A New Perspective on Memorization in Recurrent Networks of Spiking Neurons - Couverture souple

Name: A New Perspective on Memorization in Recurrent Networks of Spiking Neurons
Author: Murer, Patrick
ISBN: 9783866287587

Murer, Patrick

9783866287587: A New Perspective on Memorization in Recurrent Networks of Spiking Neurons

Couverture souple

ISBN 10 : 3866287585 ISBN 13 : 9783866287587

Editeur : Hartung-Gorre Verlag, 2022

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�diteur: Hartung-Gorre Verlag
Date d'�dition: 2022
Langue: anglais
ISBN 10: 3866287585
ISBN 13: 9783866287587
Reliure: Broch�
Nombre de pages: 228
�diteur: Loeliger Hans-Andrea
Coordonn�es du fabricant: non disponible
Personne responsable: info@bod.de
info@bod.de

�berseering 33
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Allemagne

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A New Perspective on Memorization in Recurrent Networks of Spiking Neurons

Patrick Murer

Edit� par Hartung-Gorre, 2022

ISBN 10 : 3866287585 ISBN 13 : 9783866287587

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This thesis studies the capability of spiking recurrent neural network models to memorize dynamical pulse patterns (or firing signals). In the first part, discrete-time firing signals (or firing sequences) are considered. A recurrent network model, consisting of neurons with bounded disturbance, is introduced to analyze (simple) local learning. Two modes of learning/memorization are considered: The first mode is strictly online, with a single pass through the data, while the second mode uses multiple passes through the data. In both modes, the learning is strictly local (quasi-Hebbian): At any given time step, only the weights between the neurons firing (or supposed to be firing) at the previous time step and those firing (or supposed to be firing) at the present time step are modified. The main result is an upper bound on the probability that the single-pass memorization is not perfect. It follows that the memorization capacity in this mode asymptotically scales like that of the classical Hopfield model (which, in contrast, memorizes static patterns). However, multiple-rounds memorization is shown to achieve a higher capacity with an asymptotically nonvanishing number of bits per connection/synapse. These mathematical findings may be helpful for understanding the functionality of short-term memory and long-term memory in neuroscience.In the second part, firing signals in continuous-time are studied. It is shown how firing signals, containing firings only on a regular time grid, can be (robustly) memorized with a recurrent network model. In principle, the corresponding weights are obtained by supervised (quasi-Hebbian) multi-pass learning. The asymptotic memorization capacity is a nonvanishing number measured in bits per connection/synapse as its discrete-time analogon. Furthermore, the timing robustness of the memorized firing signals is investigated for different disturbance models.The regime of disturbances, where the relative occurrence-time of the firings is preserved over a long time span, is elaborated for the various disturbance models. The proposed models have the potential for energy efficient self-timed neuromorphic hardware implementations. N� de r�f. du vendeur 9783866287587

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A New Perspective on Memorization in Recurrent Networks of Spiking Neurons

Murer, Patrick

Edit� par Hartung-Gorre, Wolfgang, 2022

ISBN 10 : 3866287585 ISBN 13 : 9783866287587

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. &Uumlber den AutorrnrnPatrick Murer was born in Stans, Switzerland, on October 25, 1989. He did a four-year apprenticeship as an electronic technician which he finished with honour in 2009. During this period he also completed the Professional . N� de r�f. du vendeur 591506768

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A New Perspective on Memorization in Recurrent Networks of Spiking Neurons

Patrick Murer

Edit� par Hartung-Gorre, Hartung-Gorre Mai 2022, 2022

ISBN 10 : 3866287585 ISBN 13 : 9783866287587

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This thesis studies the capability of spiking recurrent neural network models to memorize dynamical pulse patterns (or firing signals). In the first part, discrete-time firing signals (or firing sequences) are considered. A recurrent network model, consisting of neurons with bounded disturbance, is introduced to analyze (simple) local learning. Two modes of learning/memorization are considered: The first mode is strictly online, with a single pass through the data, while the second mode uses multiple passes through the data. In both modes, the learning is strictly local (quasi-Hebbian): At any given time step, only the weights between the neurons firing (or supposed to be firing) at the previous time step and those firing (or supposed to be firing) at the present time step are modified. The main result is an upper bound on the probability that the single-pass memorization is not perfect. It follows that the memorization capacity in this mode asymptotically scales like that of the classical Hopfield model (which, in contrast, memorizes static patterns). However, multiple-rounds memorization is shown to achieve a higher capacity with an asymptotically nonvanishing number of bits per connection/synapse. These mathematical findings may be helpful for understanding the functionality of short-term memory and long-term memory in neuroscience. In the second part, firing signals in continuous-time are studied. It is shown how firing signals, containing firings only on a regular time grid, can be (robustly) memorized with a recurrent network model. In principle, the corresponding weights are obtained by supervised (quasi-Hebbian) multi-pass learning. The asymptotic memorization capacity is a nonvanishing number measured in bits per connection/synapse as its discrete-time analogon. Furthermore, the timing robustness of the memorized firing signals is investigated for different disturbance models. The regime of disturbances, where the relative occurrence-time of the firings is preserved over a long time span, is elaborated for the various disturbance models. The proposed models have the potential for energy efficient self-timed neuromorphic hardware implementations.Books on Demand GmbH, �berseering 33, 22297 Hamburg 228 pp. Englisch. N� de r�f. du vendeur 9783866287587

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