Wulfram Gerstner

Remembering the "When": Hebbian Memory Models for the Time of Past Events

The paper develops Hebbian memory models to simulate how neural mechanisms enable remembering the time of past events, showing multiple biologically plausible implementations.

Representational similarity modulates neural and behavioral signatures of novelty

This study presents a model explaining how stimulus representations affect novelty signals in the brain and behavior, using mouse data to show effects on visual cortex responses and exploration.

Linking Neural Manifolds to Circuit Structure in Recurrent Networks

This paper connects cortical circuit models with low-dimensional population dynamics using a recurrent spiking network model and demonstrates a method to retrieve circuit structure from recordings.

Biologically informed cortical models predict optogenetic perturbations

The paper develops a biologically informed recurrent neural network to predict optogenetic perturbations in cortical circuits, showing improved performance over generic models.

Even if suboptimal, novelty drives human exploration

This study shows humans persistently explore stochastic environments despite suboptimal strategies, driven by novelty rather than optimal information gain.

Sequential critical periods support efficient local representation learning in a model of visual processing

This paper shows that staggered critical periods improve local learning in a visual processing model, enhancing representation quality and efficiency.

Two-factor synaptic consolidation reconciles robust memory with pruning and homeostatic scaling

This paper proposes a computational model of memory consolidation that integrates replay and two-factor synapses to explain experimental observations of homeostatic scaling, pruning, and memory robustness.

Fast Adaptation to Rule Switching using Neuronal Surprise

This paper introduces a spiking neural network model that uses a surprise signal to modulate synaptic plasticity, enabling stable learning during rule switching.

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

This paper develops a dynamic attractor network model to explain how memory representations change with stimulus frequency, showing that frequent stimulation increases assembly size and reduces interference, while unstimulated connections become labile, enabling forgetting.

Surprise: a unified theory and experimental predictions

The paper identifies 16 mathematical definitions of surprise, classifies them into four categories, and proposes a combined definition with experimental predictions.

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

The paper tests a hypothesis that shared neurons between concept cell assemblies encode associations, using a computational model of attractor networks and experimental data.

Adult dentate gyrus neurogenesis: a functional model

This paper develops a theoretical model explaining how adult-born dentate granule cells integrate into neural networks and support pattern separation through changes in GABAergic input.

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

This study introduces a deep sequential decision-making paradigm to explain human behavior by incorporating novelty and surprise, showing that model-free actions dominate decisions despite the availability of a world-model.

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

This study identifies cortical regions involved in delayed sensory-triggered motor responses in mice using calcium imaging and electrophysiology.

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

This paper investigates how short-term synaptic plasticity affects working memory stability in continuous attractor networks, showing facilitation stabilizes memory while depression increases volatility.

The temporal paradox of Hebbian learning and homeostatic plasticity

The paper examines the interaction between Hebbian and homeostatic plasticity, finding that slow homeostatic processes are insufficient to stabilize Hebbian-driven instabilities and proposing the need for rapid compensatory mechanisms.