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Article on Neuronal Computers - Towards Neuronal Computers

Article on Neuronal Computers -Towards Neuronal Computers



The human brain has amazing operative performance. It can work very fast, undertake tasks in parallel, adapt to new situations, and deal with the unexpected. It is also fault tolerant. Computers have become very fast and can undertake some computations with great efficiency. However, there are some areas, for example, visual recognition of objects, where the human brain out performs the best computers.

Neuronal computing addresses the development of computers and algorithms using the operation and architecture of the human brain as a source of design inspiration. The aim is to develop computers that are good at undertaking tasks that are currently poorly performed by computers, but done with great ease by people or other creatures. Another interest driving research in this field is the development of better understandings of how the brain works. In particular how the brain carries out computations and information processing.

Main Issues

There is a need to understand how the brain works and such work can benefit by bringing different disciplinary perspectives to bear on this area of research. The issues to be addressed by this research concern the identification of the algorithms implemented in the brain and the way that neurones actually produce these. Knowing how the brain works may be helpful in understanding how to build better computers that have some of those properties that the human brain possesses.

Some research is being undertaken that combines insight from computational modelling and experimental physiology, to understand the operation of neurones. Neurones are in a state of continuous operation, emitting spikes spontaneously and producing background noise. One of the key conclusions of research in this area is that the emission of output spikes by neurones to a meaningful input is enhanced by the addition of noise. Without noise, the inputs can be filtered out resulting in no output spike. The presence of noise tends to create sharper outputs.

Reverse engineering in biology is another filed of activity. Work in this area is concerned with discovering the design principles of the brain in order to design an equivalent solution in software. In this type of research it is important to use knowledge from neuroscience, psychology and computational modelling to achieve the objective. Rapid visual processing, an activity where the brain outperforms computers, is one application area that is being addressed. An approach called spike based neural computing has been developed, which is computationally efficient and can be implemented on a personal computer. This approach uses a concept called temporal coding, which basically means that the time taken to generate a spike depends upon the stimulus intensity, with a larger stimulus producing a spike faster than one of lower intensity. One of the important conclusions about the potential of neuronal computing is that it might lead to more natural interfaces for human interactions, which would be better for people as the technology would work in the same way that people operate.

Tools to help simulate the brain's operation and to support co-operation between people working in the field is a further area of research activity. Computational models of the brain help with the understanding of information processing taking place in the brain. Software techniques however can provide people with the tools to collect, compare and to analyse information from many different researchers. Collaboration between different researchers is important. Data from research is distributed and there is a need for tools to support exchange, discussion and comparison of models and data.

Another perspective is the engineering one. Computing implies a digital approach. Researchers have been working on the development of a Neural Representation Modeller, which provides modelling capabilities on different levels: the digital neural cell; the neural module; and the neural architecture. The architecture of the brain is a key area of interest.

One question that should be considered is the relevance of this sort of research. One of the main application areas is in the solution of medical problems and that industrial application might then spin-off from the medical applications. Many people feel that this is a strange situation as they expect to see medical applications arising as a spin-off from industrial applications. Neuronal computing however has the potential to change many things in industry in the longer term.

Another question is whether biological computers will ever become a reality. Some work has already been undertaken in this area, where an analogue VLSI based on neurones has been developed. However working with something like a high performance personal computer has advantages since they are rapidly evolving and no special hardware technologies are required. This latter point means that more people could be able to undertake work in this field. At the moment Pentium microprocessors are ideal, but ultimately all technologies are potentially interesting.

Conclusions and Future Directions

A lot of further research is needed in the area of neuronal computing. There is an expectation that this research will eventually inspire new computer architectures. There are things that can be learnt from biology that can be transferred to engineering and vice versa. Cross fertilisation between disciplines such as computer scientists and brain researchers is important.

There are different ideas about neuronal models. There are also different views about the level of modelling and what constitutes the important units to study in the brain and to take inspiration from. There is a need to work at all levels and also to work across levels. Perhaps this is where most work actually needs to be focused. The important thing is to ensure that there is no particular bias in the research supported in the future and that the different perspectives are allowed to flourish.



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