Deep learning machine that employs Spiking Neural Network (SNN) is currently one of the main techniques in computational intelligence to discover knowledge from various fields. It has been applied in many application areas include health, engineering, finances, environment, and others. This paper addresses a classification problem based on a functional Magnetic Resonance Image (fMRI) brain data experiment involving a subject who reads a sentence or looks at a picture. In the experiment, Signal to Noise Ratio (SNR) is used to select the most relevant features (voxels) before they were propagated in an SNN-based learning architecture. The spatiotemporal relationships between Spatio Temporal Brain Data (STBD) are learned and classified ac...
Arguably the most significant challenge in modern machine learning regards how we address the comple...
This thesis is a feasibility study of using a Spiking Neural Network (SNN) architecture named NeuCub...
The use of Electroencephalography (EEG) in Brain Computer Interface (BCI) domain presents a challeng...
The proposed feasibility analysis introduces a new methodology for modelling and understanding funct...
The proposed feasibility analysis introduces a new methodology for modelling and understanding funct...
This thesis proposes methods employing an evolving Spiking Neural Network (SNN) architecture for the...
3D spatial temporal functional magnetic resonance imaging (fMRI) for classification has gained wide ...
The application of data mining techniques, particularly classification of spatio-temporal 3D functio...
The paper presents a novel clustering method for dynamic Spatio-Temporal Brain Data (STBD) on the ca...
Spatio- and spectro-temporal data (SSTD) are the most common types of data collected in many domain ...
Spatio- and spectro-temporal data (SSTD) are the most common types of data collected in many domain ...
Spatio- and spectro-temporal data are the most common data in many domain areas, including bioinform...
The paper proposes a new method for deep learning and knowledge discovery in a brain-inspired Spikin...
Brain as main server for entire human body is a complex composition. It is a challenging task to rea...
Artificial neural networks have been used as a powerful processing tool in various areas such as pat...
Arguably the most significant challenge in modern machine learning regards how we address the comple...
This thesis is a feasibility study of using a Spiking Neural Network (SNN) architecture named NeuCub...
The use of Electroencephalography (EEG) in Brain Computer Interface (BCI) domain presents a challeng...
The proposed feasibility analysis introduces a new methodology for modelling and understanding funct...
The proposed feasibility analysis introduces a new methodology for modelling and understanding funct...
This thesis proposes methods employing an evolving Spiking Neural Network (SNN) architecture for the...
3D spatial temporal functional magnetic resonance imaging (fMRI) for classification has gained wide ...
The application of data mining techniques, particularly classification of spatio-temporal 3D functio...
The paper presents a novel clustering method for dynamic Spatio-Temporal Brain Data (STBD) on the ca...
Spatio- and spectro-temporal data (SSTD) are the most common types of data collected in many domain ...
Spatio- and spectro-temporal data (SSTD) are the most common types of data collected in many domain ...
Spatio- and spectro-temporal data are the most common data in many domain areas, including bioinform...
The paper proposes a new method for deep learning and knowledge discovery in a brain-inspired Spikin...
Brain as main server for entire human body is a complex composition. It is a challenging task to rea...
Artificial neural networks have been used as a powerful processing tool in various areas such as pat...
Arguably the most significant challenge in modern machine learning regards how we address the comple...
This thesis is a feasibility study of using a Spiking Neural Network (SNN) architecture named NeuCub...
The use of Electroencephalography (EEG) in Brain Computer Interface (BCI) domain presents a challeng...