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Excessively high, neural synchronisation has been associated with epileptic seizures, one of the most common brain diseases worldwide. A better understanding of neural synchronisation mechanisms can thus help control or even treat epilepsy.…
In this article we present the results of our research related to the study of correlations between specific visual stimulation and the elicited brain's electro-physiological response collected by EEG sensors from a group of participants.…
Neuroscientists have enjoyed much success in understanding brain functions by constructing brain connectivity networks using data collected under highly controlled experimental settings. However, these experimental settings bear little…
Detailed analysis of seizure semiology, the symptoms and signs which occur during a seizure, is critical for management of epilepsy patients. Inter-rater reliability using qualitative visual analysis is often poor for semiological features.…
Understanding cognitive flexibility and task-switching mechanisms in neural systems requires biologically plausible computational models. This tutorial presents a step-by-step approach to constructing a spiking neural network (SNN) that…
Epilepsy is a common disease of the nervous system. Timely prediction of seizures and intervention treatment can significantly reduce the accidental injury of patients and protect the life and health of patients. This paper presents a…
Purpose: To develop a robust deep learning framework for non-contrast-enhanced functional lung MRI, overcoming the limitations of spectral decomposition in the presence of physiological non-stationarity. Methods: We introduce VQ-Wave…
Naturalistic fMRI has been suggested to be a powerful alternative for investigations of human brain function. Stimulus-induced activation has been playing an essential role in fMRI-based brain function analyses. Due to the complexity of the…
Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the…
Introduction: Approximately 23 million or 30% of epilepsy patients worldwide suffer from drug-resistant epilepsy (DRE). The unpredictability of seizure occurrences, which causes safety issues as well as social concerns, restrict the…
Deep Brain Stimulation (DBS) is a well-established neurosurgical treatment aiming at symptom alleviation in a range of neurological and psychiatric diseases. Computational models of DBS are widely used to investigate the effects of…
This paper introduces an innovative framework designed for progressive (granular in time to onset) prediction of seizures through the utilization of a Deep Learning (DL) methodology based on non-invasive multi-modal sensor networks.…
Event cameras, with their high dynamic range and temporal resolution, are ideally suited for object detection, especially under scenarios with motion blur and challenging lighting conditions. However, while most existing approaches…
We present a quantitative study of phase entrainment by periodic visual stimuli in a biologically inspired neural network. The objective is to understand the neuronal population dynamics that underlie phase entrainment of brain oscillations…
Vanilla spiking neurons in Spiking Neural Networks (SNNs) use charge-fire-reset neuronal dynamics, which can only be simulated serially and can hardly learn long-time dependencies. We find that when removing reset, the neuronal dynamics can…
In many realistic networks, the edges representing the interactions between the nodes are time-varying. There is growing evidence that the complex network that models the dynamics of the human brain has time-varying interconnections, i.e.,…
Focal epilepsy is a devastating neurological disorder that affects an overwhelming number of patients worldwide, many of whom prove resistant to medication. The efficacy of current innovative technologies for the treatment of these patients…
Diagnosing epilepsy requires accurate seizure detection and classification, but traditional manual EEG signal analysis is resource-intensive. Meanwhile, automated algorithms often overlook EEG's geometric and semantic properties critical…
Spiking Neural Networks (SNNs), particularly Spiking Transformers, offer energy-efficient processing of event-based sensor data for healthcare applications. Yet current architectures are rigid: they are trained and deployed as static…
Sequential latent variable models (SLVMs) are essential tools in statistics and machine learning, with applications ranging from healthcare to neuroscience. As their flexibility increases, analytic inference and model learning can become…