Hardware-Amenable Structural Learning for Spike-based Pattern Classification using a Simple Model of Active Dendrites

This paper presents a spike-based model which employs neurons with functionally distinct dendritic compartments for classifying high dimensional binary patterns. The synaptic inputs arriving on each dendritic subunit are nonlinearly processed before being linearly integrated at the soma, giving the neuron a capacity to perform a large number of input-output mappings. The model utilizes sparse synaptic connectivity; where each synapse takes a binary value. The optimal connection pattern of a neuron is learned by using a simple hardware-friendly, margin enhancing learning algorithm inspired by the mechanism of structural plasticity in biological neurons. The learning algorithm groups correlated synaptic inputs on the same dendritic branch. Since the learning results in modified connection patterns, it can be incorporated into current event-based neuromorphic systems with little overhead. This work also presents a branch-specific spike-based version of this structural plasticity rule. The proposed model is evaluated on benchmark binary classification problems and its performance is compared against that achieved using Support Vector Machine (SVM) and Extreme Learning Machine (ELM) techniques. Our proposed method attains comparable performance while utilizing 10 to 50% less computational resources than the other reported techniques.Comment: Accepted for publication in Neural Computatio

A Sequential Optimization Sampling Method for Metamodels with Radial Basis Functions

Metamodels have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is strongly affected by the sampling methods. In this paper, a new sequential optimization sampling method is proposed. Based on the new sampling method, metamodels can be constructed repeatedly through the addition of sampling points, namely, extrema points of metamodels and minimum points of density function. Afterwards, the more accurate metamodels would be constructed by the procedure above. The validity and effectiveness of proposed sampling method are examined by studying typical numerical examples

Uma abordagem para parametrização de Redes Neurais de Função de Base Radial baseada na combinação de procedimentos não supervisionados e de uma nova proposição de escalonamento de parâmetros.

Neste trabalho será apresentada uma abordagem para parametrização de redes RBF (Radial Basis Function) baseada na combinação de procedimentos não supervisionados e uma nova proposição de escalonamento de parâmetros. A metodologia consiste em combinar procedimentos referenciados na literatura com o objetivo de obter modelos de redes RBF com melhores exatidões e algoritmos computacionais mais compactos. Alguns exemplos serão utilizados para ilustrar o emprego da abordagem proposta e também servirão para realizar comparações de resultados com os principais procedimentos referenciados em textos da área. As redes neurais com funções de base radial (RBF) são modelos não lineares que podem realizar um mapeamento (interpolação) eficiente de dados de entrada e saída de diversos tipos de sistemas, resultando em boa capacidade de generalização aliada a processamentos de informações de forma compacta, possibilitando na representação eficiente de sistemas dinâmicos complexos e de séries temporais, por exemplo. Os bons resultados na capacidade de interpolação de uma RBF dependem de alguns parâmetros que devem ser adequadamente ajustados. Algumas abordagens foram desenvolvidas nesse contexto. O procedimento proposto neste trabalho mostrou-se ser uma alternativa promissora, com aplicação direta e que apresenta uma exatidão adequada para várias aplicações práticas. Exemplos como aproximações de funções, modelagem de sistemas dinâmicos não lineares, previsão de série temporal e classificação de padrões serão discutidos com a finalidade de exemplificar os procedimentos propostos, além de servir de comparações com os resultados obtidos por outras técnicas utilizadas em redes RBF

Scaffolding type-2 classifier for incremental learning under concept drifts

© 2016 Elsevier B.V. The proposal of a meta-cognitive learning machine that embodies the three pillars of human learning: what-to-learn, how-to-learn, and when-to-learn, has enriched the landscape of evolving systems. The majority of meta-cognitive learning machines in the literature have not, however, characterized a plug-and-play working principle, and thus require supplementary learning modules to be pre-or post-processed. In addition, they still rely on the type-1 neuron, which has problems of uncertainty. This paper proposes the Scaffolding Type-2 Classifier (ST2Class). ST2Class is a novel meta-cognitive scaffolding classifier that operates completely in local and incremental learning modes. It is built upon a multivariable interval type-2 Fuzzy Neural Network (FNN) which is driven by multivariate Gaussian function in the hidden layer and the non-linear wavelet polynomial in the output layer. The what-to-learn module is created by virtue of a novel active learning scenario termed the uncertainty measure; the how-to-learn module is based on the renowned Schema and Scaffolding theories; and the when-to-learn module uses a standard sample reserved strategy. The viability of ST2Class is numerically benchmarked against state-of-the-art classifiers in 12 data streams, and is statistically validated by thorough statistical tests, in which it achieves high accuracy while retaining low complexity