Donnarumma, Francesco (2009) A Model for Programmability and Virtuality in Dynamical Neural Networks. [Tesi di dottorato] (Unpublished)


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Item Type: Tesi di dottorato
Resource language: English
Title: A Model for Programmability and Virtuality in Dynamical Neural Networks
Date: 30 November 2009
Number of Pages: 218
Institution: Università degli Studi di Napoli Federico II
Department: Matematica e applicazioni "Renato Caccioppoli"
Scuola di dottorato: Scienze matematiche e informatiche
Dottorato: Scienze computazionali e informatiche
Ciclo di dottorato: 22
Coordinatore del Corso di dottorato:
Ricciardi, Luigi
Date: 30 November 2009
Number of Pages: 218
Keywords: CTRNN, Fixed-weight networks, Neural dynamical systems, Programmability, Virtuality
Settori scientifico-disciplinari del MIUR: Area 01 - Scienze matematiche e informatiche > INF/01 - Informatica
Date Deposited: 04 Dec 2009 12:50
Last Modified: 10 Nov 2014 13:47
DOI: 10.6092/UNINA/FEDOA/4293

Collection description

In this dissertation a fixed-weight architecture for Continuous Time Recurrent Neural Networks (CTRNNs) is proposed in order to give an account for biological phenomena, controlled by neuronal activity, in which changes of behavior occur so fast that presumably no changes in the involved neuronal connectivity are possible. The proposed model possesses the following properties: a. the neural network variables have a direct biological interpretation; b. the change of behavior is controllable by auxiliary (programming) inputs; c. a single fixed-weight neural network has the capability to exhibit a wide repertoire of different behaviors given the appropriate auxiliary inputs. Such properties allow the model to be biologically plausible on the neural level and, at the same time, should sustain a programmability / virtuality capability usually associated only with the algorithmic, symbolic systems used in the high level functional modeling of biological systems. A number of experiments are performed which corroborate: 1) the capability of the proposed architecture to be programmed with auxiliary inputs in order to reproduce the dynamical behaviors of networks with weight values coded by the auxiliary input; 2) the robustness of the proposed architecture w.r.t. variations of the I/O time scales.


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