Fast adaptation to rule switching using neuronal surprise

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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 ...

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In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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Fast adaptation to rule switching using neuronal surprise

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

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Unveiling the complexity of learning and decision-making

Driving and suppressing the human language network using large language models

Subcortical correlates of consciousness with human single neuron recordings

Investigating neural resource allocation in the sensorimotor control of extra limbs

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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The neural correlates of topographical disorientation-a lesion analysis study

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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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Generalization of Scaled Deep ResNets in the Mean-Field Regime

Incorporating Projective Geometry into Deep Learning

Safe Deep Neural Networks

Enabling Uncertainty Estimation in Iterative Neural Networks

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Small-scale robotic devices for medical interventions in the brain