scholarly journals sorn: A Python package for Self Organizing Recurrent Neural Network

2021 ◽  
Vol 6 (65) ◽  
pp. 3545
Author(s):  
Saranraj Nambusubramaniyan
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Python Package ◽  
Self Organizing

scholarly journals Criticality meets learning: Criticality signatures in a self-organizing recurrent neural network

PLoS ONE ◽  
2017 ◽  
Vol 12 (5) ◽  
pp. e0178683 ◽  
Author(s):  
Bruno Del Papa ◽  
Viola Priesemann ◽  
Jochen Triesch
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Self Organizing

scholarly journals PsychRNN: An Accessible and Flexible Python Package for Training Recurrent Neural Network Models on Cognitive Tasks

eNeuro ◽  
2020 ◽  
pp. ENEURO.0427-20.2020
Author(s):  
Daniel B. Ehrlich ◽  
Jasmine T. Stone ◽  
David Brandfonbrener ◽  
Alexander Atanasov ◽  
John D. Murray
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Cognitive Tasks ◽  
Neural Network Models ◽  
Python Package

Learning and Extracting Initial Mealy Automata with a Modular Neural Network Model

1995 ◽  
Vol 7 (4) ◽  
pp. 822-844 ◽  
Author(s):  
Peter Tiňo ◽  
Jozef Šajda
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Recurrent Network ◽  
Neural Net ◽  
Self Organizing Map ◽  
Training Samples ◽  
Mealy Automata ◽  
Mealy Machine ◽  
Input Strings ◽  
Self Organizing

A hybrid recurrent neural network is shown to learn small initial mealy machines (that can be thought of as translation machines translating input strings to corresponding output strings, as opposed to recognition automata that classify strings as either grammatical or nongrammatical) from positive training samples. A well-trained neural net is then presented once again with the training set and a Kohonen self-organizing map with the “star” topology of neurons is used to quantize recurrent network state space into distinct regions representing corresponding states of a mealy machine being learned. This enables us to extract the learned mealy machine from the trained recurrent network. One neural network (Kohonen self-organizing map) is used to extract meaningful information from another network (recurrent neural network).

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