Publications by Wulfram Gerstner

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“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 ...

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 ...

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 ...

Computational neuroscience is dominated by a few paradigmatic models, but it remains an open question whether the existing modelling frameworks are sufficient to explain observed behavioural phenomena in terms of neural implementation. We take learning and ...

Populations of neurons in motor cortex engage in complex transient dynamics of large amplitude during the execution of limb movements. Traditional network models with stochastically assigned synapses cannot reproduce this behavior. Here we introduce a clas ...

How learning and memory is achieved in the brain is a central question in neuroscience. Key to today’s research into information storage in the brain is the concept of synaptic plasticity, a notion that has been heavily influenced by Hebb’s (1949) postulat ...

How well are single-cell properties reproduced by the present-day neuronal models? Recently, several labs have approached this question by assessing the quality of the models with respect to spike timing prediction or characteristic features of the voltage ...

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 ...

A good approximation to the integrate-and-fire model with diffusive noise can be obtained using a noisy threshold model. This allows the response of a population of noisy neurons to a current transient to be described using a linear filter. Here we apply these analytical results to the peristimulus time histogram (PSTH) of a single neuron. The effect of the noise on the PSTH in our model is similar to that seen in experimental findings of Poliakov et al. (J. Physiol., Part 1,495 (1996) 143–157) on hypogloss

Several methods and algorithms have recently been proposed that allow for the systematic evaluation of simple neuron models from intracellular or extracellular recordings. Models built in this way generate good quantitative predictions of the future activi ...

The storage and short-term memory capacities of recurrent neural networks of spiking neurons are investigated. We demonstrate that it is possible to process online many superimposed streams of input. This is despite the fact that the stored information is spread throughout the network. We show that simple output structures are powerful enough to extract the diffuse information from the network. The dimensional blow up, which is crucial in kernel methods, is efficiently achieved by the dynamics of the networ

Many activity dependent learning rules have been proposed in order to model long-term potentiation (LTP). Our aim is to derive a spike time dependent learning rule from a probabilistic optimality criterion. Our approach allows us to obtain quantitative results in terms of a learning window. This is done by maximising a given likelihood function with respect to the synaptic weights. The resulting weight adaptation is compared with experimental results

A model that is consistent with several neurophysiological properties of biological head-direction cells is presented. The dynamics of the system is primarily controlled by idiothetic signals which determine the direction selectivity property. By means of LTP correlation learning, allothetic cues are incorporated to stabilize the direction representation over time. The interaction between allothetic and idiothetic signals to control head-direction cells is studied. Experimental results obtained by validatin

Several studies in rats support the idea of multiple neural systems competing to select the best action for reaching a goal or food location. Locale navigation strategies, necessary for reaching invisible goals, seem to be mediated by the hippocampus and t ...

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 ...

We studied the hypothesis that synaptic dynamics is controlled by three basic principles: (1) synapses adapt their weights so that neurons can effectively transmit information, (2) homeostatic processes stabilize the mean firing rate of the postsynaptic ne ...

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 ...

The last decade has experienced a vivid enthusiasm to unravel the mystery of consciousness believed to be one of the major puzzles of humankind. We share this enthusiasm. Still,we feel that current models are incomplete suffering from a problem that we cal ...

A major challenge to understanding behavior is how the nervous system allows the learning of behavioral sequences that can occur over arbitrary timescales, ranging from milliseconds up to seconds, using a fixed millisecond learning rule. This article describes some potential solutions, and then focuses on a study by Mehta et al. that could contribute towards solving this puzzle. They have discovered that an experience-dependent asymmetric shape of hippocampal receptive fields combined with oscillatory inhib

Reconsolidation of memories has mostly been studied at the behavioral and molecular level. Here, we put forward a simple extension of existing computational models of synaptic consolidation to capture hippocampal slice experiments that have been interprete ...

Wulfram Gerstner

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Context selectivity with dynamic availability enables lifelong continual learning

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

A Computational Framework for Memory Engrams

Does computational neuroscience need new synaptic learning paradigms?

Optimal Control of Transient Dynamics in Balanced Networks Supports Generation of Complex Movements

A history of spike-timing-dependent plasticity

Quantitative single-neuron modeling: competition 2008

A functional model of adult dentate gyrus neurogenesis

Effect of noise on neuron transient response

A benchmark test for a quantitative assessment of simple neuron models

Online processing of multiple inputs in a sparsely-connected recurrent neural network

Optimal Hebbian Learning: a Probabilistic Point of View

Spatial orientation in navigating agents: Modeling head-direction cells

A computational model of parallel navigation systems in rodents

Kernel Memory Networks: A Unifying Framework for Memory Modeling

Optimality Model of Unsupervised Spike-Timing Dependent Plasticity: Synaptic Memory and Weight Distribution

Curiosity-driven exploration: foundations in neuroscience and computational modeling

Consciousness & the Small Network Argument

Coding and Learning of behavioral sequences

A Model of Synaptic Reconsolidation