Mario Senden

This paper introduces a model that performs exact Bayesian inference over a discrete state space using a local Hebbian rule, demonstrating its ability to account for perception and decision-making under noise.

The paper shows that gamma synchrony in the visual cortex depends on stimulus features and distance, aligning with weakly coupled oscillator theory and predicting human performance in texture segregation tasks.

This study analyzes 461,316 neuroscience articles to reveal the field's structural organization, dominant research domains, and trends in experimental focus and applied research.

PrediRep is a deep learning model designed to align closely with hierarchical predictive coding principles while maintaining computational performance in next-frame prediction tasks.

The paper develops a multi-scale spiking network model of human cortex to explore structure-dynamics relationships using multimodal data and validates it against electrophysiological and fMRI data.

AngoraPy enables training of complex recurrent convolutional neural networks for sensorimotor systems, facilitating goal-driven modeling in computational neuroscience.

This paper shows that the layered structure of the cortex explains increased alternation rates in bistable perception through mechanisms like interlaminar inhibition and input suppression.

This study investigates the neural correlates of high-level visual saliency using computational models and the Natural Scenes Dataset, identifying hV4 as a key region for saliency computations.

This paper reveals the structural connectivity architecture of the human brain, showing that subcortical regions are central hubs critical for global signal propagation.

The paper shows that CNNs with retinal sampling layers replicate primate visual cortex organizational principles, revealing spatial frequency-dependent radial bias.

The paper shows that convNets trained with retinal ganglion cell-based sampling replicate key organizational principles of the primate visual cortex without significantly affecting classification performance.

This paper introduces a fast pRF mapping method suitable for real-time applications, validated on simulated and empirical fMRI data.

This paper introduces MOU-EC, a whole-brain effective connectivity model for fMRI data to study brain cognition in health and disease, showing its application in extracting task-specific biomarkers and interpreting connectivity changes.

The paper shows that expert skills are vulnerable to interference from new skills, with interference magnitude depending on training amounts, and explains this through reweighting of lateral inhibition in a neural network model.

The paper compares functional connectivity measures for brain state classification across cognitive tasks and finds that different measures yield distinct and uncorrelated informative links.

This study uses fMRI and machine learning to show that visual imagery of letters preserves the topographic organization of perceived shapes in early visual cortex.

The paper defines 19 FOC features using analytical geometry and evaluates their contribution to perceived faceness in abstract stimuli through psychophysical experiments.

The paper shows that the cortical rich club increases outgoing effective connectivity during tasks, enabling fast communication with specific peripheral regions while maintaining constant incoming connectivity.

The paper proposes a real-time algorithm for estimating population receptive fields using gradient descent, showing good correspondence with ground-truth fields in simulated data.