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Frontiers in Computational Neuroscience|August 28, 2020
Emergence of Stable Synaptic Clusters on Dendrites Through Synaptic RewiringThomas Limbacher, Robert LegensteinIEEE Transactions on Neural Networks and Learning Systems|December 19, 2023
Memory-Dependent Computation and Learning in Spiking Neural Networks Through Hebbian PlasticityThomas Limbacher, Ozan Ozdenizci, Robert LegensteinNeuroscience|October 17, 2021
Dendritic Computing: Branching Deeper into Machine LearningJyotibdha Acharya, Arindam Basu, Robert Legenstein, et al.Nature Communications|February 1, 2025
Rapid learning with phase-change memory-based in-memory computing through learning-to-learnThomas Ortner, Horst Petschenig, Athanasios Vasilopoulos, et al.The Journal of Neuroscience : the Official Journal of the Society for Neuroscience|July 29, 2011
Branch-specific plasticity enables self-organization of nonlinear computation in single neuronsRobert Legenstein, Wolfgang MaassNeural Networks : the Official Journal of the International Neural Network Society|May 23, 2007
Edge of chaos and prediction of computational performance for neural circuit modelsRobert Legenstein, Wolfgang MaassPlos Computational Biology|October 24, 2014
Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environmentRobert Legenstein, Wolfgang MaassFrontiers in Neuroscience|February 15, 2024
Context association in pyramidal neurons through local synaptic plasticity in apical dendritesMaximilian Baronig, Robert LegensteinNeural Computation|November 30, 2007
On the classification capability of sign-constrained perceptronsRobert Legenstein, Wolfgang MaassFrontiers in Neuroscience|January 8, 2015
A compound memristive synapse model for statistical learning through STDP in spiking neural networksJohannes Bill, Robert LegensteinPageof 5