Publications by Wulfram Gerstner

Context selectivity with dynamic availability enables lifelong continual learning

Wulfram Gerstner, Martin Louis Lucien Rémy Barry

“You never forget how to ride a bike”, – but how is that possible? The brain is able to learn complex skills, stop the practice for years, learn other skills in between, and still retrieve the original knowledge when necessary. The mechanisms of this capab ...

Elsevier BV2025

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

High-performance deep spiking neural networks with 0.3 spikes per neuron

Wulfram Gerstner, Stanislaw Andrzej Wozniak, Ana Stanojevic, Angeliki Pantazi

Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks. This is puzzling given that theoretical resul ...

Springer Science and Business Media LLC2024

A Computational Framework for Memory Engrams

Wulfram Gerstner, Chiara Gastaldi

Memory engrams in mice brains are potentially related to groups of concept cells in human brains. A single concept cell in human hippocampus responds, for example, not only to different images of the same object or person but also to its name written down ...

Springer Cham2024

Computational models of intrinsic motivation for curiosity and creativity

Wulfram Gerstner, Alireza Modirshanechi, Sophia Becker

We link Ivancovsky et al.'s novelty-seeking model (NSM) to computational models of intrinsically motivated behavior and learning. We argue that dissociating different forms of curiosity, creativity, and memory based on the involvement of distinct intrinsic ...

2024

Learning what matters: Synaptic plasticity with invariance to second-order input correlations

Wulfram Gerstner, Carlos Stein Naves de Brito

Cortical populations of neurons develop sparse representations adapted to the statistics of the environment. To learn efficient population codes, synaptic plasticity mechanisms must differentiate relevant latent features from spurious input correlations, w ...

2024

Simplification of spiking neural network models

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

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

Fast adaptation to rule switching using neuronal surprise

Wulfram Gerstner, Martin Louis Lucien Rémy Barry

In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signa ...

2024

An exact mapping from ReLU networks to spiking neural networks

Wulfram Gerstner, Stanislaw Andrzej Wozniak, Ana Stanojevic, Giovanni Cherubini, Angeliki Pantazi

Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propose an ...

2023

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