Brain science of the mind

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<ul><li><p> Science Information</p><p> online version of this article can be found at:</p><p> DOI: 10.1177/0539018410388837</p><p> 2011 50: 25Social Science InformationTsutomu Nakada</p><p>Brain science of the mind </p><p>Published by:</p><p></p><p>On behalf of: </p><p> Maison des Sciences de l'Homme</p><p> can be found at:Social Science InformationAdditional services and information for </p><p> Alerts: </p><p> </p><p> </p><p> </p><p> </p><p> What is This? </p><p>- Mar 3, 2011Version of Record &gt;&gt; </p><p> at OLD DOMINION UNIV LIBRARY on June 7, 2014ssi.sagepub.comDownloaded from at OLD DOMINION UNIV LIBRARY on June 7, 2014ssi.sagepub.comDownloaded from </p><p></p></li><li><p>Brain science of the mind</p><p>Tsutomu NakadaUniversity of Niigata, Japan</p><p>Abstract</p><p>The human cerebral cortex contains more than one hundred billion neurons and 1014 </p><p>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 Pavlovs 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 Kohonens 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 Plyas Urn.</p><p>Keywords</p><p>brain chip, cerebellar chip, complex system, Kohonen, McCulloch-Pitts, Pauling, Plya, vortex theory of the brain</p><p>Rsum</p><p>Le cortex crbral humain contient plus de 100 billions de neurones et 1014 synapses. Mme sans prendre en compte la taille du gnome, on peut en dduire aisment quun plan dtaill dterministe des connexions dun nombre aussi norme de rseaux est irraliste. Les connaissances scientifiques actuelles indiquent que la nature utilise de </p><p>Social Science Information50(1) 25 38</p><p> The Author(s) 2011Reprints and permission:</p><p> 10.1177/0539018410388837</p><p></p><p>Corresponding author:Tsutomu Nakada, Center for Integrated Human Brain Science, Brain Research Institute, University of Niigata, 1 Asahimachi, Chuoh-ku, Niigata 9518585, Japan.Email:</p><p> at OLD DOMINION UNIV LIBRARY on June 7, 2014ssi.sagepub.comDownloaded from </p><p></p></li><li><p>26 Social Science Information 50(1)</p><p>grandes rgles gnrales plutt que des descriptions dterministes compltes pour faonner une structure requise, savoir des rgles dauto-organisation. Le cerveau est un systme complexe et sauto-organise selon un processus Markovien. En consquence, les fonctionnalits du cerveau peuvent tre vues comme des schmas spcifiques issus de processus auto-organisateurs partir de conditions dfinies par les gnes et lenvironnement. Trois systmes catgoriquement diffrents sont dsormais identifis partir de la configuration de leurs units physiologiques fonctionnelles. Alors que le systme le plus ancien rsulte dune connectivit dterministe, les deux autres systmes, savoir le cervelet et le cerebrum, utilisent des units modifiables auxquelles on se rfre respectivement comme puce crbelleuse (cerebellar chip) et puce crbrale (brain chip). Il est maintenant communment reconnu que le conditionnement classique tel celui du chien de Pavlov est bas sur les fonctionnalits du cervelet et de son unit dapprentissage, la puce crbelleuse, qui fonctionne en principe comme les neurones de McCulloch-Pitts. Le cerebrum utilise une carte auto-organisatrice non-linaire selon le concept de Kohonen pour lorganisation de la puce crbrale, un systme qui cre effectivement des champs dentropie. Par opposition lapprentissage crbelleux, qui est adaptatif, lapprentissage crbral est par nature stochastique et suit la rgle dite de lUrne de Polya.</p><p>Mots-cls</p><p>puce crbrale, puce crbelleuse, systme complexe, Kohonen, McCulloch-Pitts, Pauling, Polya, thorie du vortex</p><p>Introduction</p><p>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.</p><p>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 &amp; 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.</p><p>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 </p><p> at OLD DOMINION UNIV LIBRARY on June 7, 2014ssi.sagepub.comDownloaded from </p><p></p></li><li><p>Nakada 27</p><p>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.</p><p>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.</p><p>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.</p><p>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 </p><p>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 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).</p><p> at OLD DOMINION UNIV LIBRARY on June 7, 2014ssi.sagepub.comDownloaded from </p><p></p></li><li><p>28 Social Science Information 50(1)</p><p>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.</p><p>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 mammals 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.</p><p>Modifiable functional unit</p><p>Adaptive: cerebellar chip</p><p>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 &amp; 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).</p><p>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 </p><p>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 McCullochPitts neuron shown in Figure 3.</p><p> at OLD DOMINION UNIV LIBRARY on June 7, 2014ssi.sagepub.comDownloaded from </p><p></p></li><li><p>Nakada 29</p><p>Figure 3. McCullochPitts neuronAt given time t, input signals x</p><p>i(t) reach the synapses. Each input is transmitted through the synapse to the </p><p>neuron after each has been modified by weight wi. When the sum of the inputs reaches threshold , the </p><p>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.</p><p>( 1)( )</p><p>( )y t</p><p>if wixi t</p><p>if wixi t</p><p>1</p><p>0 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 &amp; Kwee (1986) for further details.</p><p>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).</p><p>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 &amp; Kwee, 1986).</p><p> at OLD DOMINION UNIV LIBRARY on June 7, 2014ssi.sagepub.comDownloaded from </p><p></p></li><li><p>Nakada 31</p><p>Stochastic: brain chip</p><p>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)...</p></li></ul>