Capturing behavioral and neural variability in multi-area spiking neuronal network models

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Instruction-tuning is a widely adopted finetuning method that enables large language models (LLMs) to generate output that more closely resembles human responses. However, no studies have shown that instruction-tuning actually teaches LLMs to process langu ...

This manuscript details and extends the system identification methods leveraging the backpropagation (SIMBa) toolbox presented in previous work, which uses well-established machine learning tools for discrete-time linear multistep-ahead state-space system ...

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During fear learning, defensive behaviors like freezing need to be finely balanced in the presence or absence of threat-predicting cues (conditioned stimulus, CS). Nevertheless, the circuits underlying such balancing are largely unknown. Here, we investiga ...

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Capturing behavioral and neural variability in multi-area spiking neuronal network models

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Parametrizations of all stable closed-loop responses: From theory to neural network control design

A Spatiotemporal Analysis of Brain Dynamics, a New Feature for Sleep and Seizure State Discrimination

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Reliable data-driven decision-making through optimal transport

Aggregating Spatial and Photometric Context for Photometric Stereo

Drone‐Based Solar Cell Inspection With Autonomous Deep Learning

Instruction-tuned LLMs with World Knowledge are More Aligned to the Human Brain

SIMBa: System Identification Methods Leveraging Backpropagation

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Solving Non-linear Kolmogorov Equations in Large Dimensions by Using Deep Learning: A Numerical Comparison of Discretization Schemes

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