Neuromorphic Learning towards Nano Second Precision

Temporal coding is one approach to representing information in spiking neural networks. An example of its application is the location of sounds by barn owls that requires especially precise temporal coding. Dependent upon the azimuthal angle, the arrival times of sound signals are shifted between both ears. In order to deter- mine these interaural time differences, the phase difference of the signals is measured. We implemented this biologically inspired network on a neuromorphic hardware system and demonstrate spike-timing dependent plasticity on an analog, highly accelerated hardware substrate. Our neuromorphic implementation enables the resolution of time differences of less than 50 ns. On-chip Hebbian learning mechanisms select inputs from a pool of neurons which code for the same sound frequency. Hence, noise caused by different synaptic delays across these inputs is reduced. Furthermore, learning compensates for variations on neuronal and synaptic parameters caused by device mismatch intrinsic to the neuromorphic substrate.Comment: 7 pages, 7 figures, presented at IJCNN 2013 in Dallas, TX, USA. IJCNN 2013. Corrected version with updated STDP curves IJCNN 201

Mining high utility sequential patterns

University of Technology Sydney. Faculty of Engineering and Information Technology.Sequential pattern mining refers to the identification of frequent subsequences in sequence databases as patterns. It provides an effective way to analyze the sequential data. The selection of interesting sequences is generally based on the frequency/support framework: sequences of high frequency are treated as significant. In the last two decades, researchers have proposed many techniques and algorithms for extracting the frequent sequential patterns, in which the downward closure property (also known as Apriori property) plays a fundamental role. At the same time, the relative importance of each item has been introduced in frequent pattern mining, and “high utility itemset mining” has been proposed. Instead of selecting high frequency patterns, the utility-based methods extract itemsets with high utilities, and many algorithms and strategies have been proposed. These methods can only process the itemsets in the utility framework. However, all the above methods suffer from the following common issues and problems to varying extents: 1) Sometimes, most of frequent patterns may not be informative to business decision-making, since they do not show the business value and impact. 2) Even if there is an algorithm that considers the business impact (namely utility), it can only obtain high utility sequences based on a given minimum utility threshold, thus it is very difficult for users to specify an appropriate minimum utility and to directly obtain the most valuable patterns. 3) The algorithm in the utility framework may generate a large number of patterns, many of which maybe redundant. Although high utility sequential pattern mining is essential, discovering the patterns is challenging for the following reasons: 1) The downward closure property does not hold in utility-based sequence mining. This means that most of the existing algorithms cannot be directly transferred, e.g. from frequent sequential pattern mining to high utility sequential pattern mining. Furthermore, compared to high utility itemset mining, utility-based sequence analysis faces the critical combinational explosion and computational complexity caused by sequencing between sequential elements (itemsets). 2) Since the minimum utility is not given in advance, the algorithm essentially starts searching from 0 minimum support. This not only incurs very high computational costs, but also the challenge of how to raise the minimum threshold without missing any top-k high utility sequences. 3) Due to the fundamental difference, incorporating the traditional closure concept into high utility sequential pattern mining makes the outcome patterns irreversibly lossy and no longer recoverable, which will be reasoned in the following chapters. Therefore, it is exceedingly challenging to address the above issues by designing a novel representation for high utility sequential patterns. To address these research limitations and challenges, this thesis proposes a high utility sequential pattern mining framework, and proposes both a threshold-based and top-k-based mining algorithm. Furthermore, a compact and lossless representation of utility-based sequence is presented, and an efficient algorithm is provided to mine such kind of patterns. Chapter 2 thoroughly reviews the related works in the frequent sequential pattern mining and high utility itemset/sequence mining. Chapter 3 incorporates utility into sequential pattern mining, and a generic framework for high utility sequence mining is defined. Two efficient algorithms, namely USpan and USpan+, are presented to mine for high utility sequential patterns. In USpan and USpan+, we introduce the lexicographic quantitative sequence tree to extract the complete set of high utility sequences and design concatenation mechanisms for calculating the utility of a node and its children with three effective pruning strategies. Chapter 4 proposes a novel framework called top-k high utility sequential pattern mining to tackle this critical problem. Accordingly, an efficient algorithm, Top-k high Utility Sequence (TUS for short) mining, is designed to identify top-k high utility sequential patterns without minimum utility. In addition, three effective features are introduced to handle the efficiency problem, including two strategies for raising the threshold and one pruning for filtering unpromising items. Chapter 5 proposes a novel concise framework to discover US-closed (Utility Sequence closed) high utility sequential patterns, with theoretical proof that it expresses the lossless representation of high-utility patterns. An efficient algorithm named CloUSpan is introduced to extract the US-closed patterns. Two effective strategies are used to enhance the performance of CloUSpan. All of the algorithms are examined in both synthetic and real datasets. The performances, including the running time and memory consumption, are compared. Furthermore, the utility-based sequential patterns are compared with the patterns in the frequency/support framework. The results show that high utility sequential patterns provide insightful knowledge for users

An efficient automated parameter tuning framework for spiking neural networks

As the desire for biologically realistic spiking neural networks (SNNs) increases, tuning the enormous number of open parameters in these models becomes a difficult challenge. SNNs have been used to successfully model complex neural circuits that explore various neural phenomena such as neural plasticity, vision systems, auditory systems, neural oscillations, and many other important topics of neural function. Additionally, SNNs are particularly well-adapted to run on neuromorphic hardware that will support biological brain-scale architectures. Although the inclusion of realistic plasticity equations, neural dynamics, and recurrent topologies has increased the descriptive power of SNNs, it has also made the task of tuning these biologically realistic SNNs difficult. To meet this challenge, we present an automated parameter tuning framework capable of tuning SNNs quickly and efficiently using evolutionary algorithms (EA) and inexpensive, readily accessible graphics processing units (GPUs). A sample SNN with 4104 neurons was tuned to give V1 simple cell-like tuning curve responses and produce self-organizing receptive fields (SORFs) when presented with a random sequence of counterphase sinusoidal grating stimuli. A performance analysis comparing the GPU-accelerated implementation to a single-threaded central processing unit (CPU) implementation was carried out and showed a speedup of 65× of the GPU implementation over the CPU implementation, or 0.35 h per generation for GPU vs. 23.5 h per generation for CPU. Additionally, the parameter value solutions found in the tuned SNN were studied and found to be stable and repeatable. The automated parameter tuning framework presented here will be of use to both the computational neuroscience and neuromorphic engineering communities, making the process of constructing and tuning large-scale SNNs much quicker and easier

Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

[Abstract] Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028

Implementasi Algoritma Convolutional Neural Network Pada Kendaraan Tanpa Awak Skala Kecil

Autonomous Vehicle is a vehicle capable of navigating the car independently without requiring input from the driver. This research aims to design and manufacture a prototype of an unmanned vehicle that can maneuver across a simple artificial road. This study also aims to analyze the performance of the NVIDIA Jetson Nano in processing deep learning models and driving actuators according to the predictions given by the model. The research stages include designing a prototype, creating an artificial path, taking image data, conducting training, and then implementing the training model on the car prototype. After testing the prototype, the training model made the correct steering angle prediction using epoch 50 with RMSE train and validation, 0.1792 and 0.1896, respectively. NVIDIA Jetson Nano also performs well in computing steering angle predictions with live input from the camera

Enhancing competitive island cooperative neuro - evolution through backpropagation for pattern classification

Cooperative coevolution is a promising method for training neural networks which is also known as cooperative neuro-evolution. Cooperative neuro-evolution has been used for pattern classification, time series prediction and global optimisation problems. In the past, competitive island based cooperative coevolution has been proposed that employed different instances of problem decomposition methods for competition. Neuro-evolution has limitations in terms of training time although they are known as global search methods. Backpropagation algorithm employs gradient descent which helps in faster convergence which is needed for neuro-evolution. Backpropagation suffers from premature convergence and its combination with neuro-evolution can help eliminate the weakness of both the approaches. In this paper, we propose a competitive island cooperative neuro-evolutionary method that takes advantage of the strengths of gradient descent and neuro-evolution. We use feedforward neural networks on benchmark pattern classification problems to evaluate the performance of the proposed algorithm. The results show improved performance when compared to related methods