Open AccessDissertation
Neuromorphic computational models for machine learning and pattern recognition from multi-modal time-series data
Neelava Sengupta
- 01 Jan 2018
10
TL;DR: This thesis has focused on developing neurobiologically inspired computational models known as spiking neural networks to tackle multi-modal time-series data and revisits and searches for inspiration from biological intelligence.
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Abstract: The fields of neuroscience and artificial intelligence have a long and entwined history. In recent times, however, communication and collaboration between the two fields has become a rarity as they have evolved. Written in the era when artificial intelligence and deep learning is revolutionising the world, this thesis revisits and searches for inspiration from biological intelligence. The efficiency and accuracy with which the human brain processes incoming stimulus (data) in millisecond resolution using remarkably low power is unprecedented. Motivated by this very capability in the generic sense, this thesis has focused on developing neurobiologically inspired computational models known as spiking neural networks to tackle multi-modal time-series data. In a more definitive formalisation, this work has aimed to answer three research questions: 1. How to optimally design an implementation of neuromorphic architecture which is capable of processing large volumes of spatio-temporal data? To answer this research question, the unsupervised SNNc algorithm (as part of NeuCube architecture) were studied and numerous designs of the SNNc graph was analysed in regards to storage and execution time complexities. Further, the study was extended to include an analysis of the software design principles for achieving modularity and heterogeneity. The design principles formalised here are implemented in the NeuCube software publicly available from www.kedri.aut.ac.nz/neucube. The design principles proposed in the study are also utilised in other parts of this work. 2. How to perform neural encoding on real-world data to represent information as spike-timings? This topic has been analysed from the viewpoint of data compression and information theory. To answer this research ques-
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