NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data
NK Kasabov - Neural Networks, 2014 - Elsevier
The brain functions as a spatio-temporal information processing machine. Spatio-and
spectro-temporal brain data (STBD) are the most commonly collected data for measuring …
spectro-temporal brain data (STBD) are the most commonly collected data for measuring …
Dynamic evolving spiking neural networks for on-line spatio-and spectro-temporal pattern recognition
N Kasabov, K Dhoble, N Nuntalid, G Indiveri - Neural Networks, 2013 - Elsevier
On-line learning and recognition of spatio-and spectro-temporal data (SSTD) is a very
challenging task and an important one for the future development of autonomous machine …
challenging task and an important one for the future development of autonomous machine …
Kernel association for classification and prediction: A survey
Y Motai - IEEE transactions on neural networks and learning …, 2014 - ieeexplore.ieee.org
Kernel association (KA) in statistical pattern recognition used for classification and prediction
have recently emerged in a machine learning and signal processing context. This survey …
have recently emerged in a machine learning and signal processing context. This survey …
On the computational power of circuits of spiking neurons
Complex real-time computations on multi-modal time-varying input streams are carried out
by generic cortical microcircuits. Obstacles for the development of adequate theoretical …
by generic cortical microcircuits. Obstacles for the development of adequate theoretical …
Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes
The paper offers a new methodology for modelling, recognition and understanding of
electroencephalography (EEG) spatio-temporal data measuring complex cognitive brain …
electroencephalography (EEG) spatio-temporal data measuring complex cognitive brain …
Compact hardware liquid state machines on FPGA for real-time speech recognition
B Schrauwen, M D'Haene, D Verstraeten… - Neural networks, 2008 - Elsevier
Hardware implementations of Spiking Neural Networks are numerous because they are well
suited for implementation in digital and analog hardware, and outperform classic neural …
suited for implementation in digital and analog hardware, and outperform classic neural …
Dimensions of neural-symbolic integration-a structured survey
Research on integrated neural-symbolic systems has made significant progress in the
recent past. In particular the understanding of ways to deal with symbolic knowledge within …
recent past. In particular the understanding of ways to deal with symbolic knowledge within …
A spiking neural network methodology and system for learning and comparative analysis of EEG data from healthy versus addiction treated versus addiction not …
This paper introduces a method utilizing spiking neural networks (SNN) for learning,
classification, and comparative analysis of brain data. As a case study, the method was …
classification, and comparative analysis of brain data. As a case study, the method was …
[HTML][HTML] Echo state networks: novel reservoir selection and hyperparameter optimization model for time series forecasting
The use of computational intelligence models for multi-step time series forecasting tasks has
presented satisfactory results in such a way that they are considered models with an …
presented satisfactory results in such a way that they are considered models with an …
State-dependent computation using coupled recurrent networks
U Rutishauser, RJ Douglas - Neural computation, 2009 - direct.mit.edu
Although conditional branching between possible behavioral states is a hallmark of
intelligent behavior, very little is known about the neuronal mechanisms that support this …
intelligent behavior, very little is known about the neuronal mechanisms that support this …