KR and spikin NN

Greetings , all
has been quiet?
please note most interesting recent work
 There is a lot in here relevant to many aspects of KR to keep in mind and
discuss when we get the chance-
Deep Learning and Knowledge Representation in Brain-Inspired Spiking Neural
Networks for Brain-Computer Interfaces
Kumarasinghe, Kumara Vidanalage Dona Chithrangi Kaushalya
Abstract
Brain-Computer Interfaces aim at decoding neural commands from neurological
signals and translate them into machine commands for manipulating digital
devices. It provides a way of bypassing affected neural pathways in people
with movement impairments. A growing body of literature on non-invasive
Brain-Computer Interfaces for motor recovery and restoration highlights the
need for improving the machine learning methods that decode neural activity
from EEG signals. The low accuracy in decoding movements of the same limb,
less biological plausibility, lack of interpretability, high prediction
latency, low degree of freedom are some of the significant drawbacks in
existing machine learning models used in restorative Brain-Computer
Interfaces. This thesis proposes a Brain-Inspired Spiking Neural Network
(BI-SNN) model for incremental learning of spike sequences from stochastic
data streams as a promising step towards developing intelligent machines
for Brain-Computer Interfaces. The proposed BI-SNN is a generic SNN
architecture that can be applied for the predictive modelling of
spatio-temporal data streams. Here it was applied to construct an
interpretable neural decoder which can incrementally learn spike sequences
from Electroencephalography signals. The thesis suggests that the proposed
Spiking Neural Network approach results in a better neural decoder compared
to the traditional machine learning approaches used by restorative BCIs in
multiple aspects. The thesis proposes two spike-based learning algorithms
that extended the generic NeuCube SNN framework to address seven research
questions. A series of experiments were performed to address these research
questions and to benchmark the model performance with multiple machine
learning models. In the first study, the thesis demonstrates the
feasibility of proposed eSPANNet learning algorithm to learn complex spike
sequences from stochastic data streams. As an evolving model, the eSPANNet
does not require certain predefined parameters related to network
architecture, such as the number of neurons in the hidden layer, as it
evolves neurons if needed. In the second study, the thesis presents a
theoretical framework, algorithmic pipeline and associated software for
representing and extraction of deep knowledge from Spiking Neural Networks
for enhancing the interpretability of SNN. In the third study, the thesis
integrates the proposed learning algorithms with the generic NeuCube SNN
framework for constructing a novel Brain-Inspired Brain-Computer Interface.
The thesis revealed that the integration of eSPANNet with the NeuCube SNN
architecture could gain a higher accuracy than the standalone sensor-space
eSPANNet architecture. The study benchmarked the performance of the
proposed learning algorithms and showed a statistically significant
improvement in prediction accuracy than several machine learning methods.
The thesis has shown the feasibility of extracting neural information that
contributes to controlling a wide range of motor parameters such as muscle
activity and joint kinematics from Electroencephalography using the
proposed BI-SNN in healthy people. In conclusion, this approach has shown
the potential to construct an interpretable neural decoder which can
incrementally learn to predict complex movements in real-time from
Electroencephalography. This study is one of the first attempts to examine
the feasibility of finding neural correlates of muscle activity and
kinematics from Electroencephalography using a brain-inspired computational
paradigm. Read less <https://openrepository.aut.ac.nz/handle/10292/14347#>
deep learning and knowledge representation in brain-inspired ...
https://openrepository.aut.ac.nz › KumarasingheK
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by KVDCK Kumarasinghe · 2021 — *Brain*-*Computer Interfaces* aim at
decoding neural commands from neurological