Optimizing the Conversion of Continuous-Valued Networks to Spiking Neural Networks

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Alternative title: Optimierung der Konvertierung kontinuierlich aktivierter Netzwerke zu gepulsten neuronalen Netzwerken

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Notes: München, Technische Universität München, Dissertation, 2024

Keyword: Neuronales Netz
Pulsverarbeitendes neuronales Netz
Modell
Nervennetz
Maschinelles Lernen
Rekursives neuronales Netz

Event: Veröffentlichung

(where): München

(who): Universitätsbibliothek der TU München

(when): 2024

Creator: Müller, Etienne

Contributor: Knoll, Alois Christian
Diesmann, Markus

URN: urn:nbn:de:bvb:91-diss-20241115-1703327-1-4

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:35 AM CEST

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Associated

Müller, Etienne
Knoll, Alois Christian
Diesmann, Markus
Universitätsbibliothek der TU München

Time of origin

2024

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Modeling and simulation of multi-scale spiking neuronal networks

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Theory of neuronal spike densities for synchronous activity in cortical feed-forward networks

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Hochschulschrift

Modeling and simulation of multi-scale spiking neuronal networks

Coherent noise enables probabilistic sequence replay in spiking neuronal networks

Sequence learning, prediction, and replay in networks of spiking neurons

High storage capacity of synfire chains in large-scale cortical networks of conductance-based spiking neurons

Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions

Towards a unified theory of correlations in recurrent neural networks

A spiking temporal-difference learning model based on dopamine-modulated plasticity

Correlated fluctuations in neural networks on the microscopic and mesoscopic scale

The Correlation Structure of Local Neuronal Networks Intrinsically Results from Recurrent Dynamics

Random wiring limits the development of functional structure in large recurrent neuronal networks

Theory of neuronal spike densities for synchronous activity in cortical feed-forward networks

Bifurcation analysis of synchronization dynamics in cortical feed-forward networks in novel coordinates

Hochschulschrift

Modeling and simulation of multi-scale spiking neuronal networks

Coherent noise enables probabilistic sequence replay in spiking neuronal networks

Sequence learning, prediction, and replay in networks of spiking neurons

High storage capacity of synfire chains in large-scale cortical networks of conductance-based spiking neurons

Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions

Towards a unified theory of correlations in recurrent neural networks

A spiking temporal-difference learning model based on dopamine-modulated plasticity

Correlated fluctuations in neural networks on the microscopic and mesoscopic scale

The Correlation Structure of Local Neuronal Networks Intrinsically Results from Recurrent Dynamics

Random wiring limits the development of functional structure in large recurrent neuronal networks

Theory of neuronal spike densities for synchronous activity in cortical feed-forward networks

Bifurcation analysis of synchronization dynamics in cortical feed-forward networks in novel coordinates

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Optimizing the Conversion of Continuous-Valued Networks to Spiking Neural Networks

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Other Objects (12)

Modeling and simulation of multi-scale spiking neuronal networks

Coherent noise enables probabilistic sequence replay in spiking neuronal networks

Sequence learning, prediction, and replay in networks of spiking neurons

High storage capacity of synfire chains in large-scale cortical networks of conductance-based spiking neurons

Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions

Towards a unified theory of correlations in recurrent neural networks

A spiking temporal-difference learning model based on dopamine-modulated plasticity

Correlated fluctuations in neural networks on the microscopic and mesoscopic scale

The Correlation Structure of Local Neuronal Networks Intrinsically Results from Recurrent Dynamics

Random wiring limits the development of functional structure in large recurrent neuronal networks

Theory of neuronal spike densities for synchronous activity in cortical feed-forward networks

Bifurcation analysis of synchronization dynamics in cortical feed-forward networks in novel coordinates

Modeling and simulation of multi-scale spiking neuronal networks

Coherent noise enables probabilistic sequence replay in spiking neuronal networks

Sequence learning, prediction, and replay in networks of spiking neurons

High storage capacity of synfire chains in large-scale cortical networks of conductance-based spiking neurons

Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions

Towards a unified theory of correlations in recurrent neural networks

A spiking temporal-difference learning model based on dopamine-modulated plasticity

Correlated fluctuations in neural networks on the microscopic and mesoscopic scale

The Correlation Structure of Local Neuronal Networks Intrinsically Results from Recurrent Dynamics

Random wiring limits the development of functional structure in large recurrent neuronal networks

Theory of neuronal spike densities for synchronous activity in cortical feed-forward networks

Bifurcation analysis of synchronization dynamics in cortical feed-forward networks in novel coordinates

Modeling and simulation of multi-scale spiking neuronal networks

Coherent noise enables probabilistic sequence replay in spiking neuronal networks

Sequence learning, prediction, and replay in networks of spiking neurons

High storage capacity of synfire chains in large-scale cortical networks of conductance-based spiking neurons

Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions

Towards a unified theory of correlations in recurrent neural networks

A spiking temporal-difference learning model based on dopamine-modulated plasticity

Correlated fluctuations in neural networks on the microscopic and mesoscopic scale

The Correlation Structure of Local Neuronal Networks Intrinsically Results from Recurrent Dynamics

Random wiring limits the development of functional structure in large recurrent neuronal networks

Theory of neuronal spike densities for synchronous activity in cortical feed-forward networks

Bifurcation analysis of synchronization dynamics in cortical feed-forward networks in novel coordinates

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