Focal cooling of the brain has the potential to terminate epileptic discharges. Therefore, in the present report, we review our past studies and discuss the future perspectives of brain cooling as a potential therapy for intractable epilepsy. The experiments were performed on Sprague-Dawley rats under halothane anesthesia. After craniotomy, the cooling device (Peltier chip) was placed on the cortical surface or inserted in the hippocampus. Kainic acid was then injected into either the cortex or hippocampus to provoke epileptiform discharges (EDs). Cortical cooling was also applied in patients with intractable epilepsy. The EDs decreased during either cortical or hippocampal cooling (20-25°C) in rats. The EDs diminished, and neurotransmitters, such as glutamate, were observed to decrease during cooling in epileptic patients. Both the effectiveness and safety of focal cooling for intractable epilepsy were demonstrated. Based on these data, developments of implantable focal cooling device have been promoted.

Focal brain cooling is under investigation in clinical trials of drug resistant epilepsy. This method has been studied intensively in rodents, but more evidence from large animal studies is required. To provide evidence that focal brain cooling is a safe and effective therapeutic intervention for intractable focal epilepsy, we investigated the placement of a newly-developed cooling device over the motor cortex of animals and investigated whether focal brain cooling can prevent and/or terminate focal neocortical seizures without having a significant impact on behavior. Two cats (n = 2) and a macaque monkey (n = 1) were chronically implanted with an epidural focal brain cooling device over the somatosensory and motor cortex, with adjacent EEG electrodes and a micro-injection tube. Penicillin G (PG) was delivered via the tube for induction of local seizures. Recordings were performed under awake but weakly restrained conditions. The cats and monkey exhibited spontaneous seizures with repetitive epileptiform discharges after administration of PG, and these discharges decreased at 15°C cooling. The results of this study suggest that epidural focal brain cooling is a safe and effective potential treatment modality for intractable focal epilepsy.

To investigate the effect of focal brain cooling on seizure, spectral analysis of electroencephalography (EEG) was performed in anesthetized rats intracortically injected with penicillin G potassium. Penicillin G induced epileptic discharges (EDs) and increased high frequency EEG bands. Use of a cooling device suppressed EDs with an effect that depended on the brain temperature. Fast Fourier Transform (FFT) analysis indicated that cooling to 25°C terminated the higher frequency EEG activity in Beta bands, cooling to 20°C suppressed the power spectra for both Alpha and Beta waves, and cooling to 15°C completely suppressed all spectral bands. These results suggest that a focal cooling device can terminate EDs and suppress EEG frequency bands in a manner that is dependent on the brain temperature. These findings contribute to development of clinical treatment for patients with EDs.

The realization of minimally invasive epilepsy surgery treatment, our goal, needs the accurate excision of the focus in a minimally invasive way. We think that one of the best ways to achieve this is cryosurgery. However, cryosurgery is not applied to epilepsy and the effect of cryosurgery on brain tissue is not well known. In this paper, we investigated the effects of freezing brain tissue on the neuronal and circulatory functions by application to rat brains. We achieved cryosurgery with a rat under the several conditions. After these surgeries, these specimens were treated with H&E and TUNEL staining, and changes in the specimens were observed. These results show necrotization by cryosurgery and effects of cryosurgery on blood vessel.

We propose a subdural electrode array guided by a 0.3mm-diameter shape memory alloy guidewire for a minimally-invasive method of electrocorticogram recording. The measured electric characteristics showed that the proposed electrodes are compatible with the application of electrocorticogram recording. Somatosensory evoked potential was measured by the proposed method in the animal test in vivo. The results confirmed that the proposed electrode array is available for the electrocorticogram recording under a minimally invasive surgery.

Learning systems such as multi-layer feed-forward neural networks, wavelet networks and so on need appropriate learning data (input data and teaching output data). These methods are not so useful in case when we cannot get the appropriate learning data. Even in this case, it is not so difficult to evaluate the system output for arbitrarily applied input. The learning data of input-output pairs with their evaluations are easily obtained and thus is easily used for modeling the system. SOR (self-organizing relationship) network is a modeling tool, which can be established by a set of input-output data and corresponding evaluation. This SOR network can act as a knowledge acquisition system and also act as a fuzzy inference engine. The linkage among the units in competitive layer is fixed and not flexible, and thus not used for complicated systems. In this plenary talk, the advanced learning process is presented for the original SOR network by employing evaluation-based TRN (topology representing network). By this learning, the linkage among the units in the competitive layer can be more flexible and thus used for modeling of much more complicated systems. The application of the SOR network established by this learning process to a manipulation control is also presented.

In this paper, we propose a personal identification method using sole information. We employ pressure changes of sole in walking. First, we do a preliminary examination using a load distribution sensor. We employ a neural network for a personal identification method. As the result, we show a possibility of an identification system by sole information using the load distribution sensor. Based on the result, we propose a personal identification method by sole pressure changes using three air pressure sensors. This method could identify one of five volunteers at 85.4% recognition rate.

In recent years, accumulating evidence suggests that Astrocytes type glial cells do much more than supporting neurons. In fact, it is shown that astrocytes communicate, influence and modulate neurons activity, and thus directly affect the information processing in the CNS. In the present article we propose to implement such a modulation in the standard Self-Organizing Map (SOM). We introduce an interactive adaptation/correction behavior by involving SOM's internal structure in the learning process, allowing the final weight distribution to be invariant to the initial conditions and free of any folding effect due to the well known border effect problem in standard SOM.

We propose a new fast learning algorithm for SOM and its digital hardware design based on the massively parallel architecture. When this proposed algorithm is realized by using Xilinx XC2V6000-6 FPGA, a maximum performance of 17500 MCUPS is achieved and up to 256 competing units (16 /spl times/ 16 map) can be implemented. Each competing unit have a weight vector which is represented by 128 elements of 16 bits accuracy. Furthermore, we applied the proposed hardware to a realtime digital image enlargement system. In the case of full color (24 bits) image enlargement from QQVGA (160 /spl times/ 120 pixel) to QVGA (320 /spl times/ 240 pixel), a proposed hardware requires only 0.12 second per image, while the personal computer (Intel XEON, 2.8 GHz Dual) requires more than 5 seconds per image.

Recently, the Japanese government designated 246 university departments as 21th Century Centers of Excellence (COE's) covering almost all fields of science and technology. Two COE's have been selected for the area relevant to this special session. One is formed by the Brain Science Center and other departments of Tamagawa University, and is led by M. Tsukada with the project title being "Integrative Human Science Program". The other COE is the Department of Brain Science and Systems Engineering of Kyutech, being led by T. Yamakawa. Its project title is "World of Brain Computing Interwoven out of Animals and Robots". Both COE's are engaged in biology experiments as well as engineering and computational studies.

Sensor orientation based on a 3D affine transformation is appealing for high-resolution satellite imagery for three reasons: 1) Narrow-angle imaging sensors can be characterised by a skew parallel projection, 2) the affine model is linear, and 3) it is a straightforward computational model requiring a minimum of only four ground control points. Application of the affine model with Ikonos imagery has produced sub-pixel 3D geopositioning accuracy. In spite of this, there remain questions about the fidelity of the model where the area covered is large and where there is a significant terrain height variation. This paper describes the application of the affine model to multi-scene Ikonos stereo imagery covering large block areas. It is shown that area is not a factor influencing the impressive metric performance of the affine for pixel-level geopositioning.

In this paper, a feedback self-organizing map (FSOM), which is an extension of the self-organizing map (SOM) by employing feedback loops, is proposed. The SOM consists of an input layer and a competitive layer, and the input vectors applied to the input layer are mapped to the competitive layer keeping their spatial features. In order to embed the temporal information to the SOM, feedback loops from the competitive layer to the input layer are employed. The winner unit in the competitive layer is not assigned by only current input vector but also past winner units, thus the temporal information can be embedded. The effectiveness and validity of the proposed FSOM are verified by applying it to a spatio-temporal pattern classification.

This paper describes an identification method of a landmark in a roentgenographic cephalogram by employing the input-correlated wavelet neurons. For the purpose of improvement of identification precision, a novel pattern matching method, named "wavelet neuron matching (WNM)," is proposed in this paper. Furthermore, the "weighted window", which is proposed in this paper, enables us to consider the orthodontists' knowledge on local information as precisely as possible. The effectiveness and the validity of the proposed method have been verified by the experiments to identify a landmark called Menton.

In this paper, a nonequilibrium network which works as a dynamical associative memory is designed. The design is based on a new similarity measure between any stored pattern and a state of the network. Although conventional similarity measures, such as Hamming distance, direction cosine, and so on, are not detectable in a cross-coupled network, the similarity measure proposed in this paper is. The new similarity measure is employed in our design. The network should include the following properties in its output pattern sequence, so that the dynamics of cross-coupled network may be designed: 1) Stored patterns are frequently associated in the dynamical association. 2) The dynamical association is very robust against variation of distributed parameters. Property 1) is achieved by introducing the next two operation modes with inverse N-shaped function into the dynamics of the proposed network. 1) When the state of the network is close enough to a stored pattern at a time step, the state is forced to evolve at the next time step. 2) The state of the network converges to a stored one while it is not close to any stored patterns. By considering these two operation modes, the frequency of associating stored patterns is increased. The authors emphasize the property 2) which is very important for a silicon implementation of the proposed network. In the silicon implementation, parameters of the network must be represented by transistors, resistors, capacitors, and other electric components which exhibit variation in their characteristics. Thus the second property guarantees the easy silicon implementation of the nonequilibrium network proposed in this paper.

The user intuition of an image is very important factor to evaluate the image. In this paper, the new image enhancement method, which provides the enhanced image satisfying the intuitive evaluation of the user, is proposed. The self-organizing relationship (SOR) network proposed by the authors is employed in order to obtain the relationship, which reflects the user intuition, between intensity histogram of an original image and intensity mapping curve. The SOR network can construct the relationship, which corresponds to the arbitrary evaluation, between input vector and output vector by the learning. Employing the user intuition as the evaluation, the relationship corresponding to the user intuition is obtained. The intensity histogram of original images and the intensity mapping curve are employed as the input vector and the output vector of the SOR network, the relationship between them is constructed by SOR network. The intensity histogram of an image is applied to the SOR network after the learning, the intensity mapping curve which reflects the user intuition is generated.

We propose new method of the independent component analysis (ICA), which doesn't require to know the classes of the distribution of the sources beforehand in contrast with conventional methods and achieves separation of sources with higher precision than the conventional methods. The proposed method employs the self-organizing maps (SOM) to the Bell-Sejnowski's method (1995) for the purpose of making use of the ability of SOM to approximate the probability density. SOM grasps the probability density of the input signals by nature. It can also track the changes of the probability density of the input signal adaptively. In this paper, the effectiveness and validity of the proposed method are verified by applying it to the separation of linearly mixed sounds and linearly mixed pictures by the computer simulations.

We propose novel FIR-OS hybrid type filter employing the neo fuzzy neuron, and frameworks of a linear FIR filter and an order statistic (OS) filter, aiming at elimination of a Gaussian noise and an impulsive noise at the same time, and high restoration of the signal, simultaneously. The proposed filter is synthesized by learning method which guarantees optimal design caused by employing the neo fuzzy neuron (NFN) model. In this paper, the effectiveness and validity of the proposed filter are verified by applying it to the filtering of the noisy images.

This paper deals with the so-called general machine scheduling problems. In the general machine scheduling problems, job shop type jobs and open shop type jobs are scheduled together and the imposition of precedence constraints is allowed between operations belonging to either the same job or different jobs. This paper proposes a genetic algorithm to solve such general machine scheduling problems. Some experimental results are presented to show the applicability of the proposed method. The method can be used to solve traditional job shop scheduling, flow shop scheduling, and open shop scheduling as well as general machine scheduling problems.

The machine cell formation problem is about grouping machines into machine families and parts into part families so as to minimize bottleneck machines, exceptional parts and inter-cell part movements in cellular and flexible manufacturing systems. This paper proposes a new machine cell formation method based on the adaptive Hamming net, which is a neural network model. To see the applicability of the method, this paper shows some experimental results and compares the proposed method with other cell formation methods. From the experiments, we can see that the proposed method can produce good cells for the machine cell formation problem.

This paper describes a fuzzy neuron chip which is the modification of an ordinary neuron model by fuzzy logic. The algebraic product of scaler input and connective weights in synapse is replaced by a fuzzy inner product. An excitatory connection is represented by a MIN (minimum) operation and an inhibitory connection by fuzzy logic complement followed by a MIN operation. While an ordinary neuron model is established only by leaning, the fuzzy neuron can be designed and optimized by learning. The fuzzy neuron is implemented in silicon wafer by a standard BiCMOS process. The chip is applied to a handwritten character recognition system and it exhibits very high-speed recognition (less than 500 ns).

The paper describes a generalized fuzzy learning machine, which is a generalised and modified type of the neo-fuzzy-neuron presented by the authors in 1992. This machine can well grasp the nonlinear correlation of each input. It has a very high nonlinear mapping ability compared with the conventional neural network and it guarantees the global minimum. Furthermore, learning speed and its accuracy are improved drastically. It was successfully applied to the identification of the nonlinear dynamical system, e.g. two dimensional Lorenz chaotic model, and to the automatic detection of landmark location in the roentgenographic cephalogram for orthodontic treatment. The results were promising.

A brief survey on fuzzy information processing hardware systems was presented in this paper. The distinctive features of those systems depend upon how, where, by whom and for what they are to be utilized. The attempt is continued to be made to search for novel devices or architectures which facilitate high speed, high efficiency and low cost of inference.

This paper introduces a new approach to system modeling by using a neo-fuzzy-neuron. The system of concern is modeled adaptively by simply feeding to the neo-fuzzy-neuron, the basic principle of which was proposed by the authors in 1992, the input and the output data of the objective system. Firstly, the neo-fuzzy-neuron is applied to the restoration of a saturated and/or intermittent speech or chaotic signal to show its actual effectiveness. It is then extended in order to get a better generalization capability. An adaptive fuzzy modeling with use of a piece-wise linear membership function is also introduced. The experimental results have provided substantial proofs for their practical use.>

Evaluation of the results of orthodontic treatment is crucial for the improvement of the practitioner's therapeutic skills. Conventional methods of appraisal are based on ill-defined subjective criteria, however, or fail to make their criteria explicit. In order to provide a more objective appraisal, we surveyed 10 experienced practitioners regarding their criteria for evaluating 12 items such as the overjet, or the amount of horizontal projection of the upper front teeth beyond the lower ones. For each item, their opinions were averaged as the definition of membership in the category of “completely favorable.” Algorithms were devised for comparing the posttreatment results with the pre-treatment condition of need, in order to evaluate the degree of therapeutic success. the average of all 12 evaluations is taken as the appraisal of treatment. A hardware system incorporating these algorithms has been developed. This system, representing the first successful application of fuzzy inference to the evaluation of orthodontic treatment, is expected to provide orthodontists with an accurate and objective means of making such an appraisal and thereby help improve their therapeutic skills. © 1994 John Wiley & Sons, Inc.

In this tutorial, the utility of a fuzzy system is demonstrated by providing a broad overview, emphasizing analog mode hardware, along with a discussion of the author's original work. First, the difference between deterministic words and fuzzy words is explained as well as fuzzy logic. The description of the system using mathematical equations, linguistic rules, or parameter distributions (e.g., neural networks) is discussed. Fuzzy inference and defuzzification algorithms are presented, and their hardware implementation is discussed. The fuzzy logic controller was used to stabilize a glass with wine balanced on a finger and a mouse moving around a plate on the tip of an inverted pendulum.

Nine basic fuzzy logic circuits employing p-ch and n-ch current mirrors are presented, and the fuzzy information processing hardware system design at a low cost with only one kind of master slice (semicustom fuzzy logic IC) is described. The fuzzy logic circuits presented here will be indispensable for a "fuzzy computer" in the near future.