Genetic representation and evolvability of modular neural controllers

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Topographical disorientation refers to the selective inability to orient oneself in familiar surroundings. However, to date its neural correlates remain poorly understood. Here we use quantitative lesion analysis and a lesion network mapping approach in or ...

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

2

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Hybrid perovskites have attracted enormous attention in the next generation resistive switching (RS) memristor for the artificial synapses, owing to their ambipolar charge transport, long diffusion length, and tunable visible bandgap. However, the variable ...

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On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability impr ...

One prominent approach toward resolving the adversarial vulnerability of deep neural networks is the two-player zero-sum paradigm of adversarial training, in which predictors are trained against adversarially-chosen perturbations of data. Despite the promi ...

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Objectives: Bipolar disorder (BD) is a severe, chronic, affective disorder characterized by recurrent switching between mood states, psychomotor and cognitive symptoms, which can linger in euthymic states as residual symptoms. Hippocampal alterations may p ...

Genetic representation and evolvability of modular neural controllers

Chattez avec Graph Search

The neural correlates of topographical disorientation-a lesion analysis study

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

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

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

Fast adaptation to rule switching using neuronal surprise

Investigating neural resource allocation in the sensorimotor control of extra limbs

From event-based surprise to lifelong learning. A journey in the timescales of adaptation

Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Probabilistic methods for neural combinatorial optimization

A theory of memory consolidation and synaptic pruning in cortical circuits

3D trigonal FAPbI(3)-based multilevel resistive switching nonvolatile memory for artificial neural synapse

Bayesian reconstruction of memories stored in neural networks from their connectivity

Computational models of episodic-like memory in food-caching birds

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

Adversarial Training Should Be Cast As a Non-Zero-Sum Game

Robust Design of Herringbone Grooved Journal Bearings using Multi-Objective Optimization with Artificial Neural Networks

Dynamic functional hippocampal markers of residual depressive symptoms in euthymic bipolar disorder

Robust Design of Herringbone Grooved Journal Bearings using Multi-Objective Optimization with Artificial Neural Networks