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Wulfram Gerstner

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Related publications (124)

Page 6 of 7

Simplification of spiking neural network models

Wulfram Gerstner, Henry Markram, Eilif Benjamin Muller, James Gonzalo King, Christian Antonio Pozzorini, Csaba Erö, Christian Andreas Rössert, Idan Segev, Willem Anna Mark Wybo

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 ...

2024

The Silent Period of Evidence Integration in Fast Decision Making

Michael Herzog, Wulfram Gerstner, Johannes Rüter

In a typical experiment on decision making, one out of two possible stimuli is displayed and observers decide which one was presented. Recently, Stanford and colleagues (2010) introduced a new variant of this classical one-stimulus presentation paradigm to ...

Public Library of Science2013

Spike-timing prediction in cortical neurons with active dendrites

Wulfram Gerstner, Richard Naud, Brice Bathellier

A complete single-neuron model must correctly reproduce the firing of spikes and bursts. We present a study of a simplified model of deep pyramidal cells of the cortex with active dendrites. We hypothesized that we can model the soma and its apical dendrit ...

Frontiers Research Foundation2014

Computational models of episodic-like memory in food-caching birds

Wulfram Gerstner, Johanni Michael Brea

How the 'what', 'where', and 'when' of past experiences are stored in episodic memories and retrieved for suitable decisions remains unclear. In an effort to address these questions, the authors present computational models of neural networks that behave l ...

Nature Portfolio2023

Synaptic consolidation: from synapses to behavioral modeling

Wulfram Gerstner, Friedemann Zenke, Lorric Ziegler

Synaptic plasticity, a key process for memory formation, manifests itself across different time scales ranging from a few seconds for plasticity induction up to hours or even years for consolidation and memory retention. We developed a three-layered model ...

Society for Neuroscience2015

An exact mapping from ReLU networks to spiking neural networks' [Neural Networks(vol168, pg 74, 2023)

Wulfram Gerstner, Stanislaw Andrzej Wozniak, Angeliki Pantazi, Guillaume Emmanuel Fernand Bellec, Ana Stanojevic

Pergamon-Elsevier Science Ltd2023

Dependence of the spike-triggered average voltage on membrane response properties

Wulfram Gerstner, Laurent Badel

The spike-triggered average voltage (STV) is an experimentally measurable quantity that is determined by both the membrane response properties and the statistics of the synaptic drive. Here, the form of the STV is modelled for neurons with three distinct t ...

Elsevier2006

Modeling spatial and temporal aspects of visual backward masking

Michael Herzog, Wulfram Gerstner, Gediminas Luksys

Visual backward masking is a versatile tool for understanding principles and limitations of visual information processing in the human brain. However, the mechanisms underlying masking are still poorly understood. In the current contribution, the authors s ...

American Psychological Association2008

Spike-timing prediction in a neuron model with active dendrites

Wulfram Gerstner, Richard Naud, Brice Bathellier

A complete model for single neurons must reproduce correctly the firing of spikes and bursts. Yet it must remain simple enough to be tractable in large-scale simulations. We present a study of a simplified model of Layer V pyramidal cells of the cortex wit ...

2009

Extracting non-linear integrate-and-fire models from experimental data using dynamic I – V curves

Wulfram Gerstner, Carl Petersen, Laurent Badel, Sandrine Lefort

The dynamic I–V curve method was recently introduced for the efficient experimental generation of reduced neuron models. The method extracts the response properties of a neuron while it is subject to a naturalistic stimulus that mimics in vivo-like fluctua ...

Springer Verlag2008

Integrating Hebbian and homeostatic plasticity: the current state of the field and future research directions

Wulfram Gerstner, Lu Chen

We summarize here the results presented and subsequent discussion from the meeting on Integrating Hebbian and Homeostatic Plasticity at the Royal Society in April 2016. We first outline the major themes and results presented at the meeting. We next provide ...

Royal Soc2017

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

Wulfram Gerstner

2010

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

Wulfram Gerstner

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 ...

American Physiological Society2005

Biological Learning: Synaptic Plasticity, Hebb Rule and Spike Timing Dependent Plasticity

Wulfram Gerstner

Springer Science+Business Media2017

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

Wulfram Gerstner, Johanni Michael Brea, Valentin Marc Schmutz

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 ...

American Physical Society (APS)2025

A taxonomy of surprise definitions

Wulfram Gerstner, Johanni Michael Brea, Alireza Modirshanechi

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 ...

2022

Temporal whitening by power-law adaptation in neocortical neurons

Wulfram Gerstner, Richard Naud, Skander Mensi, Christian Antonio Pozzorini

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 ...

Nature Publishing Group2013

Wulfram Gerstner

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Simplification of spiking neural network models

The Silent Period of Evidence Integration in Fast Decision Making

Spike-timing prediction in cortical neurons with active dendrites

Computational models of episodic-like memory in food-caching birds

Synaptic consolidation: from synapses to behavioral modeling

An exact mapping from ReLU networks to spiking neural networks' [Neural Networks(vol168, pg 74, 2023)

Dependence of the spike-triggered average voltage on membrane response properties

Modeling spatial and temporal aspects of visual backward masking

Spike-timing prediction in a neuron model with active dendrites

Extracting non-linear integrate-and-fire models from experimental data using dynamic I – V curves

Integrating Hebbian and homeostatic plasticity: the current state of the field and future research directions

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

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

Biological Learning: Synaptic Plasticity, Hebb Rule and Spike Timing Dependent Plasticity

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