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Short Course No. 1Brain-Inspired Spiking Neural Network Systems for Explainable and Life-Long Learning

The Ulster University, ISRC - CN3 Autumn School, North Ireland, Oct 2022       Prof N Kasabov ISRC UU

Course organizer: Ulster University

Lecturers: Prof Nikola Kasabov

Reference: N.Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence Springer, 2019.

1- Why brain-inspired computation?

  • How is knowledge represented in the brain?

  • The challenge is to build AI systems that can learn incrementally and possibly in a life-long learning mode and can be interpreted as knowledge discovery at any phase of their learning.

2- BI-SNN architectures. NeuCube.

  • What is a BI-SNN architecture?

  • The NeuCube Architecture

  • Spike encoding methods

  • Spiking neuron models for BI-SNN

  • Methods for unsupervised learning in SNN: Spike-Time Dependent Plasticity (STDP)

  • Methods for supervised learning in SNN: Rank order (RO) learning rule

  • Dynamic Evolving SNN (deSNN)

  • Deep learning in NeuCube

  • Incremental and transfer learning and knowledge evolution in BI-SNN

  • Life-long Learning (LLL) in the brain and in NeuCube

  • Time-Space Rule (TSR) representation in BI-SNN – “opening the cube”

  • Capturing time-space knowledge as information exchange between clusters

  • Capturing knowledge representation in a BI-SNN through supervised learning with deSNN

  • Extracting Time-Space Rules (TSR) from a trained NeuCube using EEG data for the GAL task

  • NeuCube development environment for SNN system design for TSD

  • Implementation of SNN models: From von Neumann principles and Atanassov’s ABC Machine to Neuromorphic Hardware

  • Quantum (or quantum-inspired) computation

  • Quantum-inspired optimization of eSNN


3- Application specific methods and systems

  • Deep learning and deep knowledge representation of neuroimaging spatio-temporal brain data

  • Understanding human decision-making

  • Understanding brain re-wiring due to mindfulness training using EEG

  • Brain Machine Interfaces using Brain-Inspired SNN

  • NeuCube for BCI with neurofeedback for prosthetic hands

  • BI-SNN for neurorehabilitation

  • Learning and understanding brain-VR/AR interaction in time-space

  • Deep learning and deep knowledge representation of fMRI data

  • Personalized Modelling using both fMRI and DTI data

  • Deep learning and modelling of audio and visual and multimodal audio-visual data in BI-SNN

  • Deep learning of visual information

  • BI-SNN for fast object recognition from video streaming data

  • Deep learning and knowledge representation of moving objects using DVS and retinotopic mapping in NeuCube

  • Personalized predictive modelling of individual risk of stroke

  • Using an ensemble of eSNN for Predicting Hourly Air Pollution in London Area from sensory TSD

  • Using BI-SNN as universal machines for explainable and life-long learning systems for various spatio/spectro -temporal data


4- Discussions and Future Directions

  • Advantages, Problems, and limitations of BI-SNN

  • BI-AI through BI-SNN architectures

  • Computational Neuro-Genetic Modelling (CNGM)

  • Knowledge-based human-machine interaction and symbiosis based on deep learning, knowledge representation and knowledge transfer with BI-SNN architectures

  • Understanding how humans synchronize and transfer knowledge between each other through hyper-scanning and using BI-SNN to model it.

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