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Wulfram Gerstner

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Related publications (129)

Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances

Wulfram Gerstner, Clément Hongler, Johanni Michael Brea, Francesco Spadaro, Berfin Simsek, Arthur Jacot

We study how permutation symmetries in overparameterized multi-layer neural networks generate `symmetry-induced' critical points. Assuming a network with

L

layers of minimal widths

r_1^*, \ldots, r_{L-1}^*

reaches a zero-loss minimum at $ r_1^*! \c ...

2021

Local plasticity rules can learn deep representations using self-supervised contrastive predictions

Wulfram Gerstner, Jean Ventura, Bernd Albert Illing

Learning in the brain is poorly understood and learning rules that respect biological constraints, yet yield deep hierarchical representations, are still unknown. Here, we propose a learning rule that takes inspiration from neuroscience and recent advances ...

2021

When shared concept cells support associations: Theory of overlapping memory engrams

Wulfram Gerstner, Tilo Schwalger, Chiara Gastaldi, Emanuela De Falco

Assemblies of neurons, called concepts cells, encode acquired concepts in human Medial Temporal Lobe. Those concept cells that are shared between two assemblies have been hypothesized to encode associations between concepts. Here we test this hypothesis in ...

2021

Rapid suppression and sustained activation of distinct cortical regions for a delayed sensory-triggered motor response

Wulfram Gerstner, Carl Petersen, Sylvain Crochet, Samuel Pavio Muscinelli, Georgios Foustoukos, Vahid Esmaeili, Keita Tamura, Anastasiia Oryshchuk, Marta Boscaglia, Yanqi Liu, Alireza Modirshanechi

The neuronal mechanisms generating a delayed motor response initiated by a sensory cue remain elusive. Here, we tracked the precise sequence of cortical activity in mice transforming a brief whisker stimulus into delayed licking using wide-field calcium im ...

2021

A functional model of adult dentate gyrus neurogenesis

Wulfram Gerstner, Olivia Gozel

In adult dentate gyrus neurogenesis, the link between maturation of newborn neurons and their function, such as behavioral pattern separation, has remained puzzling. By analyzing a theoretical model, we show that the switch from excitation to inhibition of ...

2021

Learning in Volatile Environments With the Bayes Factor Surprise

Wulfram Gerstner, Vasiliki Liakoni, Johanni Michael Brea, Alireza Modirshanechi

Surprise-based learning allows agents to rapidly adapt to nonstationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting o ...

MIT PRESS2021

Mesoscopic population equations for spiking neural networks with synaptic short-term plasticity

Wulfram Gerstner, Valentin Marc Schmutz, Tilo Schwalger

Coarse-graining microscopic models of biological neural networks to obtain mesoscopic models of neural activities is an essential step towards multi-scale models of the brain. Here, we extend a recent theory for mesoscopic population dynamics with static s ...

2020

Dendritic Voltage Recordings Explain Paradoxical Synaptic Plasticity: A Modeling Study

Wulfram Gerstner, Claire Meissner-Bernard, Matthias Chinyen Tsai, Laureline Logiaco

Experiments have shown that the same stimulation pattern that causes Long-Term Potentiation in proximal synapses, will induce Long-Term Depression in distal ones. In order to understand these, and other, surprising observations we use a phenomenological mo ...

FRONTIERS RESEARCH FOUNDATION2020

On the choice of metric in gradient-based theories of brain function

Wulfram Gerstner, Jean-Pascal Théodor Pfister, Johanni Michael Brea

The idea that the brain functions so as to minimize certain costs pervades theoretical neuroscience. Because a cost function by itself does not predict how the brain finds its minima, additional assumptions about the optimization method need to be made to ...

2020

Biologically plausible deep learning – but how far can we go with shallow networks?

Wulfram Gerstner, Johanni Michael Brea, Bernd Albert Illing

Training deep neural networks with the error backpropagation algorithm is considered implausible from a biological perspective. Numerous recent publications suggest elaborate models for biologically plausible variants of deep learning, typically defining s ...

2019

How single neuron properties shape chaotic dynamics and signal transmission in random neural networks

Wulfram Gerstner, Tilo Schwalger, Samuel Pavio Muscinelli

While most models of randomly connected neural networks assume single-neuron models with simple dynamics, neurons in the brain exhibit complex intrinsic dynamics over multiple timescales. We analyze how the dynamical properties of single neurons and recurr ...

2019

Optimal Stimulation Protocol in a Bistable Synaptic Consolidation Model

Wulfram Gerstner, Chiara Gastaldi, Samuel Pavio Muscinelli

Synaptic changes induced by neural activity need to be consolidated to maintain memory over a timescale of hours. In experiments, synaptic consolidation can be induced by repeating a stimulation protocol several times and the effectiveness of consolidation ...

2019

Stability of working memory in continuous attractor networks under the control of short-term plasticity

Wulfram Gerstner, Alexander Kevin Seeholzer, Moritz Deger

Continuous attractor models of working-memory store continuous-valued information in continuous state-spaces, but are sensitive to noise processes that degrade memory retention. Short-term synaptic plasticity of recurrent synapses has previously been shown ...

2019

One-shot learning and behavioral eligibility traces in sequential decision making

Wulfram Gerstner, Marco Philipp Lehmann, Michael Herzog, Vasiliki Liakoni, Kerstin Preuschoff, He Xu

In many daily tasks, we make multiple decisions before reaching a goal. In order to learn such sequences of decisions, a mechanism to link earlier actions to later reward is necessary. Reinforcement learning (RL) theory suggests two classes of algorithms s ...

2019

Balancing New against Old Information: The Role of Puzzlement Surprise in Learning

Wulfram Gerstner, Mohammadjavad Faraji, Kerstin Preuschoff

2018

Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules

Wulfram Gerstner, Marco Philipp Lehmann, Vasiliki Liakoni, Dane Sterling Corneil, Johanni Michael Brea

2018

Excitable neuronal assemblies with adaptation as a building block of brain circuits for velocity-controlled signal propagation

Wulfram Gerstner, Hesam Setareh, Moritz Deger

2018

Multicontact Co-operativity in Spike-Timing-Dependent Structural Plasticity Stabilizes Networks

Wulfram Gerstner, Alexander Kevin Seeholzer, Moritz Deger

Oxford University Press2018

Multi-Timescale Memory Dynamics Extend Task Repertoire in a Reinforcement Learning Network With Attention-Gated Memory

Wulfram Gerstner, Aditya Gilra

2018

Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation

Wulfram Gerstner, Dane Sterling Corneil, Johanni Michael Brea

Modern reinforcement learning algorithms reach super-human performance on many board and video games, but they are sample inefficient, i.e. they typically require significantly more playing experience than humans to reach an equal performance level. To imp ...

2018

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