Abstract
Recent advances in “neural” computation models1 will only demonstrate their true value with the introduction of parallel computer architectures designed to optimise the computation of these models. Many special-purpose neural network hardware implementations are currently underway2,3,4. While these machines may solve the problem of realising the potential of specific models, the problem of designing a “general-purpose” Neural Computer has not been really addressed. This Neural Computer should provide a framework for executing neural models in much the same way that traditional computers address the problems of number crunching which they are best suited for. This framework must include a means of programming (i.e. operating system and programming languages) and the hardware must be reconfigurable in some manner.
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© 1989 Springer-Verlag Berlin Heidelberg
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Recce, M., Treleaven, P.C. (1989). Parallel Architectures for Neural Computers. In: Eckmiller, R., v.d. Malsburg, C. (eds) Neural Computers. Springer Study Edition, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-83740-1_49
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DOI: https://doi.org/10.1007/978-3-642-83740-1_49
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