Distributed Dynamics on Networks and their Applications in Network Communities, Sensor Networks and EEG's

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Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

Correlation Clustering is a fundamental and widely-studied problem in unsupervised learning and data mining. The input is a graph and the goal is to construct a clustering minimizing the number of inter-cluster edges plus the number of missing intra-cluste ...

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Aim: Adolescents born very preterm (VPT;

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Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

DiffUrb is an interdisciplinary research collaboration aiming at describing and understanding how urban differences in terms of diverse population groups are dealt with across five European cities: Geneva, Brussels, Hamburg, Turin, and Zurich. To this end, ...

Subcortical brain structures such as the basal ganglia or the thalamus are involved in regulating motor and cognitive behavior. However, their contribution to perceptual consciousness is still unclear, due to the inherent difficulties of recording subcorti ...

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Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the prediction error is an important component that compares the expected and actual rewards. Dopamine plays a critical role in encoding these prediction errors. In my ...

Distributed Dynamics on Networks and their Applications in Network Communities, Sensor Networks and EEG's

Chattez avec Graph Search

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Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

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Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

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