Tim P. Vogels

This paper shows that Dalean connectivity, stability, and nonlinear responses can generate diverse neural dynamics without data fitting.

This paper introduces a computational model showing that inhibitory-inhibitory plasticity enables stable recall of overlapping memory engrams through disinhibition.

This paper characterizes and predicts effective unsupervised plasticity rules for spiking reservoir computing by analyzing their performance on a Pong game task.

The paper shows that transcription-specific synaptic plasticity in distinct neuronal ensembles drives memory computations in mice.

This study constructs a cross-species spinal interneuron atlas and shows that Hox-dependent regionalization of circuits drives motor output diversity across vertebrate body regions.

This paper shows that noradrenaline causes a spread of association in the hippocampal cognitive map, leading to overgeneralisation errors and predicted by physiological markers.

This study shows that reducing Letm1 increases mitochondrial Ca2+ retention, overactivates metabolism, and enhances long-term memory in flies and mice.

The paper introduces a standardized ion channel model based on Hodgkin-Huxley formulation, demonstrating its ability to fit most voltage-gated models and enable parameter comparison and inference.

This study uses automated search algorithms to discover diverse synaptic plasticity rules that support memory tasks and network stability.

This paper uses evolutionary strategies to meta-learn plasticity rules in spiking networks, finding stable rules for synapse types but facing challenges with complex co-active rules.

This study shows that nightingales imitate whistle syllable durations in real-time but adjust within their natural vocal range when exposed to novel stimuli.

This paper introduces a context-modular memory network model that enhances memory capacity through context-dependent neuronal and synaptic gating, showing improved stability and accessibility of memory patterns.

The paper introduces a framework to model synaptic codependency between excitatory and inhibitory plasticity, explaining how they contribute to memory formation and stability in neural networks.

The paper proposes that non-input driven neuronal firing serves as a mechanism to protect neurons from mitochondrial toxicity by regulating metabolic products and reactive oxygen species.

The paper shows that multiple distinct plasticity rules can produce similar network dynamics, highlighting the need to consider degenerate mechanisms in plasticity studies.

The paper models how a developmental shift from synaptic depression to facilitation stabilizes neural activity and controls excitation-inhibition balance.

The paper explores transient amplification regimes in neuronal networks by manipulating eigenspectra and non-normality in upper triangular matrices, showing that imaginary distribution manipulation can lead to high amplification despite biological constraints.

This study shows that impaired spike generation and synaptic transmission in parvalbumin interneurons contribute to Dravet syndrome severity and chronic pathology in an experimental model.

This paper investigates how surrogate gradient learning performs in spiking neural networks, showing its robustness to different surrogate derivatives and the importance of regularization for effective learning.

The paper shows that inhibitory plasticity rules shape receptive fields by modulating the correlation between excitatory and inhibitory inputs, allowing context-dependent changes in neuronal responses.

This paper uses meta-learning to discover synaptic plasticity rules that can produce desired neural functions, re-discovering known rules and finding new ones for complex tasks.

This paper introduces a machine learning method using deep neural density estimators to perform Bayesian inference on neural dynamics models, demonstrating its application to various neural systems.

This paper proposes a unified neural model of working memory that integrates self-sustained neural firing and activity-silent synaptic traces to explain attention and memory effects.

The paper shows that modulating neuronal input-output gains in cortical networks can generate diverse movements and control their speed and shape.

This paper explores the role of inhibitory engrams in perception and memory, showing they regulate behavior and memory storage through balanced neural activity.

The paper introduces a framework to classify ion channel models and provides a web interface for comparing models with experimental data.