Analog VLSI Neural Networks: A Special Issue of Analog by Yoshiyasu Takefuji (auth.), Yoshiyasu Takefuji (eds.)

By Yoshiyasu Takefuji (auth.), Yoshiyasu Takefuji (eds.)

This publication brings jointly in a single position vital contributions and state of the art study within the swiftly advancing region of analog VLSI neural networks.
The booklet serves as an outstanding reference, supplying insights into probably the most vital matters in analog VLSI neural networks examine efforts.

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The Model The interested reader is referred to the work of Granger et al. for details of the olfactory model [44-48]. The essential features of the model which are relevant to the proposed implementation are summarized as follows. , a receptor which responds to particular chemical stimuli) projects its axons along with those of similar cells to a delimited area of the olfactory bulb which is denoted a glomerulus. The aggregate firing rate of these input cells is regarded as the input to the corresponding glomerulus.

109-118, 1990. 4. L. L. Reilly, "Into silicon: real time learning in a high density RBF neural network, in Proc. Int. Joint Con! Neural Networks, Seattle, WA, Vol. I, pp. 551-556, 1991. 5. U. Hopfield, "Neural networks and physical systems with emergent collective computational abilities," Proc. Amer. Acad. Sci. Vol. 79, pp. 2554-2558, 1982. 6. E. 1. Sejnowski, "Learning and relearning in Boltzmann machines:' in Parallel Distributed Processing, Explorations in the Microstructure ofCognition, Vol.

Within the framework suggested by the biological model, we have developed a pair of alternatives for this processing/normalization which are implementable with closed-loop circuits similar to those used in automatic gain control (AGC). One most closely follows the form given by Ambros-Ingerson [45], consisting of a vector AGC loop with sigmoidal nonlinearity acting on each component within the loop, as illustrated in figure 1a. A second includes an AGC loop without the sigmoids, but with a global offset added to each component within the loop such that the largest net input elicits maximal activity from its glomerulus.

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