By Yoshiyasu Takefuji (auth.), Yoshiyasu Takefuji (eds.)
This e-book brings jointly in a single position very important contributions and cutting-edge learn within the speedily advancing quarter of analog VLSI neural networks.
The publication serves as a good reference, delivering insights into probably the most very important matters in analog VLSI neural networks examine efforts.
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Additional resources for Analog VLSI Neural Networks: A Special Issue of Analog Integrated Circuits and Signal Processing
G;* represent net input components and Gi normalized input components. (a) Scheme which closely follows the original biological model, with sigmoidal nonlinearity blocks included in the feedback loop. KG is a reference level corresponding to desired total activation. (b) Scheme which insures that the largest net input component elicits a full-scale response. FS is a reference level corresponding to full-scale activation. Normalized output components are assumed to saturate low at zero. one or more weighting transistors and the crossing of several mitral output and piriform input lines and interconnections are established at random between pairs of input and output lines within each cell.
Solid-State Circuits, Vol. 24, pp. 313-319, 1989. 14. D. P. E. Howard, "Electronic neural network chips," Appl. Opt. Vol. 26, pp. 5077-5080, 1987. 15. FJ. K. Moon, LA. M. Long, "Programmable analog vector-matrix multipliers," IEEE 1. Solid-State Circuits Vol. 25, pp. 207-214, 1990. 16. P. Eberhardt, T. P.
35 298 Shoemaker, Hutchens and Patil An outstanding problem in analog networks is the practical implementation of learning, which in the neural network field usually comprises some algorithmic procedure for modification of interconnection weights between neuronal analogs in response to stimuli and possibly desired response or other feedback presented to the network. Few implementations reported to date actually include learning of this kind on chip [17, 20, 22]. Implementations of biologically inspired networks are often hardwired [24-26], although a few models with limited adaptive capabilities have been built [27, 28].