Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications [PDF]
Neural Networks, 2016 The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major ...
Nikola Kasabov +2 more
exaly +6 more sources
Mapping Temporal Variables Into the NeuCube for Improved Pattern Recognition, Predictive Modeling, and Understanding of Stream Data [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2017 This paper proposes a new method for an optimized mapping of temporal variables, describing a temporal stream data, into the recently proposed NeuCube spiking neural network architecture. This optimized mapping extends the use of the NeuCube, which was initially designed for spatiotemporal brain data, to work on arbitrary stream data and to achieve a ...
Nikola Kasabov
exaly +7 more sources
Classification and segmentation of fMRI Spatio-Temporal Brain Data with a NeuCube evolving Spiking Neural Network model [PDF]
2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), 2014 The proposed feasibility analysis introduces a new methodology for modelling and understanding functional Magnetic Resonance Image (fMRI) data recorded during human cognitive activity. This constitutes a type of Spatio-Temporal Brain Data (STBD) measured according to neurons spatial location inside the brain and their signals oscillating over the ...
Elisa Capecci, Nikola Kasabov
exaly +3 more sources
Personalised modelling on integrated clinical and EEG Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network system [PDF]
2016 International Joint Conference on Neural Networks (IJCNN), 2016 This paper introduces a novel personalised modelling framework and system for analysing Spatio-Temporal Brain Data (STBD) along with person clinical static data. For every individual, based on selected subset of similar to this individual clinical data, a subset of STBD is used for training a personalised Spiking Neural Network (PSNN) model using the ...
Nikola Kasabov
exaly +4 more sources
Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware [PDF]
Journal of Neural Engineering, 2019 Abstract Objective . The objective of this work is to use the capability of spiking neural networks to capture the spatio-temporal information encoded in time-series signals and decode them without the use of hand-crafted features and vector-based learning and the realization of the spiking ...
Jan Behrenbeck +9 more
openaire +4 more sources
NeuCube EvoSpike Architecture for Spatio-temporal Modelling and Pattern Recognition of Brain Signals [PDF]
, 2012 The brain functions as a spatio-temporal information processing machine and deals extremely well with spatio-temporal data. Spatio- and spectro-temporal data (SSTD) are the most common data collected to measure brain signals and brain activities, along with the recently obtained gene and protein data. Yet, there are no computational models to integrate
Kasabov, N, Nikola Kasabov
openaire +3 more sources
Analyzing darknet traffic through machine learning and neucube spiking neural networks
Intelligent and Converged NetworksThe rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication channels. Effective detection of clandestine darknet traffic is therefore critical yet immensely challenging.
Iman Akour +4 more
doaj +2 more sources
Dynamic 3D Clustering of Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network Architecture on a Case Study of fMRI Data [PDF]
, 2015 The paper presents a novel clustering method for dynamic Spatio-Temporal Brain Data (STBD) on the case study of functional Magnetic Resonance Image (fMRI). The method is based on NeuCube spiking neural network (SNN) architecture, where the spatio-temporal relationships between STBD streams are learned and simultaneously the clusters are created.
Maryam Gholami Doborjeh +1 more
openaire +2 more sources
Machine learning with a snapshot of data: Spiking neural network 'predicts' reinforcement histories of pigeons' choice behavior. [PDF]
J Exp Anal Behav, 2022 An accumulated body of choice research has demonstrated that choice behavior can be understood within the context of its history of reinforcement by measuring response patterns. Traditionally, work on predicting choice behaviors has been based on the relationship between the history of reinforcement—the reinforcer arrangement used in training ...
Plessas A +4 more
europepmc +2 more sources
Security and Communication Networks, Volume 2022, Issue 1, 2022., 2022 The virtually coupled train sets (VCTS) have been proposed to improve operational capability and passenger satisfaction and ensure punctuality, thus alleviating the rapidly worsening traffic pressure. Recently, due to the lack of reliable wireless communications and accurate perceptual information, VCTS based on industrial internet of things (IIoT) are
Shuomei Ma +4 more
wiley +1 more source