Neuroinformatics as Explanatory Neuroscience

There are two points of view about the meaning of neuroinformatics, which wemay write neuro-Informatics and Neuro-informatics, to reflect their different emphases. The proponents of neuro-Informatics hold that it is the application of conventional informatics to the domain of neuroscience. By contrast, the proponents of Neuro-informatics hold that it studies information processing by nervous systems. There is a very significant conceptual difference between these two views, which arise very naturally out of two contrary views of science. The first view, neuro-Informatics, arises out of the philosophy that science is description, and so the major task in modern science is to accumulate and catalogue data. Thus, neuro-Informaticians look to informatics as a maturing information technology based on general purpose computing principles. For the proponents of this goal, informatics is a tool to aid neuroscience. The aid it gives is to catalogue and manipulate neuroscientific data. The hidden assumption is that scientific data are absolute, and that once we have enough data, we will inevitably be able to answer the hard questions. The second view, Neuro-informatics, arises out of the philosophy that science is explanation, and so the major task is to extract predictive principles. Neuroinformaticians take the view that nervous systems are probably qualitatively different from the general purpose computing principles that have dominated the past few decades. Reasons for anticipating these differences are not hard to find. Indeed, many of them were pointed out by von Neumann, the very inventor of general purpose computers. In the view of Neuroinformaticians, resources should be focused on the substantive problem of neuroscience: What is the nature of computation in biological nervous systems? Our research, at the Institute of Neuroinformatics in Zurich, follows the latter point of view. More specifically, we aim to cast the neural computational processes in an electronic medium, using analog very large scale integration (aVLSI) technology. Carver Mead (1989) introduced the term neuromorphic engineering for this new approach based on the design and fabrication of artificial neural systems, such as vision systems, head–eye systems, and roving robots, whose architecture and design principles are based on those of biological nervous systems (Douglas et al., 1995). Neuromorphic systems try to emulate the organization and function of biological nervous systems—they are a method of exploring the principles of neural computation from the vantage points of both neuroscience on the one hand and engineering and computer science on the other. Implicit in neuromorphic engineering is the hypothesis that neural computation may be qualitatively different from classical computers and computation. The enormous success of digital technology and general purpose computers in performing abstract tasks bred confidence that neural computation could be simply captured by those tools. In fact, general purpose computers have been quite unsuccessful in performing autonomously tasks that require any degree of sophisticated sensorimotor interaction with the real world. Even rather primitive biological nervous systems are able to extract meaningful information from a noisy world in real time, but artificial systems still lag far behind such performance.