator Controlled by a
ACKNOLEDGEMENT Brain-machine Interface, Journal of
I would like to express my gratitude to my Neuroscience, Vol.25, No.19, pp.4681-4693,
collaborators, Megumi Kiyotoki, Kansai 2005.
University, Japan and Ai Kiyohara, Minori [2] L.R.Hochberg, M.D.Serruya, G.M.Friehs,
Tokuda, Kwansei Gakuin University, Japan. This J.A.Mukand, M.Saleh, A.H.Caplan,
work is partially supported by the Ministry of A.Branner, D.Chen, R.D.Penn,
Education, Culture, Sports, Science, and J.P.Donoghue: Neuronal Ensemble Control of
Technology of Japan under Grant-in-Aid for Prosthetc Devices by a Human with
Scientific Research 18500181, 19200018, and Tetraplegia, Nature, Vol.442, pp.164-173,
18048043 and by the Organization for Research 2006.
and Development of Innovative Science and [3] Y.Tsukamoto: Fuzzy Sets as an Interface
Technology (ORDIST) of Kansai University. between Language Model and Mathmatics
Model, Proc. of the 24th Fuzzy System
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Neuronal Network and Outer World, Journal
of Robotics and Mechatronics, Vol.19, No.5, BIOGRAPHICAL SKETCH
pp.592-600, 2007. Isao Hayashi is Professor of Informatics at
[7] S.N.Kudoh and T.Taguchi: Operation of Kansai University, Japan. After he received his
Spatiotemporal Patterns Stored in Living B.Eng. degree in Industrial Engineering from
Neuronal Networks Cultured on a Osaka Prefecture University, he worked at Sharp
Microelectrode Advanced
Array, Corporation, Japan. After he received his M.Eng.
Computational Intelligence and Intelligent degree from Osaka Prefecture University in 1987,
Informatics, Vol.8, No2, pp.100-107, 2003. he was a Senior Research Fellow of the Central
[8] L.M.A.Bettencourt, G.J.Stephens, M.I.Ham, Research Laboratory of Matsushita Electric
and G.W.Gross: Functional Structure of Industrial (Panasonic) Co. Ltd and proposed a
Cortical Neuronal Networks Grown in Vitro, neuro-fuzzy system on intelligent control.
Phisical Review, Vol.75, p.02915, 2007. He received his D.Eng. degree based on
[9] B.Schweizer and A.Sklar: Associative his contributions to the neuro-fuzzy model from
Functions and Statistical Triangle Osaka Prefecture University in 1991. He then
Inequalities, Publicationes Mathematicae joined Faculty of Management Information of
Debrecen, Vol.8, pp.169-186, 1961. Hannan University in 1993 and joined Faculty of
[10] I.Hayashi, E.Naito, and N.Wakami: Proposal Informatics of Kansai University in 2004. He is
for Fuzzy Connectives with a Learning an editorial member of International Journal of
Function Using the Steepest Descent Hybrid Intelligent Systems, Journal of Advanced
Method, Japanese Journal of Fuzzy Theory Computational Intelligence and Intelligent
and Systems, Vol.5, No.5, pp.705-717, 1993. Informatics, and has served on many conference
[11] D.J.Bakkum, A.C.Shkolnik, G.Ben-Ary, program and organizing committees. He is the
P.Gamblen, T.B.DeMarse, and S.M. Potter: president of Kansai Chapter of Japan Society for
Removing Some `A' from AI: Embodied Fuzzy Theory and Intelligent Informatics (SOFT),
Cultured Networks, in Embodied Artificial and the chair of the Technical Group on Brain
Intelligence, editered by F.Iida, R.Pfeifer, and Perception in SOFT. He research interests
L.Steels, and Y.Kuniyoshi, New York, include visual models, neural networks, fuzzy
Springer, pp.130-145, 2004. systems, neuro-fuzzy systems, and
[12] T.B.DeMarse and K.P.Dockendorf: Adaptive brain-computer interface.
Suguru N. Kudoh received his Master‘s
degree in Biophysical Engineering in 1995 and
PhD from the Osaka university in 1998. He was a
research fellow of JST(Japan science and
technology agency) from 1997 to 1998, and a
research scientist of National Institute of
Advanced Industrial Science and Technology
(AIST) from 1998 to 2009. Now he is an associate
professor at Kwansei Gakuin 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.