scholarly journals Dynamic 3D Clustering of Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network Architecture on a Case Study of fMRI Data

2015 ◽  
pp. 191-198 ◽  
Author(s):  
Maryam Gholami Doborjeh ◽  
Nikola Kasabov
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Fmri Data ◽  
Spiking Neural Network ◽  
Neural Network Architecture ◽  
Spatio Temporal ◽  
Brain Data

scholarly journals A Deep Graph Neural Network Architecture for Modelling Spatio-temporal Dynamics in resting-state functional MRI Data

2020 ◽  
Author(s):  
Tiago Azevedo ◽  
Alexander Campbell ◽  
Rafael Romero-Garcia ◽  
Luca Passamonti ◽  
Richard A.I. Bethlehem ◽  
...  
Keyword(s):  
Neural Network ◽  
Resting State ◽  
Network Architecture ◽  
Temporal Dynamics ◽  
Feature Learning ◽  
Blood Oxygen Level Dependent ◽  
Fmri Data ◽  
Neural Network Architecture ◽  
Pairwise Correlation ◽  
Spatio Temporal

AbstractResting-state functional magnetic resonance imaging (rs-fMRI) has been successfully employed to understand the organisation of the human brain. For rs-fMRI analysis, the brain is typically parcellated into regions of interest (ROIs) and modelled as a graph where each ROI is a node and pairwise correlation between ROI blood-oxygen-level-dependent (BOLD) time series are edges. Recently, graph neural networks (GNNs) have seen a surge in popularity due to their successes in modelling unstructured relational data. The latest developments with GNNs, however, have not yet been fully exploited for the analysis of rs-fMRI data, particularly with regards to its spatio-temporal dynamics. Herein we present a novel deep neural network architecture, combining both GNNs and temporal convolutional networks (TCNs), which is able to learn from the spatial and temporal components of rs-fMRI data in an end-to-end fashion. In particular, this corresponds to intra-feature learning (i.e., learning temporal dynamics with TCNs) as well as inter-feature learning (i.e., leveraging spatial interactions between ROIs with GNNs). We evaluate our model with an ablation study using 35,159 samples from the UK Biobank rs-fMRI database. We also demonstrate explainability features of our architecture which map to realistic neurobiological insights. We hope our model could lay the groundwork for future deep learning architectures focused on leveraging the inherently and inextricably spatio-temporal nature of rs-fMRI data.

A Spiking Neural Network Architecture for Object Tracking

2019 ◽  
pp. 118-132
Author(s):  
Yihao Luo ◽  
Quanzheng Yi ◽  
Tianjiang Wang ◽  
Ling Lin ◽  
Yan Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Tracking ◽  
Network Architecture ◽  
Spiking Neural Network ◽  
Neural Network Architecture

scholarly journals Modelling Peri-Perceptual Brain Processes in a Deep Learning Spiking Neural Network Architecture

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Zohreh Gholami Doborjeh ◽  
Nikola Kasabov ◽  
Maryam Gholami Doborjeh ◽  
Alexander Sumich
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Architecture ◽  
Spiking Neural Network ◽  
Neural Network Architecture
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