Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification

Existing methods in neuromorphic olfaction mainly focus on implementing the data transformation based on the neurobiological architecture of the olfactory pathway. While the transformation is pivotal for the sparse spike-based representation of odor data, classification techniques based on the bio-computations of the higher brain areas, which process the spiking data for identification of odor, remain largely unexplored. This paper argues that brain-inspired spiking neural networks constitute a promising approach for the next generation of machine intelligence for odor data processing. Inspired by principles of brain information processing, here we propose the first spiking neural network method and associated deep machine learning system for classification of odor data. The paper demonstrates that the proposed approach has several advantages when compared to the current state-of-the-art methods. Based on results obtained using a benchmark dataset, the model achieved a high classification accuracy for a large number of odors and has the capacity for incremental learning on new data. The paper explores different spike encoding algorithms and finds that the most suitable for the task is the step-wise encoding function. Further directions in the brain-inspired study of odor machine classification include investigation of more biologically plausible algorithms for mapping, learning, and interpretation of odor data along with the realization of these algorithms on some highly parallel and low power consuming neuromorphic hardware devices for real-world applications

Incorporating Structural Plasticity Approaches in Spiking Neural Networks for EEG Modelling

Structural Plasticity (SP) in the brain is a process that allows neuronal structure changes, in response to learning. Spiking Neural Networks (SNN) are an emerging form of artificial neural networks that uses brain-inspired techniques to learn. However, the application of SP in SNNs, its impact on overall learning and network behaviour is rarely explored. In the present study, we use an SNN with a single hidden layer, to apply SP in classifying Electroencephalography signals of two publicly available datasets. We considered classification accuracy as the learning capability and applied metaheuristics to derive the optimised number of neurons for the hidden layer along with other hyperparameters of the network. The optimised structure was then compared with overgrown and undergrown structures to compare the accuracy, stability, and behaviour of the network properties. Networks with SP yielded ~94% and ~92% accuracies in classifying wrist positions and mental states(stressed vs relaxed) respectively. The same SNN developed for mental state classification produced ~77% and ~73% accuracies in classifying arousal and valence. Moreover, the networks with SP demonstrated superior performance stability during iterative random initiations. Interestingly, these networks had a smaller number of inactive neurons and a preference for lowered neuron firing thresholds. This research highlights the importance of systematically selecting the hidden layer neurons over arbitrary settings, particularly for SNNs using Spike Time Dependent Plasticity learning and provides potential findings that may lead to the development of SP learning algorithms for SNNs

Spatiotemporal EEG Dynamics of Prospective Memory in Ageing and Mild Cognitive Impairment

Prospective memory (PM, the memory of future intentions) is one of the first complaints of those that develop dementia-related disease. Little is known about the neurophysiology of PM in ageing and those with mild cognitive impairment (MCI). By using a novel artificial neural network to investigate the spatial and temporal features of PM related brain activity, new insights can be uncovered. Young adults (n = 30), healthy older adults (n = 39) and older adults with MCI (n = 27) completed a working memory and two PM (perceptual, conceptual) tasks. Time-locked electroencephalographic potentials (ERPs) from 128-electrodes were analysed using a brain-inspired spiking neural network (SNN) architecture. Local and global connectivity from the SNNs was then evaluated. SNNs outperformed other machine learning methods in classification of brain activity between younger, older and older adults with MCI. SNNs trained using PM related brain activity had better classification accuracy than working memory related brain activity. In general, younger adults exhibited greater local cluster connectivity compared to both older adult groups. Older adults with MCI demonstrated decreased global connectivity in response to working memory and perceptual PM tasks but increased connectivity in the conceptual PM models relative to younger and healthy older adults. SNNs can provide a useful method for differentiating between those with and without MCI. Using brain activity related to PM in combination with SNNs may provide a sensitive biomarker for detecting cognitive decline. Cognitively demanding tasks may increase the amount connectivity in older adults with MCI as a means of compensation

Pengaruh motivasi dan kesannya terhadap prestasi akademik: tinjauan terhadap pelajar Sarjana Muda Kejuruteraan Mekanikal Sesi 1999/2000 KUiTTHO

Laporan Projek Sarjana ini mempersembahkan hasil kajian yang bertajuk 'PENGARUH MOTIVASI DAN KESANNYA TERHADAP PRESTASI AKADEMIK'. Kajian ini bertujuan untuk mengenalpasti hubungan faktor-faktor yang signifikan dalam penentuan prestasi akademik pelajar (faktor dalaman, luaran dan persekitaran) dengan prestasi akademik yang diukur melalui Purata Markah Keseluruhan atau CGPA. Sampel kajian adalah seramai 60 orang pelajar Saijana Muda Kejuruteraan Mekanikal sesi 1999/2000 KUiTTHO. Kajian adalah berbentuk tinjauan yang menggunakan sejenis instrumen kajian dalam mendapatkan data iaitu borang soal selidik. Kesemua data dianalisis dan dikemukakan dalam bentuk analisis statistik secara deskriptif dan secara inferensi. Korelasi Pearson digunakan untuk melihat hubungan antara setiap pembolehubah. Terdapat tiga faktor utama yang dikaji iaitu faktor dalaman(min=3.6), faktor luaran(min=3.7) dan faktor persekitaran (min=2.9). Hasil kajian menunjukkan bahawa ketiga-tiga tiga faktor tersebut mempunyai hubungan yang positif dengan prestasi akademik. Faktor dalaman yang paling memberi hubungan yang signifikan dalam prestasi akademik dengan 0.795, faktor luaran 0.650 dan faktor persekitaran 0. 339. Di akhir kajian ini, pengkaji mencadangkan agar (i) Mengadakan banyak Kem Motivasi, (ii) Peningkatan cara pengajaran pensyarah, (iii) Penyediaan peralatan pembelajaran yang mencukupi, (iv) Sumber rujukan seperti buku dan majalah di Perpustakaan mesti mencukupi

Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications

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 processing modules. ECOS and eSTDM in particular can learn incrementally from data streams, can include 'on the fly' new input variables, new output class labels or regression outputs, can continuously adapt their structure and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction due to the ability of the SNN to spike early, before whole input vectors (they were trained on) are presented. A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using this is presented. The implementation of this framework in MATLAB, Java, and PyNN (Python) is presented. The latter facilitates the use of neuromorphic hardware platforms to run the eSTDM. Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data. Future directions are discussed, including extension of the NeuCube framework for building neurogenetic eSTDM and also new applications of eSTDM