scholarly journals Compositional Behavioral Modeling of Analog Neural Networks

2021 ◽  
Vol 2 (2) ◽  
Keyword(s):  
Neural Networks ◽  
Behavioral Modeling ◽  
Analog Neural Networks

scholarly journals Single-Poly Floating-Gate Memory Cell Options for Analog Neural Networks

2021 ◽  
pp. 108062
Author(s):  
Maksym Paliy ◽  
Tommaso Rizzo ◽  
Piero Ruiu ◽  
Sebastiano Strangio ◽  
Giuseppe Iannaccone
Keyword(s):  
Neural Networks ◽  
Memory Cell ◽  
Floating Gate ◽  
Floating Gate Memory ◽  
Analog Neural Networks

Analog neural networks with local competition. I. Dynamics and stability

1993 ◽  
Vol 47 (6) ◽  
pp. 4524-4536 ◽  
Author(s):  
F. R. Waugh ◽  
R. M. Westervelt
Keyword(s):  
Neural Networks ◽  
Local Competition ◽  
Analog Neural Networks

THE SUPER-TURING COMPUTATIONAL POWER OF PLASTIC RECURRENT NEURAL NETWORKS

2014 ◽  
Vol 24 (08) ◽  
pp. 1450029 ◽  
Author(s):  
JÉRÉMIE CABESSA ◽  
HAVA T. SIEGELMANN
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Neural Model ◽  
Connectivity Pattern ◽  
General Mechanism ◽  
Computational Power ◽  
Biologically Inspired ◽  
Basic Model ◽  
Analog Neural Networks ◽  
Architectural Plasticity

We study the computational capabilities of a biologically inspired neural model where the synaptic weights, the connectivity pattern, and the number of neurons can evolve over time rather than stay static. Our study focuses on the mere concept of plasticity of the model so that the nature of the updates is assumed to be not constrained. In this context, we show that the so-called plastic recurrent neural networks (RNNs) are capable of the precise super-Turing computational power — as the static analog neural networks — irrespective of whether their synaptic weights are modeled by rational or real numbers, and moreover, irrespective of whether their patterns of plasticity are restricted to bi-valued updates or expressed by any other more general form of updating. Consequently, the incorporation of only bi-valued plastic capabilities in a basic model of RNNs suffices to break the Turing barrier and achieve the super-Turing level of computation. The consideration of more general mechanisms of architectural plasticity or of real synaptic weights does not further increase the capabilities of the networks. These results support the claim that the general mechanism of plasticity is crucially involved in the computational and dynamical capabilities of biological neural networks. They further show that the super-Turing level of computation reflects in a suitable way the capabilities of brain-like models of computation.

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