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Wulfram Gerstner

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Related publications (124)

Page 3 of 7

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

Hebbian plasticity requires compensatory processes on multiple timescales

Wulfram Gerstner, Friedemann Zenke

We review a body of theoretical and experimental research on Hebbian and homeostatic plasticity, starting from a puzzling observation: while homeostasis of synapses found in experiments is a slow compensatory process, most mathematical models of synaptic p ...

Royal Soc2017

Paradoxical Evidence Integration in Rapid Decision Processes

Michael Herzog, Wulfram Gerstner, Johannes Rüter, Nicolas Marcille

Decisions about noisy stimuli require evidence integration over time. Traditionally, evidence integration and decision making are described as a one-stage process: a decision is made when evidence for the presence of a stimulus crosses a threshold. Here, w ...

Public Library of Science2012

Theory and Simulation in Neuroscience

Wulfram Gerstner

Modeling work in neuroscience can be classified using two different criteria. The first one is the complexity of the model, ranging from simplified conceptual models that are amenable to mathematical analysis to detailed models that require simulations in ...

American Association for the Advancement of Science2012

Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks

Wulfram Gerstner, Friedemann Zenke

Synaptic plasticity, the putative basis of learning and memory formation, manifests in various forms and across different timescales. Here we show that the interaction of Hebbian homosynaptic plasticity with rapid non-Hebbian heterosynaptic plasticity is, ...

Nature Publishing Group2015

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

Wulfram Gerstner, Guillaume Emmanuel Fernand Bellec, 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

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

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

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

Optimal Spike-Timing Dependent Plasticity for Precise Action Potential Firing in Supervised Learing

Wulfram Gerstner

In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimizes via gradient ascent the likelihood of postsynaptic firing at one or sever ...

2005

Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation

Wulfram Gerstner, Johanni Michael Brea, Dane Sterling Corneil

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

An online Hebbian learning rule that performs Independent Component Analysis

Wulfram Gerstner

Independent component analysis (ICA) is a powerful method to decouple signals. Most of the algorithms performing ICA do not consider the temporal correlations of the signal, but only higher moments of its amplitude distribution. Moreover, they require some ...

MIT Press2008

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

Improved Similarity Measures for Small Sets of Spike Trains

Wulfram Gerstner, Richard Naud, Joao Emanuel Felipe Gerhard, Skander Mensi

Multiple measures have been developed to quantify the similarity between two spike trains. These measures have been used for the quantification of the mismatch between neuron models and experiments as well as for the classification of neuronal responses in ...

Massachusetts Institute of Technology Press2011

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

Wulfram Gerstner, Moritz Deger, Alexander Kevin Seeholzer

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

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

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

Code-specific policy gradient rules for spiking neurons

Wulfram Gerstner, Guillaume Hennequin

Although it is widely believed that reinforcement learning is a suitable tool for describing behavioral learning, the mechanisms by which it can be implemented in networks of spiking neurons are not fully understood. Here, we show that different learning r ...

Massachusetts Institute of Technology Press2009

Variational Learning for Recurrent Spiking Networks

Wulfram Gerstner, Daniël Pieter Wierstra, Danilo Jimenez Rezende

We derive a plausible learning rule updating the synaptic efficacies for feedforward, feedback and lateral connections between observed and latent neurons. Operating in the context of a generative model for distributions of spike sequences, the learning me ...

2011

Exponentially long orbits in Hopfield neural networks

Wulfram Gerstner, Johanni Michael Brea, Samuel Pavio Muscinelli

We show that Hopfield neural networks with synchronous dynamics and asymmetric weights admit stable orbits that form sequences of maximal length. For N units, these sequences have length T = 2^N; that is, they cover the full state space. We present a mathe ...

Massachusetts Institute of Technology Press2017

Short-term-plasticity orchestrates the response of pyramidal cells and interneurons to population bursts

Wulfram Gerstner, Henry Markram, Gilad Silberberg

The synaptic drive from neuronal populations varies considerably over short time scales. Such changes in the presynaptic rate trigger many temporal processes absent under steady-state conditions. This paper examines the differential impact of pyramidal cel ...

2005

Modelling Path Integrator Recalibration Using Hippocampal Place cells

Wulfram Gerstner, Ricardo Andres Chavarriaga Lozano, Thomas Strösslin

The firing activities of place cells in the rat hippocampus exhibit strong correlations to the animal's location. External (e.g. visual) as well as internal (proprioceptive and vestibular) sensory information take part in controlling hippocampal place fiel ...

Springer-Verlag GmbH2005

Signal buffering in random networks of spiking neurons: microscopic vs. macroscopic phenomena

Wulfram Gerstner, Julien Mayor

In randomly connected networks of pulse-coupled elements a time-dependent input signal can be buffered over a short time. We studied the signal buffering properties in simulated networks as a function of the networks' state, characterized by both the Lyapu ...

2005