The study of the mind and of its biological basis is one of the greatest scientific endeavors of all times. It is the key to the definitive understanding of the very nature of human beings. It is regarded by many as one of the "final frontiers" of big science.
However, the sheer complexity of the nervous system and the many methodological barriers which exist on the way to the objective study of its structure and function require extensive collaboration among the scientific disciplines. Collaboration and integration leads to new knowledge, which would not be possible without this integration. It may assume several forms: in one of them, multidisciplinarity, it occurs when disciplines work side by side in distinct problems of aspects of a single problem. Interdisciplinarity, on the other hand, occurs when disciplines intermesh, integrate and collaborate among themselves (Nissani, 1995). Modern neuroscience recruits collaboration among molecular and cellular biology, developmental biology, genetics, biochemistry, biophysics, pharmacology, electronics, information technology, biomedical engineering, mathematics, statistics, physics, cognitive sciences, psychology, linguistics and many other disciplines. They converge and intermesh in what is probably the most multidisciplinary and interdisciplinary of all sciences.
For example, the membrane of a neuron can be studied separately by chemistry or physics as a complex phase of organic molecules with distinctive electrical properties; abstracting entirely the fact that it is part of a living cell and the result of organic evolution. This is multidisciplinary research. However, when the membrane's structure, properties and functions are studied using a approach combining the contributions of several disciplines working together, we have interdisciplinarity. For example, in a research project described as "the influence of second messenger systems upon the molecular conformation of ionic channels and its consequence on integration of input information by dendritic fields of genetically defective neurons in the visual area, using electronic imaging and micromachined electrodes" we note the intrinsic collaboration of many disciplines from many different areas of knowledge. The outcome of this seemingly important research would not be possible without their integration.
The Roots of Interdisciplinarity
The fascination of many philosophers and natural scientists with the workings of the brain and of the mind has made neuroscience a strong multi- and interdisciplinary field of research and speculation centuries before the very concept of interdisciplinarity was invented.
The baffling and unfathomable nature of nervous activity has bred for hundreds of years all sorts of "explanatory models", which, in turn, were highly influenced by the cultural and scientific paradigms of the day (Kuhn, 1970). Since the detailed structural study of the human body could be effectively carried out many centuries before any meaningful physiological studies were possible, and due to the fact that Renaissance science was fascinated with everything mechanical, it is not surprising that the first models of function of the nervous system were mechanical ones (Sabbatini, 1998). The real philosophical breakthrough was to regard the body as a kind of machine, albeit suffused with "vital fluids" which seemed to differentiate it from the mere artifacts created by man. This stimulated a lot of interdisciplinary thought and collaboration.
For example, René Descartes proposed around 1660 that nerves conducted the stimulus to the brain thanks to a wave of propulsion of the fluid in its hollow interior. Pumped full of this fluid, the muscles would expand, leading to contraction. Descartes delighted in comparing what he called the "nervous machine" with the elaborate water fountains which were in vogue at the time. This hydraulic model, using internal fluids, was universally accepted for more than a century by anatomists such as Thomas Willis and Albrecht von Haller, as well as by physicists such as Sir Isaac Newton (see Brazier, 1959, for a compact and highly readable description of the historical development of neurophysiology).
In the last decade of the 18th century, however electricity substituted mechanics as the main interest of physical science, and also, as the most likely explanation for the actions of the nervous system. The new paradigm in neurophysiology started by simple observations by naturalists of electric fishes, which proved that animals could generate electricity in their bodies. The Italian anatomist and physician Luigi Galvani (1737-1798) was one of the first to investigate experimentally the phenomenon of what came to be named "bioelectrogenesis". In a series of experiments started around 1780, Galvani, working in the University of Bologna, found that the electric current delivered by a Leyden jar or a rotating static electricity generator would cause the contraction of the muscles in the leg of a frog and many other animals, either by applying the charge to the muscle or to the nerve. His conclusions were published on 1791, with an essay titled "De Viribus Electricitatis in Motu Musculari Commentarius" (Commentary on the Effect of Electricity on Muscular Motion). A famous polemic with physicist Alessandro Volta ensued, but eventually Galvani was proven right, thus originating two centuries of collaboration between physicists and biologists in the study of nervous action at the cellular level.
The paradigm shift was complete: nerves were not water pipes or channels, as Descartes and his contemporaries thought, but electrical conductors. Information within the nervous system was carried by electricity generated directly by the organic tissue. This became an enormously powerful paradigm for the experimental investigation in neurosciences. It was the first time in the history of science that a close cooperation between physics and biology was necessary, not only because biology was looking upon the phenomena of neural function under the light of the recent knowledge provided by physics, but also because they were using the same measuring instruments, devices and apparatuses (for a review, see the section on "Tools of the Trade" in my 1998 paper on the history of the discovery of bioelectricity).
The applied knowledge amassed by experimental physicists in the fields of electricity, optics and mechanics was starting to make a great contribution to physiology. Many times, however, the physiologists who were carrying out pioneering experiments were forced to invent or to adapt existing instruments in order to fit the demanding requirements of working with living tissue, capable of generating extremely feeble currents and voltages, never before studied by physicists. This was the origin of the interdisciplinary professional in neuroscience. Electrical stimulation and recording, and visualization of neural tissue at the cellular level demanded the most sophisticated and advanced physical equipment of the time, such as foil and string galvanometers, electrical piles and generators, mechanical time recorders, optical microscopes, etc. Later on, as both the instruments and neurophysiology evolved, one field boosted the other. High gain electronic amplifiers, cathode ray tube oscilloscopes, precision square-wave stimulators, wave recorders, etc., were essential for electrophysiological investigation. New diagnostic tools for medicine such as the electroencephalographic recorder, were also born as the children of the marriage between the physical and biological disciplines. The same happened in the area of neurochemistry, leading to the discovery and study of neurotransmitters and many other nervous phenomena.
The Emergence of Modern Neuroscience
The emergence of neuroscience as a distinct discipline is relatively recent. It occurred in the early 1960s, as a result of the convergence of more traditional fields, such as neuroanatomy, neurophysiology, neurochemistry, behavioural psychology, ethology, etc., and its birth was greatly influenced by interdisciplinary research programmes, such as those developed by Stephen W. Kuffler and Francis O. Schmitt, among others. The concept of integrated and interdisciplinary neurosciences rapidly evolved as the consequence of a series of symposia, conferences, publications (such as the influential "Study Programs" series, edited by Schmitt for the Rockefeller University Press), and, finally, the launching of the Society for Neuroscience, in the USA, and of the International Brain Research Organization (IBRO), both in the 1970s. Many prominent neuroscientists, such as H.H. Jasper, Albe Fessard, W.J.H. Nauta, Mary A. Brazier, were involved in this.
Schmitt's early efforts, beginning in 1962, is a good example of the role of interdisciplinarity in the birth of modern neuroscience. He started the Neurosciences Research Program (NRP), affiliated with the Massachusetts Institute of Technology and the American Academy of Arts and Sciences, and later with the Rockefeller University. From the very beginning, NRP was revolutionary because it involved an interdisciplinary, inter-university programme joining the exact, the biomedical and the behavioural sciences to "investigate the physicochemical and biophysical bases of mental processes, such as memory, learning and consciousness". A disproportionately large number of past and current scientific leaders in the neurosciences were participants in the NRP.
More recently, the strong development of molecular and cellular biology, genetic mapping and engineering, in great part aided by spectacular technical advances in neuroinformatics, bioinformatics, laboratory robotics, etc. have greatly accelerated the neuroscientific knowledge on the basic functions and development of the nervous system, as well as the causes of many neurological and psychiatric disorders. Due to these developments, three noted neuroscientists have recently presaged the "emergence in the coming decades of a new nosology, certainly in neurology and perhaps also in psychiatry, based not on symptomatology but on the dysfunction of specific genes, molecules, neuronal organelles and particular neural systems." (Cowan, Harter & Kandel, 2000).
New impulses to the interdisciplinary integration of the neurosciences were given by the announcement of the Decade of the Brain, in 1990, by the American government, and the formation of the Human Brain Project (HBP), an internationally funded drive to increase dramatically our knowledge about our brains and minds. The result of all this has been nothing short of spectacular. According to the Society for Neuroscience (SFN), " In 1970, neuroscience barely existed as a separate discipline. Today, more than 300 training programs exist in neuroscience alone, and neuroscience is one of the most exciting areas of biomedical research.". A clear indicator of this growth is SFN itself, which has now more than 29,000 members and more than 100 local chapters.
New Interdisciplines with Neuroscience in Mind
The last decade of the last century has witnessed an extraordinary increase in the number of bridges between disciplines, having neuroscience as the focus. If we imagine that each discipline of knowledge is a vertex in a polygon, the lines connecting these vertices in double, triple and n-tuple configurations have been proliferating at a phenomenal rate. In fact, it has been difficult to keep pace, and the naming conventions for new interdisciplinary and transdisciplinary fields are becoming sometimes hard to recognize.
Just to give the reader an idea about this proliferation, see below a very limited list of interdisciplinary neuroscience areas which have appeared recently:
The degree of interdisciplinarity in any of these new areas may vary, of course. Nissani (1995) proposes to characterize the degree of interdisciplinary integration according to four criteria:
Interdisciplinarity evolves, too. What was interdisciplinary in the past, such as the integration between biology and physics, became a discipline (e.g., biophysics), with its own set of experts, conferences, journals, professional societies, committees, graduate courses and students, methods and even established professions with undergraduate curricula (Klein, 1990).
In the following sections we will examine the dynamics and rationale of creation of new "interconnecting lines" between disciplines and levels of analysis, and two case studies which we will use to exemplify the powerful results of these interactions in two areas: technology helping brain research and applications, and the approach between computer and neurosciences.
Interdisciplinary Integration: The Concept of Levels of Analysis
Most of the advances which occurred in the history of the basic and clinical neurosciences have been limited to research within a single level of organization; i.e., either at the molecular, cellular, organ, psychological/behavioral or social/environmental levels. This is the traditional intradisciplinary research. For example, biochemists have studied intraneuronal proteins at the molecular level, cell biologists have studied neural organelles and processes at the cellular level, neurophysiologists have studied the properties, structure and functions of larger neural networks in sensorial and motor systems, psychologists have studied psychological and behavioral phenomena such as learning, conditioning, emotions, etc.; and sociologists, biologists and other professionals have studied social organizations, the relationships of human societies to the environment, and so on.
Interdisciplinary, cross-disciplinary and multidisciplinary research requires that some degree of interaction occur between these levels of analysis. Cacciopo and Berntson, in 1992, have proposed the concept of multilevel analysis as a way of breaking the barriers between traditional disciplines and to achieve interdisciplinary collaboration. Multilevel analysis is not synonymous with interdisciplinarity, because interdisciplinarity may occur even when restricted to a single level. However, it is essential for a wider collaboration between disciplines which have specialized themselves in distinct levels of this hierarchy.
Two processes are necessary for multilevel analysis to take place: the first one is to use discoveries, findings and concepts taken from one level of analysis to inform, to refine, and to constrain inferences obtained at another level of analysis. Evolutionary psychology is a new interdisciplinary field which shows this: the knowledge about evolutionary genetics is used to inform, clarify and derive new knowledge about human social behavior, language, cognitive development, etc. which were hitherto not seen under this new light. The second one is to study simultaneously a phenomenon using two or more levels of analysis, or, in other words, by disciplines which are specialized at these levels. In the same vein of the previous example, a phenomenon such as the human laughter can be studied by comparative ethologists, developmental human biologists, neurophysiologists, clinical neurologists, social psychologists and other professionals to understand what is laughter, why we laugh, how it appeared in the human record, what is its function in human evolution and social organization, how it is organised by the brain, how much of it is innate and how much is socially learned, and so on (Cardoso, 2000). The difference here is that the research hypotheses themselves are multilevel in nature. The resulting explanations can coexist at several levels, simultaneously, and also generate new explanations at any single level.
The needs for a multilevel approach and its advantages are clearly spelled out by Andersen (1998):
"Although the success of single-discipline research is evident, this approach may also be seen as somewhat limiting. The limitations exist because, while the disciplines concerned with health research may be separated conceptually, methodologically, and administratively, the processes about which they are concerned are inextricably linked. In other words, the social, behavioral, and biological processes that affect health are interdependent. Failure to conduct research across disciplinary lines precludes the discovery of these interdependent processes. […] Research that integrates the various levels of analysis represents one of the next great frontiers in the health sciences, with the potential to accelerate advances in both basic and clinical research and in public health. The hallmark of such integrated, multilevel research is interdisciplinary collaborations, which use the expertise of several disciplines to address complex health issues. "
Multilevel analysis provides an excellent paradigm to understand, explain and initiate new interdisciplinary research in the neurosciences. The several types of interactions between the levels of analysis have been systematized and are described in the review paper by Andersen (1998). In our view, this systematic knowledge about how and where to carry out multilevel interdisciplinary research should be a required subject of all courses on the methodology of science for future scientists.
The reason is that there are still wide distances between several levels of analysis which remain to be bridged over. Noted neuroscientist Jaak Panksepp has recently written an essay for a new journal devoted to cover the gap between neuroscience and psychoanalysis, a giant and influential field which has been often criticised for its lack of scientific support:
"The failure of too many in the various psychological, psychoanalytic and neuroscience communities to concurrently embrace the full hierarchical complexity of the human brain-mind with a full devotion to conceptual flexibility and scientific rigor, has saddened many generations of students who wanted a deep and realistic understanding of the human condition. […] Because of the recent revolutions in neuroscience and molecular biology along with emerging psycho-behavioral and dynamic perspectives, we may finally be in a position to deal with some of those long neglected issues credibly using rather standard, albeit somewhat more theoretically flexible, scientific approaches. The only thing that should matter in this scientific game is the capacity to make predictions that can be empirically verified or falsified […]."
The same applies to philosophy and other areas of the human sciences which evolved much earlier and separately from the neurosciences, in regard to the study of the human mind and its manifestations, under totally different paradigms. It remains to be seen whether the gaping chasm between them and the "hard" natural sciences eventually will be filled by novel interdisciplinary approaches.
Interdisciplinary Collaboration: The Case of Functional Neuroimaging
A good example on how a totally new field of research and application can be created by interdisciplinary and multidisciplinary collaboration, and how this approach can revolutionize a traditional science, is functional neuroimaging.
Powerful neuroimaging techniques developed in the last decade have allowed neuroscientists to map and to study in detail the brain regions which are associated with many motor, sensory, cognitive and language actions and tasks in humans. Their most important contribution so far has been the establishment of clearer relationships and influences between the social/behavioral and organ levels of analysis we have described above, by providing novel insights into the function of several regions of the brain that underlie normal and abnormal behavior (Nemeroff, 1999). The so-called "big four" functional imaging techniques are SPECT (Single-Photon Emission Computed Tomography), PET (Positron Emission Tomography), MRS (Magnetic Resonance Spectroscopy) and fMRI (functional Magnetic Resonance Imaging). The first two are based on radioligand tracing with scintillography, using short lived radiopharmaceuticals which can be used to produce two and three dimensional images of the brain depicting in color the increased activity in metabolism, glucose and oxygen consumption by neural cells, regional blood flow, distribution and density of receptors and transporters, neurotransmitter synthesis activity, etc. The last two use the differential paramagnetic properties of substances found in the body, such as deoxy-hemoglobin, to obtain images related to neural activity, concentration and distribution, etc. Externally administered pharmaceuticals can also be used to trace specific activities.
In addition to these four imaging methods, we have also two topographical signal analysis techniques which are also able to produce bi- and three-dimensional dynamic maps of neural activity, which are Colour Electroencephalographic Topography and Magnetic Electroencephalography.
The impressive fact is that all these techniques have been developed in a relatively short period by virtue of a wide and unprecedented interdisciplinary effort, involving electrical and mechanical engineers, physicists, mathematicians, computer programmers, neurologists, psychiatrists, and many other professionals. The main fuels of this effort have been scientific, medical and economic interests. The functional imaging industry is nowadays a major segment of the biomedical equipment market, as it can be attested by the size of the technical exhibit during the famous annual meeting of the Radiological Society of North America (RSNA) in Chicago, reputedly the largest medical meeting in the world. The field continues to evolve at a furious pace, recruiting ever more technical talents and aggregating more and more advanced concepts and inventions, such as virtual reality, telepresence, robotics, etc. The industry is already developing, manufacturing and marketing powerful combined devices, such as those which put together in a single apparatus a PET and a fMRI imaging systems, or a PET and a CT scanner, or a CT scanner and a magnetoencephalograph. The resulting images are cross-registered or aligned, in order to produce, for example, a picture of metabolic neural activation using deoxyfluoroglucose (DFG) superimposed on a MRI anatomical slice of the brain at exactly the same level. Complex algorithms and mathematical techniques can be used to extract novel information from these images in real time and depict them in the form of dynamical maps in two, three and four dimensions.
Astounding examples of the power of these combined techniques can be seen in many research projects carried out by the International Consortium for Brain Mapping, such as a probabilistic four dimensional map of the human brain which is in development under the leadership of UCLA's Dr. John Maziotta (Maziotta et al., 2001). Microscopic and macroscopic, functional and anatomic images of the brains of ca. 7,000 individuals are being analysed. Detailed demographic, clinical and behavioral information is also collected for each subject, and, in addition, 5,800 subjects will contribute DNA for the purpose of determining genotype– phenotype–behavioral correlations. This and other contributions proposed by the Human Brain Project can be seen in the book by Pechura & Martin (1991).
Among the many interdisciplinary fields which were richly fertilized with the tools of functional brain imaging, cognitive neuroscience stands high. According to one of the pioneers and leaders in this area, Marcus Raichle, cognitive neuroscience "combines the experimental strategies of cognitive psychology with various techniques to actually examine how brain function supports mental activities" (Raichle, 1998). Our current knowledge about the brain substrate for learning, memory, visual and auditory cognition, etc. has been impacted tremendously in the last ten years by the advent of neuroimaging. The same has happened to the clinical neurosciences, were the understanding about the causes and consequences of several brain and mental diseases have profited from the availability of functional neuroimaging in patients (Honey et al., 2002).
The Emergence of Interdisciplines: The Approach Between Computing and Neurosciences
The recognition, by philosophers and scientists alike, that the brain is a kind of computing device is quite old, and has spawned many new interdisciplines, of which, the most recent ones are Computational Neurosciences and Neuroinformatics.
Charles Babbage, the 19th century prodigious inventor of the first programmable computer, laid out the basis for artificial machines capable of imitating some aspects of human intelligence, such as numerical calculations, symbolic processing, etc. But it was only in the second half of the 20th century that pioneers such as Alan M. Turing, Norbert Wiesner and John von Neumann argued that the brain could be seen as a kind of computer, and that making a machine capable of duplicating human intelligence was a desirable and achievable goal. Von Neumann, one of the most brilliant mathematicians of all times, and the inventor of the stored-programme concept for digital computers, expressed the view, in 1956, in his now famous Yale lecture "The Computer and the Brain", that the brain seemed to operate in part using digital (binary) computing, and in part using analogical computing, and that it really was quite different from man-made computers, including the fact that it was really a statistical computing device.
At the same time, however, logical calculus using the binary system was developed for electrical circuits and it was shown to be amenable to implementation in automatic mechanical or electrical machines, thus providing the basis for achieving "machine thinking" using deterministic logic, an old desire (and fear) of the human race since the times of the Golem legend.
Thereafter, advances in digital automatic stored-programme computing technology enabled in the 1950s and 1960s the development of a new branch of the computer sciences, called Artificial Intelligence (A.I.). It was created by the work of pioneers such as Marvin Minsky, John McCarthy, Edward Feigenbaum, Alan Newell, Herbert Simon and others. It tried to model human intelligence in a machine without actually understanding how it works, an approach which is called "heuristics". For example, a superior chess playing program can be programmed to defeat the world champion of chess (such as IBM's Deep Blue) using only algorithmic resources which have nothing to do with the way the human brain works when playing chess! However, A.I. has influenced directly and indirectly the development of the cognitive sciences, mainly because, as it was put by Herbert A. Simon, one of its founders, "Cognitive science is the study of intelligence and intelligent systems, with particular reference to intelligent behavior as computation". This is apparent in the writings of modern thinkers such as Daniel C. Demett, when musing on the basis of the consciousness.
Although it essentially failed its grand promises of "fifth generation computing", of achieving the level of human intelligence by the year 2000, and of producing of a new breed of symbolic processing machines, the top-down A.I. approach produced a number of useful domain-restricted expert functions for computers, which are widely utilised in many areas, such as credit rating in banks, the interpretation of medical lab tests, the discovery of new knowledge hidden in statistical databases, etc.; but it uses little of what neuroscience knows about the brain, because it was unfeasible to develop hardware and software working similarly to the nervous system at the time that A.I. began to unfold.
This bottom-up approach started to have results only recently, and in large part due to interdisciplinary collaboration between neuroscientists and computer scientists (see Churchland & Sejnowski, 1999, for a comprehensive and didactic review of the field). It all began very early with a MIT neurophysiologist, Warren McCulloch, who in the 1950s and 1960s tried to model intelligence based on the networked action of individual neuron, an approach which has been called "emergent", or "connectivist computing", because computing functions emerge from the collective action of interconnected simpler and stereotyped elements ("neurodes"). Parallel distributed processing (PDP) is also another name given to this approach, because it is a key component of any kind of biological intelligence.
In 1950, Frank Rosenblatt, an American psychologist, developed the first artificial neural network capable of learning useful computations, which he called the "perceptron", which connected artificial neurones in two layers (input and output). Geoffrey Hinton and others later extended the model to multilayer perceptrons (MLPs), which were then mathematically proven to be capable of performing complex non-linear calculations. After some initial mishaps, this gave origin in the 1990s to a rapidly developing and fully-bloomed interdisciplinary area called Neural Computing, or Neuroinformatics. Many of the artificial neural networks that were subsequently invented, such as those by Stephen Grossberg, rely heavily on what neuroscience knows about sensory systems, attention, learning, memory, etc. They were also shown to be capable of amazing feats which were impossible to implement efficiently by using conventional Artificial Intelligence, such as the recognition of human faces, of handwritten letters and of spoken words, etc. Among other applications, we have used artificial neural networks (ANNs) to carry out complex medical prognoses and diagnoses, such as the accurate prediction of mortality for cardiac patients based on clinical and echocardiographic data (Sabbatini & Ortiz, 1993) and the recommendation of brain surgery for patients with head trauma (Botelho, Araújo & Sabbatini, 1997).
Neuroinformatics combines neuroscience research with computer sciences, mathematics, physics, engineering and related sciences. Its aim is to produce artificial devices which work very much as the complex perceptual, motor and cognitive functions of human brains. In addition, according to the NIH, it aims at developing new conceptual approaches to basic and/or clinical neuroscientific research and analysis and to acquire, store, retrieve, organize, manage, analyze, visualize, manipulate, integrate, synthesize, disseminate, and share data about the brain and behavior. Therefore, there is clearly here a circular chain of influence: Neuroinformatics builds its artificial devices based on knowledge about the brain, and as a result provides tools that can enhance our understanding about the brain. Other interdisciplines have arisen as a result, such as neurobionics, the invention of intelligent neural prostheses (for instance, artificial retinas and cochleas, neurally controlled hands and limbs, etc.), and neuro-robotics (robots controlled by artificial neural networks). This is one of the most powerful results of interdisciplinary integration that are known.
From the initial interests of neurophysiologists in the mathematical modeling of the electrical activity of neurons, another interdisciplinary branch soon grew out: computational neurosciences. The fathers of this approach were British biologists Adrian Huxley and Alan Hodgkin (both of whom were later awarded the Nobel Prize and life peerages). In the 1950s, they described a set of differential equations which modeled the behaviour of ion transport and the dynamic electrical and chemical equilibrium of membrane polarization in active, transmitting neurones ("action potential"). By simulating in a computer the numerical integration of these equations, they obtained a remarkably accurate reproduction of naturally observed recordings in the squid giant axon and the HH model, as it is called, remained for almost three decades the theoretical basis of the electrochemistry of action potentials. Other pioneers in the field, such as Wilfred Rall, Michael Hines and Art Vance later continued the interdisciplinary work which advanced computational neurosciences at the single cell level.
It was only a matter of time that the computer simulation of more complex neural networks was feasible by joining excitatory an inhibitory HH neurones to each other and by observing the resulting activity. Jim Bower, at Caltech, was one of the first to demonstrate that neural networks with up to 40,000 elements could be simulated in supercomputers and render remarkable accurate models of the naturally-observed olfactory cortex activity, for example (Eeckman & Bower, 1993). He also showed how the progress of Computational Neurosciences could be spurred by having tools for realistic neural simulation using special software tools, like GENESIS, developed by him and his group at Caltech, and which is widely used by researchers in the field.
Nowadays, Computational Neuroscience is developing at full speed and, in the USA, it is a priority area for the National Sciences Foundation and of the National Institute of Mental Health, which have established a special grants programme for it. Many academic institutions have established research programmes in Computational Neurosciences, particularly in the interface with the cognitive neurosciences and the training and recruiting of new talents proceed at a brisk pace. There are at least a major annual conference (Computational and Neural Sciences), an international journal (Journal of Computational Neurosciences), several national and international summer courses and all the accoutrements of established science. Computational Neuroscience is also firmly entrenched in the Human Brain Project, because the emergence of supercomputers and novel simulation techniques have provided a hope that the neurosciences will be one day the first "complex object" biological science to have a strong mathematical basis, such as it happens with other "hard sciences", such as physics and chemistry. The value of this cannot be underestimated, and, in my opinion, it is a future development that will eventually be able to integrate molecular and genetic aspects, too. In other words, the powerful field of Bioinformatics, which deals with the processing and representation of molecular genetic information on the genome, proteome, etc., will join hands with computational neuroscience, leading to an amazing powerful tool for understanding all of the brain.
The ability that computer databases and algorithms will have on the modeling and description of entire nervous systems is already evident with simple organisms, such as the soil nematode Caenorhabditis elegans, which has all of its 302 neural cells described and catalogued, as well as its entire genome, with 97 million bases, therefore making it the most completely characterized metazoan. A total computer model of this organism, incorporating all the knowledge accumulated about it, from its basic molecular systems to its behavioral repertoire, is not far away. WORMATLAS, a database comprising its behavioural and structural anatomy is already available. It is entirely possible that other organisms, more complex that C. elegans, will be described in the same way in the future. The consequences for biological research are simply mind boggling.
It is easy to see that the advancement of the neurosciences will rely more and more on interdisciplinary approaches on the next years. This poses three great challenge to the neuroscientific establishment: first, the way neuroscientists are trained; second, the way departments and laboratories are organized in the academic institutions, and third, how to foster interdisciplinarity in the neurosciences.
The first of these challenges, in my opinion, is the most serious one. It is marked by the fact that many biologists usually do not like the exact sciences and are wary of the methods of engineering, computing and mathematics. Their professional profile is still too "soft", too devoid of the knowledge and interactions which are required to be a modern and effective professional. In consequence, it is difficult to find and to recruit interdisciplinary talents, because they are rare, difficult and slow to train by conventional educational methods. This must be changed, and a new kind of neurobiologist must arise: one who is able to work comfortably and to interact simultaneously with the specialists of many of the disciplines and levels which are being integrated. Someone in the research team must have the overarching vision of what the project is about, and how and when the different disciplines are going to interact. More resources must also be expended toward finding and training the new interdisciplinary neuroscientist. Experts agree that the best point in time to do this is during the undergraduate formative years. Talent for interdisciplinary work already appears at college age and must be cultivated by means of special educational programs which will take this student out of the conventional "mill" and put him or her in alternate tracks which emphasize team work, multidisciplinary and interdisciplinary work and which encourage the study and specialization in two or more disciplines. For instance, many neuroinformatics labs accept only graduate students who have previous practical experience and good background in neurobiology and informatics, and who have talent for advanced mathematics.
A particular area appears as challenging enough to require its own kind of interdisciplinary effort and dedicated professionals: how we are going to collect, systematize and disseminate the huge amount of scientific information about the brain and the mind. Informatics and computer networks such as the Internet are now essential for this endeavor (Cardoso, 1998). For example, the GeneBank, which holds all the known genetic sequences of all species, is so large and changes so frequently that the only way of maintaining it updated and to carry out quick universal distribution is via the Internet. The USA and other countries have established exemplary huge computational and networked structures to serve this purpose, such as the National Center for Biotechnology Information. A similar centralized effort is due for neuroscience data. The Human Brain Project is one of the most impressive interdisciplinary programs which were proposed to face this challenge. A strong collaboration between informatics and neurosciences has been a vital component for its brain mapping efforts (Pechura & Martin, 1991)
What it's in the future? The complete sequencing of the human genome, and subsequent efforts to decode the entire set of human genes and its expression, which are underway, is also a major stepping stone for a new interdisciplinary neuroscience. Everything in neural biology will be referenced to this framework, and unfortunately it is also the case that the practical and theoretical knowledge of neuroscientists in this area must be increased if the integration to other levels of analysis is to happen.
Creativity in science depends on the establishment of links between previously unrelated ideas. The growth of neuroscience will ultimately depend on this creativity, as more and more complex problems are tackled.
Renato M.E. Sabbatini is a neuroscientist with a PhD in neurophysiology of behavior by the University of São Paulo, Brasil, and a post-doctoral fellow in the Department of Behavioral Physiology of the Max-Planck Institute of Psychiatry, Munich, Germany. Currently, Dr. Sabbatini is the director of the Center for Biomedical Informatics and Chairman of Medical Informatics of the Medical School of the State University of Campinas, Campinas, Brazil. He is also the associate editor of "Brain & Mind" magazine, and editor-in-chief of Intermedic, a journal on Internet and Medicine. Email: firstname.lastname@example.org
Silvia Helena Cardoso, PhD. Psychobiologist, master and doctor in Sciences by the University of São Paulo and post doctoral fellowship by the University of California, Los Angeles. Invited Professor and Associate Researcher of the Center for Biomedical Informatics, State University of Campinas (Unicamp), Brazil. She is also editor-in-chief of "Brain & Mind" magazine, and associate editor of Intermedic, a journal on Internet and Medicine. Email: email@example.com