Search Results for author:
Found 12 papers, 4 papers with code
Optimizing Genetically-Driven Synaptogenesis
In this paper we introduce SynaptoGen, a novel framework that aims to bridge the gap between genetic manipulations and neuronal network behavior by simulating synaptogenesis and guiding the development of neuronal networks capable of solving predetermined computational tasks.
Decoding visual brain representations from electroencephalography through Knowledge Distillation and latent diffusion models
Additionally, we incorporated an image reconstruction mechanism based on pre-trained latent diffusion models, which allowed us to generate an estimate of the images which had elicited EEG activity.
Through their eyes: multi-subject Brain Decoding with simple alignment techniques
We compared ridge regression, hyper alignment, and anatomical alignment techniques for fMRI data alignment.
Brain Captioning: Decoding human brain activity into images and text
Our brain captioning approach outperforms existing methods, while our image reconstruction pipeline generates plausible images with improved spatial relationships.
Beyond Multilayer Perceptrons: Investigating Complex Topologies in Neural Networks
Our study sheds light on the potential of complex topologies for enhancing the performance of ANNs and provides a foundation for future research exploring the interplay between multiple topological attributes and their impact on model performance.
Semantic Brain Decoding: from fMRI to conceptually similar image reconstruction of visual stimuli
Brain decoding is a field of computational neuroscience that uses measurable brain activity to infer mental states or internal representations of perceptual inputs.
BayesNetCNN: incorporating uncertainty in neural networks for image-based classification tasks
The willingness to trust predictions formulated by automatic algorithms is key in a vast number of domains.
VAESim: A probabilistic approach for self-supervised prototype discovery
Then, we reconstruct the sample based on a similarity measure between the sample embedding and the prototypical vectors of the clusters.
Contrastive learning for unsupervised medical image clustering and reconstruction
The lack of large labeled medical imaging datasets, along with significant inter-individual variability compared to clinically established disease classes, poses significant challenges in exploiting medical imaging information in a precision medicine paradigm, where in principle dense patient-specific data can be employed to formulate individual predictions and/or stratify patients into finer-grained groups which may follow more homogeneous trajectories and therefore empower clinical trials.
Application of the nnU-Net for automatic segmentation of lung lesion on CT images, and implication on radiomic models
The use of manual vs automatic segmentation in the performance of survival radiomic models was assessed, as well.
Physically constrained neural networks to solve the inverse problem for neuron models
Systems biology and systems neurophysiology in particular have recently emerged as powerful tools for a number of key applications in the biomedical sciences.
4Ward: a Relayering Strategy for Efficient Training of Arbitrarily Complex Directed Acyclic Graphs
Thanks to their ease of implementation, multilayer perceptrons (MLPs) have become ubiquitous in deep learning applications.
