Auckland University of Technology

About: Auckland University of Technology is a education organization based out in Auckland, New Zealand. It is known for research contribution in the topics: Population & Context (language use). The organization has 4116 authors who have published 13461 publications receiving 353076 citations. The organization is also known as: AUT & AUT University.

Selection and Optimization of Temporal Spike Encoding Methods for Spiking Neural Networks

Abstract: Spiking neural networks (SNNs) receive trains of spiking events as inputs. In order to design efficient SNN systems, real-valued signals must be optimally encoded into spike trains so that the task-relevant information is retained. This paper provides a systematic quantitative and qualitative analysis and guidelines for optimal temporal encoding. It proposes a methodology of a three-step encoding workflow: method selection by signal characteristics, parameter optimization by error metrics between original and reconstructed signals, and validation by comparison of the original signal and the encoded spike train. Four encoding methods are analyzed: one stimulus estimation [Ben’s Spiker algorithm (BSA)] and three temporal contrast [threshold-based, step-forward (SW), and moving-window (MW)] encodings. A short theoretical analysis is provided, and the extended quantitative analysis is carried out applying four types of test signals: step-wise signal, smooth (sinusoid) signal with added noise, trended smooth signal, and event-like smooth signal. Various time-domain and frequency spectrum properties are explored, and a comparison is provided. BSA, the only method providing unipolar spikes, was shown to be ineffective for step-wise signals, but it can follow smoothly changing signals if filter coefficients are scaled appropriately. Producing bipolar (positive and negative) spike trains, SW encoding was most effective for all types of signals as it proved to be robust and easy to optimize. Signal-to-noise ratio (SNR) can be recommended as the error metric for parameter optimization. Currently, only a visual check is available for final validation.

The role of Kemeny's constant in properties of Markov chains

Abstract: In a finite irreducible Markov chain with stationary probabilities {π i } and mean first passage times m ij (mean recurrence time when i = j) it was first shown, by Kemeny and Snell (1960), that is a constant, K, (Kemeny's constant) not depending on i. A variety of techniques for finding expressions and bounds for K are given. The main interpretation focuses on its role as the expected time to mixing in a Markov chain. Various applications are considered including perturbation results, mixing on directed graphs and its relation to the Kirchhoff index of regular graphs.

Markets changing, changing markets: Institutional work as market shaping:

Abstract: There is increasing interest in the marketing discipline to adopt an institutional perspective when examining markets. This requires seeing markets as complex systems that evolve through time, rath...