Abstract
In this paper we consider a formulation of neural computation in terms of multilayer co-operative processes between local inference units. Each neuron is described in terms of a local computation frame with slots and semantic tables to represent the meaning of the input and output representation spaces. After some methodological remarks on the distinction between processors and processes in the auditor paradigm, we introduce the computational model of co-operativity where global computations are divided into a large number of dinamically reconfigurable sub-computations. Some theoretical requirements are considered and the lack of developments on concurrent programming is pointed out. Then, the frame neuron is introduced and the meaning of the different slots is stablished. Linear and algorithmic formulation appears as a particular case of the frame to which we can reduce all previous connectionistic formulations. This computational model is used to “revisit” the analogical level where neuronal function is formulated in terms of linear spatio-temporal filters followed by a threshold function.
A personal feeling underlying all the paper is the lack of appropriate tools in systems theory powerful enough to describe complex behavior such as that of neural nets where the linear and logic levels are not sufficient.
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Mira, J., Delgado, A.E. (1992). Linear and algorithmic formulation of co-operative computation in neural nets. In: Pichler, F., Díaz, R.M. (eds) Computer Aided Systems Theory — EUROCAST '91. EUROCAST 1991. Lecture Notes in Computer Science, vol 585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0021001
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DOI: https://doi.org/10.1007/BFb0021001
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