brain science of the mind

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Brain science of the mind

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Brain science of the mind

Tsutomu NakadaUniversity of Niigata, Japan

Abstract

The human cerebral cortex contains more than one hundred billion neurons and 1014

synapses. Even without regard to the size of the genome, it can be easily deduced that a deterministic blueprint for connectivity of such an enormous number of networks is unrealistic. Existing scientific knowledge indicates that nature utilizes principal rules instead of complete deterministic descriptions to fashion a desired structure, namely, the rules of self-organization. The brain is a complex system and self-organizes based on the Markovian process. Accordingly, brain functionality can be seen as specific patterns created by self-organizing processes based on conditions defined by genes and the environment. Three categorically different systems are now recognized based on their physiological functional unit configuration. While the oldest system is made up of deterministic connectivity, the remaining two systems, namely, cerebellum and cerebrum, utilize modifiable units, often referred to as cerebellar chip and brain chip, respectively. Classical conditioning, as in the case of Pavlov’s dog, is now recognized to be based on functionality of the cerebellum and its learning unit, the cerebellar chip, which in principle works as McCulloch-Pitts neurons. The cerebrum utilizes the concept of Kohonen’s non-linear self-organizing map in the organization of the brain chip, a system that effectively creates entropy fields. In contrast to cerebellar learning which is adaptive, cerebral learning is stochastic in nature, and follows the rule known as Pólya’s Urn.

Keywords

brain chip, cerebellar chip, complex system, Kohonen, McCulloch-Pitts, Pauling, Pólya, vortex theory of the brain

Résumé

Le cortex cérébral humain contient plus de 100 billions de neurones et 1014 synapses. Même sans prendre en compte la taille du génome, on peut en déduire aisément qu’un plan détaillé déterministe des connexions d’un nombre aussi énorme de réseaux est irréaliste. Les connaissances scientifiques actuelles indiquent que la nature utilise de

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Corresponding author:Tsutomu Nakada, Center for Integrated Human Brain Science, Brain Research Institute, University of Niigata, 1 Asahimachi, Chuoh-ku, Niigata 951–8585, Japan.Email: [email protected]

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26 Social Science Information 50(1)

grandes règles générales plutôt que des descriptions déterministes complètes pour façonner une structure requise, à savoir des règles d’auto-organisation. Le cerveau est un système complexe et s’auto-organise selon un processus Markovien. En conséquence, les fonctionnalités du cerveau peuvent être vues comme des schémas spécifiques issus de processus auto-organisateurs à partir de conditions définies par les gènes et l’environnement. Trois systèmes catégoriquement différents sont désormais identifiés à partir de la configuration de leurs unités physiologiques fonctionnelles. Alors que le système le plus ancien résulte d’une connectivité déterministe, les deux autres systèmes, à savoir le cervelet et le cerebrum, utilisent des unités modifiables auxquelles on se réfère respectivement comme puce cérébelleuse (cerebellar chip) et puce cérébrale (brain chip). Il est maintenant communément reconnu que le conditionnement classique – tel celui du chien de Pavlov – est basé sur les fonctionnalités du cervelet et de son unité d’apprentissage, la puce cérébelleuse, qui fonctionne en principe comme les neurones de McCulloch-Pitts. Le cerebrum utilise une carte auto-organisatrice non-linéaire – selon le concept de Kohonen – pour l’organisation de la puce cérébrale, un système qui créée effectivement des champs d’entropie. Par opposition à l’apprentissage cérébelleux, qui est adaptatif, l’apprentissage cérébral est par nature stochastique et suit la règle dite de l’Urne de Polya.

Mots-clés

puce cérébrale, puce cérébelleuse, système complexe, Kohonen, McCulloch-Pitts, Pauling, Polya, théorie du vortex

Introduction

Since ancient times, physical definition of the mind has been a consistent theme of intellec-tual discussion. In spite of serious efforts by renowned scholars, scientific description of the mind remained metaphysical. A logical theorem has to start with axioms. Therefore, as long as the axiomatic expressions for thinking processes are metaphysical, the resultant logic is also metaphysical. Clearly, science of the mind has suffered from a lack of proper axioms.

The physical counterpart of the mind is activities of the brain. It is natural that studies have focused on neural activities in attempts to understand the mind. The success of Hodgkin and Huxley brought considerable enthusiasm to neuroscientists and, indeed, sig-nificant advances have been made in the form of electrophysiology (Hodgkin & Huxley, 1952). Were detailed analysis of neural activities of the brain sufficient to uncover the won-ders of the mind, science of the mind would have been concluded by the end of the twenti-eth century. Unfortunately, such has not been the case. Efforts based on the supposition that deterministic description of neural connectivity is responsible for brain higher functionality have thus far obviously failed in elucidating the biophysical substrate of the mind.

Classical human brain science began with the description of expressive aphasia by the French neurosurgeon Pierre Paul Broca. Since then, research in the field of higher func-tion has been focused on pinpointing functions to specific brain regions, the concept of so-called localizationism. Extreme localizationists believe that the brain can eventually be understood as the linear sum of predetermined functional units. Introduction of functional



Nakada 27

magnetic resonance imaging (fMRI) accentuated the blind enthusiasm of localizationists. The concept that there is specific deterministic circuitry in the brain responsible for a given functionality is, however, virtually identical to the concept of a ‘grandmother cell’ (a hypothetical neuron that represents a complex but specific concept or object), which has been repeatedly refuted in the world of neurophysiology.

The human genome project demonstrated that the human genome contains information only approximately twice that of a fruit fly. The relative scarcity of genes confirms the supposition that no absolutely deterministic genetically encoded blue print exists for cortical connectivity. Clinically, it is well known that cortical areas believed to be respon-sible for specific functions in normal individuals are in fact not so specifically dedicated. Individuals with regional brain compromise often demonstrate normal functionality without having the prerequisite anatomic regions of normal individuals.

The cumulative data strongly indicate that functional localization represents the result of cortical self-organization and does not represent predefined deterministic functional blocks of the brain (Figure 1). Under similar initial and boundary conditions, similar results will appear. This strongly implies that self-organizing processes responsible for creating functional localization, and not localized functionality itself, should be the main target of brain science.

Given the brain represents a complex system, it is implicit that neural processing creat-ing functionality is non-linear. It follows that retrograde analysis from the end point of functionality to the basic element of the self-organizing processes is virtually impossible, since reversal of a single process does not necessarily reproduce the state prior to the given process. As illustrated by Markovian processes (memory less stochastic processes), the rule for self-organization is simple. Conditions, and not complex algorithms, are responsible for

Figure 1. Functional MRI of IgoIgo, also popularly known as Go, is one of the most popular board games in the Orient. Neural processes that support the ability to play a full game of Igo are unquestionably complex. However, a set of problem-solving patterns at the corner of the board, called shikatsu-mondai, has been shown to require much simpler cognitive processes (see http://www.usgo.org). Given a shikatsu-mondai task, a beginner activates a rather large area of the cortex to solve the problem. By contrast, an expert can solve the problem utilizing only a small area within the parietal lobe around the intraparietal sulcus. Technical details of functional magnetic resonance imaging (fMRI) can be found in Nakada et al. (1988, 2008).




resultant, self-organized complex functionality. If one understands the basis of a complex system, therefore, one should be able to identify the basic functional unit within the cerebral cortex responsible for and capable of creating the complex activities we commonly refer to as the ‘mind’.

The core architecture of the nervous system is deterministic, the principal function of which is virtually identical to that of arithmetic logical gates (Figure 2). By contrast, a mammal’s capability for learning indicates that the mammalian brain is composed of modifiable, rather than deterministic neural networks. In order to understand the mind, therefore, one must understand the architectural configuration of modifiable functional units. It is now known that there are two kinds of such neural networks, namely, the unit composing the cerebellar cortex and the unit composing the cerebral cortex. The former behaves adaptively, whereas the latter is stochastic.

Modifiable functional unit

Adaptive: cerebellar chip

Classical conditioning, first demonstrated by Pavlov in his famous dog, represents a prim-itive form of memory formed by repetitive learning processes. It is now known that the cerebellum plays a principal role in the acquisition of classical conditioning (Thompson & Steinmetz, 2009). The modifiable neural network system of the cerebellum responsible for this form of learning is often described as the cerebellar chip, termed to parallel its engi-neering counterpart, the perceptron, the principle of which is described by McCulloch and Pitts (Figure 3). The concept is now widely utilized in the field of neural nets (Arbib, 2002).

A simplified representation of the cerebellar chip is shown in Figure 4. The chip is organ-ized around a single output neuron, the Purkinje cell. Information reaching the cerebellum

Figure 2. Arithmetic logical gatesAn example of the simplest gate, AND, is shown: • represents the ‘off’ state, and ° the ‘on’ state. In this arithmetic logical gate, output will be on only if/when both inputs are on. Accordingly, the gate is termed AND. One can create any deterministic gate. Inputs can be multiple as in the case of the McCulloch–Pitts neuron shown in Figure 3.



Nakada 29

Figure 3. McCulloch–Pitts neuronAt given time t, input signals x

i(t) reach the synapses. Each input is transmitted through the synapse to the

neuron after each has been modified by weight wi. When the sum of the inputs reaches threshold θ, the

output of the neuron at time t + 1, y(t + 1), will become 1. A learning trigger is explicitly added to parallel the cerebellar chip shown in Figure 4.

( 1)( )

( )y t

if wixi t

if wixi t

1

0 <i

i

$ i

i+ = *

//

Figure 4. Cerebellar chipFunctionally, the cerebellum is now considered to be an organ formed collectively by identical functional units. The individual unit is termed ‘cerebellar chip’, by analogy to a computer chip. Each cerebellar chip has a single output neuron, the Purkinje cell. Information to the cerebellum is first processed by numerous preprocessing neurons, such as the granular cells, and eventually reaches the Purkinje cells via parallel fiber (PF) input. The transmission efficacy of the synapses between parallel fibers and Purkinje cells is modifiable, forming the basis of synaptic plasticity, and provides the substrate of the cerebellar learning process. The outcome of cerebellar-system output is examined in other systems, and error signals are fed back to each Purkinje cell if the outcome is undesirable. These error signals are carried by the climbing fiber (CF) and provide the learning trigger signal (LTS) for the corresponding Purkinje cells. Once a desirable outcome is achieved, the error signals will cease, and the learning process will be put on hold, similar to the situation where the hold command is given to the McCulloch–Pitts neuron.




Figure 5. Example of the physiological role of the adaptive systemA control system for visuovestibular interaction and vestibule-ocular reflex (VOR) is shown. The effectiveness of parallel fiber transmission to the Purkinje cells is considered modifiable by climbing fiber input. The retinal error signal, e, reaches the inferior olivary nucleus (ION) via the nucleus of the optic tract (not). Through their climbing fiber discharges, the ION controls the modifiable gain element, K*, for VOR adaptation. The transfer functions Gc, Go and Gp can be expressed as: Gc = Tcs/(l + Tcs), Go = l/(1 + Tcs) and Gp – l, respectively, where s and Tc represent the Laplace operator and cupula time constant respectively. The Golgi-granule cell system can be considered to be a leaky integrator with a time constant Tg. For simplification, transmission efficacy of input signals to the granule cell is considered to be uniformly 1.0. Accordingly, a very simplified transfer function of the Golgi-granule cell system can be given as: Gg = Tgs/(2 + Tgs). The characteristic function of this system is given as: P(s) = 2TcTgs2 + [2Tc + Tg(l – Kv – K*)]s + 2(1 – Kv). Stability analysis of the system using theRouth–Hurwitz criterion indicates that the system becomes unstable when Ko = Kv + K* > 1 + 2Tc/Tg.It follows that to maintain stability of automatic eye movement, constant input of error signals and cerebellar modification by the gain element is essential. Without proper function of the adaptive modifiable unit of the cerebellum, the individual will have abnormal eye oscillations (nystagmus). See Nakada & Kwee (1986) for further details.

is first processed by many so-called pre-processing neurons such as the granular cells. The output of these pre-processing neurons reaches the Purkinje cells via the parallel fibers and forms synaptic connections with dendrites of the Purkinje cells. Transmission efficacy of the synapses between parallel fibers and the Purkinje cells is modifiable, forming the basis of synaptic plasticity, and provides the biologic substrate of the cerebellar learning process. The role of transmission efficacy in the learning process is analogous to the variable weights in the learning process of the McCulloch-Pitts neuron (Figure 3).

This type of learning is adaptive and is most frequently utilized in the nervous system for adjusting the gain of precise motion feedback in automatic control systems (Ito, 2001). The functionality of this modifiable system, especially in relation to deterministic core neural networks in humans, is neatly demonstrated in the archi-cerebellar control of the vestibulo-ocular reflexes (Figure 5), the failure of which results in an unstable system that manifests clinically as involuntary ocular oscillation (Nakada & Kwee, 1986).



Nakada 31

Stochastic: brain chip

The human cerebral cortex contains more than one hundred billion neurons and 1014 synapses. Even without regard to the size of the genome, it can be easily concluded that a deterministic blueprint for connectivity of such an enormous number of networks is untenable (Von Der Malsburg, 1995). Among the many neural-net algorithms, the concept generally referred to as Kohonen’s self-organizing map represents the most probable candidate that applies to self-organizing functional connectivity in the cerebral cortex (Kohonen, 2001).

Kohonen’s map is a non-linear, two dimensional, self-organizing neural net (Figure 6). Kohonen’s map automatically creates ‘areas’ within the map that can be specifically acti-vated by a corresponding stimulus through the repeated application of identical or similar stimuli. The algorithm is capable of automatically creating all aspects of functional organ-ization thus far identified in neurophysiology, including the orientation of the columns of the visual cortex, utilizing only repeated stimuli. Therefore, as far as cortical functional connectivity is concerned, Kohonen’s map meets not only a necessary but also a suffi-cient condition for an algorithm of the cerebral cortex.

From the standpoint of system organization, Kohonen’s net is actually a two-dimensional version of the McCulloch-Pitts neuron. While the McCulloch-Pitts neuron learns individually, neurons in the Kohonen’s map learn simultaneously as a group. The biological realization of the McCulloch-Pitts neuron is generally accepted to be the cer-ebellar chip (Ito, 2001). Similarly, the biological realization of Kohonen’s map has been described as the brain chip (Nakada, 2000, 2004) and cortical columnar organization, which forms the biophysical substrate of the mind (Figure 7). Its virtually identical behavior to Kohonen’s map is clearly demonstrated in humans in the acquisition process of a new intelligent task (Figure 8).

Figure 6. Kohonen’s self-organizing mapOne of the most successful neural net applications for the creation of associative memories similar to that observed in brain, in vivo, is the self-organizing map (SOM) initially introduced by Kohonen, a non-linear method based on unsupervised learning processes. Any point on the two-dimensionally spread neural lattice can be excited. For each input x, the learning processes are confined to a local group of neurons centered at site s, where maximum adaptation occurs. The adaptive changes will decay according to their distance from the center (Neighborhood kernel) in Gaussian fashion.

hr s

rs = −exp( )|| - ||

2

2σ

Assuming that active, non-linear forgetting, ε, occurs, the learning rule of each synapse can be given as:

w w xr rs r rsnew old

h h( ) ( )( . ) . . .= − +1 ε ε




System integration

The brain as an entropy fieldClaude Shannon introduced the mathematical definition of information as entropy. The concept rapidly propagated and resulted in the development of two important modern fields in mathematics, information theory and coding theory.

Figure 7. Schematic representation of brain chipThe brain chip, the biophysical substrate of the mind, is a two-dimensional cerebellar chip where multiple pyramidal cells receive learning trigger signals (LTS, blue arrows) simultaneously. In this schema, two sets of six pyramidal cells are considered. Neurons of each group will receive learning LTS simultaneously. The signal intensity of LTS will diminish according to the distance from the center, and, therefore, neurons in the near group (brown dotted line) will receive LTS more frequently than those in the distant group (grey dotted line). This provides a ‘weighted learning’ process necessary for creating a unit which conforms to the algorithm equivalent to Kohonen’s map. NNG: near neuron group, DNG: distant neuron group.

Figure 8. fMRI of IgoBrain activation associated with shikatsu-mondai as shown in Figure 1 in Igo-naïve subjects. Totally naïve subjects see the visual task as a simple pattern and only activate a small visual area (Before). After quick training, subjects start utilizing a large area of cortex. The pattern of activation is highly consistent with those predicted by the algorithm known as Kohonen’s map.



Nakada 33

The brain processes information, which arrives at the cerebral cortices in the form of multi-modality motor and sensory impulses. Since entropy requires the presence of a dynamic system and the mind is derived from brain activities, it follows logically that the mind ‘exists’ only when the brain actively processes information. Thus, although the mind has a defined biophysical substrate, the mind remains a metaphysical entity.

The main role of the brain is to accumulate information derived from each experience. As illustrated in the case of Igo processing (Figure 8), the way the brain processes identi-cal information changes significantly based on experience. These types of observations imply that the brain continuously modifies the way it processes information as it accu-mulates information. This relationship is illustrated in Figure 9. The concept is identical to the self-programming computational system of cellular automata.

The contents of the brain at a given time should be the sum of information accumu-lated non-linearly based on the rule of Kohonen’s map. The brain processes new infor-mation based on this non-linearly accumulated sum of information. Therefore, the mind can be defined as an entropy field created by the sum of information accumulated in the individual brain since birth. The obvious significance of this is that precise prediction of an individual mind would not be possible even if the entire rule of brain activities were described, unless one could identify all the information (experience) for an individual since her/his birth. That is virtually impossible.

Figure 9. Brain works like cellular automataInformation arriving in the brain is processed (learning). As a result, the contents of the brain change, which in turn changes the way the next information will be processed. The self-iterating process is similar to cellular automata, a self-programming processing system. The mind can be defined as dynamical activities of a system, which consists of the non-linear sum of information, ∑ιυφ, within the post-central association cortex as shown.




Figure 10. Prefrontal function modification of informationAcquisition of prefrontal function provides humans with the capability to modify information that forms the mind.

On the other hand, characteristic behavior of the mind can be predicted based on the common behavior of a stochastic system. Typical behavior of a stochastic system that follows Markovian processes, such as the cerebral cortex, as shown in Figure 9, has been described by Pólya and is known as Pólya’s urn. It predicts the behavior of a system will converge asymptotically to a level, the characteristics of which are strongly influenced by results given by earlier Markovian steps. In other words, behavior of an individual’s brain activities and, hence, characteristics of an individual’s mind are strongly influenced by experiences in early childhood.

Human species

The parts of the brain more significantly developed in humans than in other mammals are the frontal lobe and the cerebellum, specifically, the prefrontal area of the frontal lobe and neocortical area of the cerebellum.

Similar to humans, birds possess a highly developed cerebellum. Both species are capable of having non-linear control of the motor system. Human bipedal locomotion is unique, and differs significantly from that of imitators such as circus animals. Humans can walk or stand by locking their knees. Flight of birds is unique, and is substantially different from that of imitators such as airplanes. Birds fly, not just glide. Both motor functions require substantial non-linearity. Development of far-advanced, non-linear motor control is essential in achieving such sophisticated locomotion, and a significantly larger cerebellum likely reflects such development. Similar brain-volume expansion also likely parallels other unique abilities birds and humans have in common, namely, language and music.

Although it is difficult to understand intuitively the specific functions of the prefrontal area, the famous case of Phineas Gage – an American railroad-construction foreman, who survived without specific neurological deficits a serious accident in which a large iron rod was driven through his head, destroying his prefrontal lobe – provides the fact



Nakada 35

that prefrontal function is not essential for language, memories or establishment of self. If the highly developed cerebellum of humans and birds reflects an advanced non-linear motor control system, similar consideration gives us the plausible conclusion that the prefrontal cortex represents an advanced non-linear control system of information processing that takes place primarily in post-central association cortex. All animals with a post-central association cortex, therefore, have a mind. Furthermore, animals with a prefrontal cortex, as represented by humans, have the additional capability of modifying information (Figure 10).

Origin of consciousness

Double Nobel Laureate (Chemistry and Peace) Linus Carl Pauling was deeply intrigued by the fact that the inert gas xenon was an excellent general anesthetic. His research led him to conclude that the only property common to all anesthetic agents, including xenon, should be their effect on water crystallization (Pauling, 1961; see simulation at http://coe.bri.niigata-u.ac.jp/content/VTheory_en). Pauling attempted to apply his theory directly to the dynamics of intracellular water molecules and neuronal membrane potentials. The effort did not yield corroborative results. Furthermore, scientific methodologies during his era were limited and still primarily based on linear concepts. Complex system analysis was still in its infancy. Given that environment, Pauling’s hydrate-microcrystal theory was not particularly attractive to the general scientific community (Marinacci, 1995).

Needless to say, Pauling’s theory of the molecular mechanisms of general anesthesia has tremendous significance for brain science, especially for arousal (subjective conscious-ness; consciousness is here defined medically and is similar to arousal or subjective con-sciousness). If one accepts that alteration in the dynamic condition of water molecules is the basis of general anesthesia, it can be easily deduced that the behavior of water mole-cules in the brain should play an important, if not the sole, role in creating and maintaining arousal (subjective consciousness).

The brain deals with information. Mathematically, information is defined as entropy, as detailed by Claude Shannon in his information theory (Shannon, 1948). In order for the brain to deal with entropy, its units must also work in concert (Figure 11). This implies that each entire columnar unit, the brain chip, which in sum forms the cerebral cortex, has to be stochastically isotropic. This will manifest as isotropic noise formation in the base state (highest entropy state) of the system, the state which is ready but not actually processing information. The concept is analogous to ‘snow’ on a cathode ray tube (CRT) of the old fashioned analog television (Figure 12), seen when power of the CRT is on but no information is displayed. The human brain may not have an identi-cal state to CRT ‘snow’, namely, the state where the person is subjectively conscious but her/his brain is not processing any information, not even endogenous information. Nevertheless, it has been previously successfully shown that such a system can be bio-logically realized by the brain chip and establishment of isotropic thermal noise by water molecules driven by core heat dissipation (Nakada, 2009). As predicted by Linus Pauling, disruption in this system by an anesthetic agent produces ‘unconsciousness’ by changing water molecular activities.




Figure 12. CRT SnowThe power of the television is on. But no broadcasting signal has reached the television. As a result, the CRT shows snow created by internal isotropic thermal noise. This is analogous to the base state of the brain. The person is subjectively conscious, but her/his brain is not processing any information.

Figure 11. Mean as highest entropy stateShannon’s entropy, H, is defined as:

H P s P ss

= −∑ ( ) log ( )

where P(s) represents probability distribution. The lower the entropy, the more information that is contained. Probability distribution can be presented in normal distribution (Gaussian). The highest entropy state corresponds to the mean. In order to compare different probability distributions by least sampling, as in the case of biological systems, all the Gaussians should have identical means. Gaussian with a different mean (grey line) requires full sampling.



Nakada 37

Conclusion

The mind is a metaphysical existence. Nevertheless, modern science now provides a definition of the mind based on tangible biophysical substrates subjected to mathemati-cal treatment. The human nervous system consists of three distinctive functional parts, namely, core deterministic neural networks, an adaptive modifiable system in the cerebel-lum and a stochastic modifiable system in the cerebrum. Acquisition of prefrontal cortex allows humans to modify information utilized in formation of the mind. The mind arises from dynamic activities of the non-linear sum of information, which provides an algo-rithm to continuously process in-coming information.

Given that the mind is formed by the non-linear sum of information, it is apparent that mind development is affected by the environment in which each individual develops, the so-called societal or cultural influences. To some extent, this is true for any animal. Nevertheless, the effects of the environment on mind formation are, without doubt, most evident in humans. Intelligent language of humans further accentuates the process, allow-ing for an individual to ‘experience’ through another person’s mind. This line of reasoning implies that in their evolution humans have knowingly or unknowingly inherited, non-genetically, certain contents of the mind.

It is generally believed that Homo Sapiens evolved in Africa and subsequently, migrated to the rest of the world. Therefore, regardless of whether or not further evolution of humans has taken place in various locations of the world, there must be certain common ‘concepts’ inherited non-genetically into all human minds. There are many tangible remnants of such ancient, global concepts evidenced by mankind throughout the world, most frequently in the form of mythologies. This can be said to be another ‘Pólya’s urn’ carried by the human species in addition to the one carried by individuals.

FundingThe study was supported by grants from the Japanese Ministry of Education, Culture, Sports, Science and Technology.

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Author biographyTsutomu Nakada received his M.D. (Medicine) and Ph.D. (Biomedical NMR) from the University of Tokyo. After completion of his clinical training at the University of Tokyo, University of California (San Francisco and Davis) and Stanford University, he became an Assistant Professor of Neurology at the University of California, Davis, in 1982. Subsequently, he was promoted to Associate Professor in 1988 and Professor in 1992. In 1996, he was recruited to establish a new research center at the Brain Research Institute, University of Niigata, where he currently holds a professorship and a directorship. He is board certified in Internal Medicine (Japan), Neurology (Japan and USA), Clinical Neurophysiology (USA) and Imaging (USA). He was elected to fellow of the American Academy of Neurology in 1988 and council member of the Science Council of Japan in 2008.



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