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, Valentin Marc Schmutz, Johanni Michael Brea

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, Angeliki Pantazi, Ana Stanojevic

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

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, Angeliki Pantazi, Ana Stanojevic, Giovanni Cherubini

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

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

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

Pergamon-Elsevier Science Ltd2023

Curiosity-driven exploration: foundations in neuroscience and computational modeling

Wulfram Gerstner, Alireza Modirshanechi

Curiosity refers to the intrinsic desire of humans and animals to explore the unknown, even when there is no apparent reason to do so. Thus far, no single, widely accepted definition or framework for curiosity has emerged, but there is growing consensus th ...

Cell Press2023

A dynamic attractor network model of memory formation, reinforcement and forgetting

Wulfram Gerstner, Chiara Gastaldi, Marta Boscaglia

Empirical evidence shows that memories that are frequently revisited are easy to recall, and that familiar items involve larger hippocampal representations than less familiar ones. In line with these observations, here we develop a modelling approach to pr ...

Public Library Science2023

Surprise and novelty in the brain

Wulfram Gerstner, Johanni Michael Brea, Alireza Modirshanechi, Sophia Becker

Notions of surprise and novelty have been used in various experimental and theoretical studies across multiple brain areas and species. However, 'surprise' and 'novelty' refer to different quantities in different studies, which raises concerns about whethe ...

CURRENT BIOLOGY LTD2023

Distributed and specific encoding of sensory, motor, and decision information in the mouse neocortex during goal-directed behavior

Wulfram Gerstner, Carl Petersen, Sylvain Crochet, Vahid Esmaeili, Anastasiia Oryshchuk, Alireza Modirshanechi, Christos Sourmpis

Goal-directed behaviors involve coordinated activity in many cortical areas, but whether the encoding of task variables is distributed across areas or is more specifically represented in distinct areas remains unclear. Here, we compared representations of ...

Cell Press2023

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

Kernel Memory Networks: A Unifying Framework for Memory Modeling

Wulfram Gerstner, Georgios Iatropoulos, Johanni Michael Brea

We consider the problem of training a neural network to store a set of patterns with maximal noise robustness. A solution, in terms of optimal weights and state update rules, is derived by training each individual neuron to perform either kernel classifica ...

Curran Associates2022

Approximating Relu Networks By Single-Spike Computation

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

Developing energy-saving neural network models is a topic of rapidly increasing interest in the artificial intelligence community. Spiking neural networks (SNNs) are biologically inspired models that strive to leverage the energy efficiency stemming from a ...

IEEE2022

Brain signals of a Surprise-Actor-Critic model: Evidence for multiple learning modules in human decision making

Wulfram Gerstner, Marco Philipp Lehmann, Vasiliki Liakoni, Kerstin Preuschoff, Johanni Michael Brea, Alireza Modirshanechi

Learning how to reach a reward over long series of actions is a remarkable capability of humans, and potentially guided by multiple parallel learning modules. Current brain imaging of learning modules is limited by (i) simple experimental paradigms, (ii) e ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2022

Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making

Wulfram Gerstner, Marco Philipp Lehmann, Michael Herzog, He Xu, Alireza Modirshanechi

Classic reinforcement learning (RL) theories cannot explain human behavior in the absence of external reward or when the environment changes. Here, we employ a deep sequential decision-making paradigm with sparse reward and abrupt environmental changes. To ...

2021