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

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

ISBN-10: 1461365929

ISBN-13: 9781461365921

ISBN-10: 1461535824

ISBN-13: 9781461535829

This booklet brings jointly in a single position vital contributions and state of the art examine within the quickly advancing quarter of analog VLSI neural networks.
The booklet serves as an exceptional reference, delivering insights into essentially the most vital matters in analog VLSI neural networks examine efforts.

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Additional info for Analog VLSI Neural Networks: A Special Issue of Analog Integrated Circuits and Signal Processing

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The effect of the extra zero at higher frequencies can be neglected. 5 2 ~ ~ ::::l g. 5 6 Time (sec) Fig. 9. Output response for the largest time constant to several block input voltages. k--- Actual Circuit 10 ----0--- o 2 3 4 5 6 7 8 9 10 11 12 Design 13 14 15 Digital Code Fig. 10. Variation of the time constant with the digital tuning code. A good logarithmic dependence is observed. 9 Time (sec) Fig. 11. Output response to a constant step voltage for three different time constants. 8. 1. Evaluation of Optimization For the optimization procedure a few simplifications were made, concerning the circuit layout and integration.

Learning in this system, which is modeled after long-term potentiation, is coactivity-based: the weights of excitatory synapses from active mitral cells onto "winning" piriform cells are incremented. , it can be turned on or oft). Weights can saturate; when fully potentiated they are larger than naive weights by a factor of only two to three. Learning increments are of constant magnitude and typically represent 5 %-10% of the range between naive and fully potentiated weights. LTP, as the name implies, is a longlasting phenomenon in which measurable weight decay is not observed.

II HIGH INPlJ 2 IL INFL ~ -\ RES5 -4 -6 8 28 48 68 80 180 TIME. 120 140 160 ISO 200 ns Fig. 9. Simulated transient response of a 32-stage winner-take-all circuit. Twenty-eight inputs were at the low level of 60 fJ-A, three were at the high level of 138 fJ-A, and the winning input was at 140 fJ-A. Examples of three corresponding outputs are shown. Time course of resetting is indicated. will increase both capacitance at the feedback node and the bypass current which discharges capacitance at the input node via M 1 and M2.

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Analog VLSI Neural Networks: A Special Issue of Analog Integrated Circuits and Signal Processing by Yoshiyasu Takefuji (auth.), Yoshiyasu Takefuji (eds.)

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