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Wulfram Gerstner
Neuronal Dynamics
Hebbian Learning and Spiking Neurons
Connectivity Reflects Coding: a Model of Voltage-‐Based Spike-‐ Timing-‐Dependent-‐P
Neural Coding and Integrate-And-Fire Model
Eligibility Traces and Three-Factor Rules of Synaptic Plasticity
Burst-Dependent Synaptic Plasticity Can Coordinate Learning in Hierarchical Circuits
Spiking Neuron Models Single Neurons, Populations, Plasticity
Optimal Stimulation Protocol in a Bistable Synaptic Consolidation Model
Neuronal Dynamics: from Single Neurons to Networks and Models of Cognition Wulfram Gerstner, Werner M
Voltage-Based Inhibitory Synaptic Plasticity: Network Regulation, Diversity, and Flexibility
Deep Learning with Spike-Assisted Contextual Information Extraction
Neuronal Dynamics
Hebbian Plasticity Requires Compensatory Processes on Multiple Timescales
Fitting Summary Statistics of Neural Data with a Differentiable Spiking Network Simulator
Neural Codes: Firing Rates and Beyond
Learning Navigational Maps Through Potentiation and Modulation of Hippocampal Place Cells
Training Spiking Neurons with Isolated Spikes Coding
Does Computational Neuroscience Need New Synaptic Learning Paradigms?
Top View
Cooperation Across Timescales Between and Hebbian and Homeostatic Plasticity
Neuronal Dynamics – from Single Neurons to Networks and Models of Cognition
Population Dynamics of Spiking Neurons: Fast Transients, Asynchronous States, and Locking
Predicting Neuronal Activity with Simple Models of the Threshold Type: Adaptive Exponential Integrate-And-Fire Model with Two Compartments
Theoretical Neuroscience Sp9 Sp4 Theoretical Neuroscience Sp6 Sp7
Noise in Integrate-And-Fire Neurons: from Stochastic Input to Escape Rates
Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze–Like Environments
MATH 1370: Introduction to Computational Neuroscience
Neural Coding 2016
Generalized Integrate-And-Fire Models of Neuronal Activity Approximate
Romain Brette and Wulfram Gerstner J Neurophysiol 94:3637-3642, 2005