research-article

Sigma-pi neural unit: switching circuit simulation

Authors:

Alexander A. Potapov,

Wei Zhang,

Tianhua Feng,

Sergo RekhviashviliAuthors Info & Claims

AIIPCC '19: Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing

Article No.: 21, Pages 1 - 5

https://doi.org/10.1145/3371425.3371477

Published: 19 December 2019 Publication History

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Abstract

Electrical circuit to control power is designed for TTL IC sigma-pi neural units. The designed unit is implementable as an integral circuit. The unit representation features are considered in a given circuitry basis. A neural circuit simulation is performed through the PSpice interpreter. The properties of neural networks are considered. The prospects for creating multi-bit processors based on sigma-pi neural units are discussed.

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Cited By

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Shibzukhov ZBelov O(2024)Constructive Learning of Parameterized Boolean $$\Sigma $$ $$\Pi $$-networksAdvances in Neural Computation, Machine Learning, and Cognitive Research VIII10.1007/978-3-031-73691-9_3(23-29)Online publication date: 20-Oct-2024
https://doi.org/10.1007/978-3-031-73691-9_3
Shibzukhov Z(2020)Parameterized Families of Correctly Functioning Sigma-Pi NeuronsBrain-Inspired Cognitive Architectures for Artificial Intelligence: BICA*AI 202010.1007/978-3-030-65596-9_57(478-483)Online publication date: 9-Dec-2020
https://doi.org/10.1007/978-3-030-65596-9_57

Index Terms

Sigma-pi neural unit: switching circuit simulation
1. Networks
  1. Network components
    1. Physical links

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Published In

AIIPCC '19: Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing

December 2019

464 pages

ISBN:9781450376334

DOI:10.1145/3371425

Conference Chairs:
João Manuel R. S. Tavares,
Zeshui Xu

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 December 2019

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AIIPCC '19

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ASciE

AIIPCC '19: 2019 International Conference on Artificial Intelligence, Information Processing and Cloud Computing

December 19 - 21, 2019

Sanya, China

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AIIPCC '19 Paper Acceptance Rate 78 of 211 submissions, 37%;

Overall Acceptance Rate 78 of 211 submissions, 37%

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Cited By

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Shibzukhov ZBelov O(2024)Constructive Learning of Parameterized Boolean $$\Sigma $$ $$\Pi $$-networksAdvances in Neural Computation, Machine Learning, and Cognitive Research VIII10.1007/978-3-031-73691-9_3(23-29)Online publication date: 20-Oct-2024
https://doi.org/10.1007/978-3-031-73691-9_3
Shibzukhov Z(2020)Parameterized Families of Correctly Functioning Sigma-Pi NeuronsBrain-Inspired Cognitive Architectures for Artificial Intelligence: BICA*AI 202010.1007/978-3-030-65596-9_57(478-483)Online publication date: 9-Dec-2020
https://doi.org/10.1007/978-3-030-65596-9_57

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The Training of Pi-Sigma Artificial Neural Networks with Differential Evolution Algorithm for Forecasting

Backpropagation generalized delta rule for the selective attention Sigma-if artificial neural network

A Sigma-Pi-Sigma Neural Network (SPSNN)