Stefan Schliebs

Because of increasing transport and trade there is a growing threat of marine invasive species being introduced into regions where they do not presently occur. So that the impacts of such species can be mitigated, it is important to predict how individuals, particularly passive dispersers are transported and dispersed in the ocean as well as in coastal regions so that new incursions of potential invasive species are rapidly detected and origins identified. Such predictions also support strategic monitoring, containment and/or eradication programs. To determine factors influencing a passive disperser, around coastal New Zealand, data from the genus Physalia (Cnidaria: Siphonophora) were used. Oceanographic data on wave height and wind direction and records of occurrences of Physalia on swimming beaches throughout the summer season were used to create models using artificial neural networks (ANNs) and Naϊve Bayesian Classifier (NBC). First, however, redundant and irrelevant data were removed using feature selection of a subset of variables. Two methods for feature selection were compared, one based on the multilayer perceptron and another based on an evolutionary algorithm. The models indicated that New Zealand appears to have two independent systems driven by currents and oceanographic variables that are responsible for the redistribution of Physalia from north of New Zealand and from the Tasman Sea to their subsequent presence in coastal waters. One system is centred in the east coast of northern New Zealand and the other involves a dynamic system that encompasses four other regions on both coasts of the country. Interestingly, the models confirm, molecular data obtained from Physalia in a previous study that identified a similar distribution of systems around New Zealand coastal waters. Additionally, this study demonstrates that the modelling methods used could generate valid hypotheses from noisy and complicated data in a system about which there is little previous knowledge.► Factors influencing the passive dispersal of Physalia (Cnidaria) were investigated. ► Artificial neural networks and evolutionary algorithms were compared. ► Two independent systems determine Physalia occurrence in New Zealand.

In this paper we propose SPAN, a LIF spiking neuron that is capable of learning input-output spike pattern association using a novel learning algorithm. The main idea of SPAN is transforming the spike trains into analog signals where computing the error can be done easily. As demonstrated in an experimental analysis, the proposed method is both simple and efficient achieving reliable training results even in the context of noise.

This study employs networks of stochastic spiking neurons as reservoirs for liquid state machines (LSM). We experimentally investigate the separation property of these reservoirs and show their ability to generalize classes of input signals. Similar to traditional LSM, probabilistic LSM (pLSM) have the separation property enabling them to distinguish between different classes of input stimuli. Furthermore, our results indicate some potential advantages of non-deterministic LSM by improving upon the separation ability of the liquid. Three non-deterministic neural models are considered and for each of them several parameter configurations are explored. We demonstrate some of the characteristics of pLSM and compare them to their deterministic counterparts. pLSM offer more flexibility due to the probabilistic parameters resulting in a better performance for some values of these parameters.

We present a method that is based on Particle Swarm Optimization (PSO) for training a Spiking Neural Network (SNN) with dynamic synapses to generate precise time spike sequences. The similarity between the desired spike sequence and the actual output sequence is measured by a simple leaky integrate and fire spiking neuron. This measurement is used as a fitness function for PSO algorithm to tune the dynamic synapses until a desired spike output sequence is obtained when certain input spike sequence is presented. Simulations are made to illustrate the performance of the proposed method.

It is well known that the clusters produced by a clustering algorithm depend on the chosen initial centers. In this paper we present a measure for the degree to which a given clustering algorithm depends on the choice of initial centers, for a given data set. This measure is calculated for four well-known offline clustering algorithms (k-means Forgy, k-means Hartigan, k-means Lloyd and fuzzy c-means), for five benchmark data sets. The measure is also calculated for ECM, an online algorithm that does not require the number of initial centers as input, but for which the resulting clusters can depend on the order that the input arrives. Our main finding is that this initialization dependence measure can also be used to determine the optimal number of clusters.

Abstract: This thesis proposes a novel feature selection and classification method employing evolving spiking neural networks (eSNN) and evolutionary algorithms (EA). The method is named the Quantum-inspired Spiking Neural Network (QiSNN) framework. QiSNN ...

The paper introduces a framework and implementation of an integrated connectionist system, where the features and the parameters of an evolving spiking neural network are optimised together using a quantum representation of the features and a quantum inspired evolutionary algorithm for optimisation. The proposed model is applied on ecological data modeling problem demonstrating a significantly better classification accuracy than traditional neural network approaches and a more appropriate feature subset selected from a larger initial number of features. Results are compared to a naive Bayesian classifier.

This study investigates the characteristics of the Quantum-inspired Spiking Neural Network (QiSNN) feature selection and classification framework. The self-adapting nature of QiSNN due to the simultaneous optimization of network parameters and feature subsets represents a highly desirable characteristic in the context of machine learning and knowledge discovery. In this paper, the evolution of the parameters and feature subsets is studied in detail. The goal of this analysis is a comprehensive understanding of all parameters involved in QiSNN and some practical guidelines for using the method in future research and applications. We also highlight the role of the employed neural encoding technique along with its impact on the classification abilities of QiSNN.

An extension of an evolving spiking neural network (eSNN) is proposed that enables the method to process spatio-temporal information. In this extension, an additional layer is added to the network architecture that transforms a spatio-temporal input pattern into a single intermediate high-dimensional network state which in turn is mapped into a desired class label using a fast one-pass learning algorithm. The intermediate state is represented by a novel probabilistic reservoir computing approach in which a stochastic neural model introduces a non-deterministic component into a liquid state machine. A proof of concept is presented demonstrating an improved separation capability of the reservoir and consequently its suitability for an eSNN extension.

This study extends the recently proposed Evolving Spiking Neural Network (ESNN) architecture by combining it with an optimization algorithm, namely the Versatile Quantum-inspired Evolutionary Algorithm (vQEA). Following the wrapper approach, the method is used to identify relevant feature subsets and simultaneously evolve an optimal ESNN parameter setting. Applied to carefully designed benchmark data, containing irrelevant and redundant features of varying information quality, the ESNN-based feature selection procedure lead to excellent classification results and an accurate detection of relevant information in the dataset. Redundant and irrelevant features were rejected successively and in the order of the degree of information they contained.

This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different representations to explore the two search spaces: A binary representation for optimizing feature subsets and a continuous representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naïve Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.

The construction of a Spiking Neural Network (SNN), i.e. the choice of an appropriate topology and the configuration of its internal parameters, represents a great challenge for SNN based applications. Evolutionary Algorithms (EAs) offer an elegant solution for these challenges and methods capable of exploring both types of search spaces simultaneously appear to be the most promising ones. A variety of such heterogeneous optimization algorithms have emerged recently, in particular in the field of probabilistic optimization. In this paper, a literature review on heterogeneous optimization algorithms is presented and an example of probabilistic optimization of SNN is discussed in detail. The paper provides an experimental analysis of a novel Heterogeneous Multi-Model Estimation of Distribution Algorithm (hMM-EDA). First, practical guidelines for configuring the method are derived and then the performance of hMM-EDA is compared to state-of-the-art optimization algorithms. Results show hMM-EDA as a light-weight, fast and reliable optimization method that requires the configuration of only very few parameters. Its performance on a synthetic heterogeneous benchmark problem is highly competitive and suggests its suitability for the optimization of SNN.

This study points out some weaknesses of existing quantum-inspired evolutionary algorithms (QEA) and explains in particular how hitchhiking phenomena can slow down the discovery of optimal solutions and encourage premature convergence. A new algorithm, called versatile quantum- inspired evolutionary algorithm (vQEA), is proposed. With vQEA, the attractors moving the population through the search space are replaced at every generation without considering their fitness. The new algorithm is much more reactive. It always adapts the search toward the last promising solution found thus leading to a smoother and more efficient exploration. In this paper, vQEA is tested and compared to a classical genetic algorithm CGA and to a QEA on several benchmark problems. Experiments have shown that vQEA performs better than both CGA and QEA in terms of speed and accuracy. It is a highly scalable algorithm as well. Finally, the properties of the vQEA are discussed and compared to estimation of distribution algorithms (EDA).