Criteria for Robust Stability In A Class Of Lateral Inhibition Networks Coupled Through Resistive Grids

Author:

Wyatt John L.1,Standley David L.1

Affiliation:

1. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

Abstract

In the analog VLSI implementation of neural systems, it is sometimes convenient to build lateral inhibition networks by using a locally connected on-chip resistive grid to interconnect active elements. A serious problem of unwanted spontaneous oscillation often arises with these circuits and renders them unusable in practice. This paper reports on criteria that guarantee these and certain other systems will be stable, even though the values of designed elements in the resistive grid may be imprecise and the location and values of parasitic elements may be unknown. The method is based on a rigorous, somewhat novel mathematical analysis using Tellegen's theorem (Penfield et al. 1970) from electrical circuits and the idea of a Popov multiplier (Vidyasagar 1978; Desoer and Vidya sagar 1975) from control theory. The criteria are local in that no overall analysis of the interconnected system is required for their use, empirical in that they involve only measurable frequency response data on the individual cells, and robust in that they are insensitive to network topology and to unmodelled parasitic resistances and capacitances in the interconnect network. Certain results are robust in the additional sense that specified nonlinear elements in the grid do not affect the stability criteria. The results are designed to be applicable, with further development, to complex and incompletely modeled living neural systems.

Publisher

MIT Press - Journals

Subject

Cognitive Neuroscience,Arts and Humanities (miscellaneous)

Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Beyond Backpropagation: Bilevel Optimization Through Implicit Differentiation and Equilibrium Propagation;Neural Computation;2022-11-08

2. On a Generalization of Tellegen’s Theorem to Quantum Circuits;2022 IEEE International Symposium on Circuits and Systems (ISCAS);2022-05-28

3. Analog VLSI circuits for competitive learning networks;Analog Integrated Circuits and Signal Processing;1998

4. Analog VLSI Circuits for Competitive Learning Networks;Cellular Neural Networks and Analog VLSI;1998

5. Vector mapping with a nonlinear electronic layer for distributed neural networks;IEEE Transactions on Neural Networks;1995

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