Publications de Wulfram Gerstner

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We introduce a two-dimensional integrate-and-fire model that combines an exponential spike mechanism with an adaptation equation, based on recent theoretical findings. We describe a systematic method to estimate its parameters with simple electrophysiologi ...

Can spiking neural networks (SNNs) approximate the dynamics of recurrent neural networks? Arguments in classical mean-field theory based on laws of large numbers provide a positive answer when each neuron in the network has many “duplicates”, i.e., other n ...

Surprising events trigger measurable brain activity and influence human behavior by affecting learning, memory, and decision-making. Currently there is, however, no consensus on the definition of surprise. Here we identify 18 mathematical definitions of su ...

Spike-frequency adaptation (SFA) is widespread in the CNS, but its function remains unclear. In neocortical pyramidal neurons, adaptation manifests itself by an increase in the firing threshold and by adaptation currents triggered after each spike. Combini ...

The simplification of neural network models is described. For example, a method for simplifying a neural network model includes providing the neural network model to be simplified, defining a first temporal filter for the conveyance of input from a neuron ...

In dynamical models of cortical networks, the recurrent connectivity can amplify the input given to the network in two distinct ways. One is induced by the presence of near-critical eigenvalues in the connectivity matrix W, producing large but slow activit ...

Hebbian changes of excitatory synapses are driven by and further enhance correlations between pre- and postsynaptic activities. Hence, Hebbian plasticity forms a positive feedback loop that can lead to instability in simulated neural networks. To keep acti ...

Wulfram Gerstner

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From Hebb rules to Spike-Timing-Dependent Plasticity: a personal account

Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity

Emergent Rate-Based Dynamics in Duplicate-Free Populations of Spiking Neurons

A taxonomy of surprise definitions

Temporal whitening by power-law adaptation in neocortical neurons

Special issue on quantitative neuron modeling

Spike-timing-dependent plasticity: a comprehensive overview

Simplification of neural network models

Non-normal amplification in random balanced neuronal networks

Beyond Pair-Based STDP: a Phenomenological Rule for Spike Triplet and Frequency Effects

Modelling feature-integration in human vision with drift diffusion models

Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector

Populations of spiking neurons

The role of interconnected hub neurons in cortical dynamics

A two stage model for perceptual decision making

Spiking Neurons

Surprise minimization as a learning strategy in neural networks

Neuromodulation by surprise: a biologically plausible model of the learning rate dynamics

Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns

A two stage model for perceptual decision making