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Cited by 300

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Characteristics of Random Nets of Analog Neuron-Like Elements

Amarimber, Shun-Ichi·1972·IEEE Transactions on Systems, Man, and Cybernetics

Abstract

The dynamic behavior of randomly connected analog neuron-like elements that process pulse-frequency modulated signals is investigated from the macroscopic point of view. By extracting two statistical parameters, the macroscopic state equations are derived in terms of these parameters under some hypotheses on the stochastics of microscopic states. It is shown that a random net of statistically symmetric structure is monostable or bistable, and the stability criteria are explicitly given. Random nets consisting of many different classes of elements are also analyzed. Special attention is paid to nets of randomly connected excitatory and inhibitory elements. It is shown that a stable oscillation exists in such a net-in contrast with the fact that no stable oscillations exist in a net of statistically symmetric structure even if negative as well as positive synaptic weights are permitted at a time. The results are checked by computer-simulated experiments.

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Metadata

Authors
Amarimber, S.-I.
Venue
IEEE Transactions on Systems, Man, and Cybernetics
Published
1972-11
Volume
SMC-2
Issue
5
Pages
643-657
ISSN
  • 0018-9472 (ISSN-L)
  • 2168-2909
OpenAlex
W2075510082
Cited by
300
References
7
FWCI
3.71
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Characteristics of Random Nets of Analog Neuron-Like Elements. https://app.cochat.ai/papers/10.1109/tsmc.1972.4309193
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Amarimber, S.-I. (1972). Characteristics of Random Nets of Analog Neuron-Like Elements. IEEE Transactions on Systems, Man, and Cybernetics. https://doi.org/10.1109/tsmc.1972.4309193

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.