Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals

Spiking neural networks (SNNs) enable power-efficient implementations due to their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN that uses unsupervised learning to extract discriminative features from speech signals, which can subsequently be used in a classifier. The architecture consists of a spiking convolutional/pooling layer followed by a fully connected spiking layer for feature discovery. The convolutional layer of leaky, integrate-and-fire (LIF) neurons represents primary acoustic features. The fully connected layer is equipped with a probabilistic spike-timing-dependent plasticity learning rule. This layer represents the discriminative features through probabilistic, LIF neurons. To assess the discriminative power of the learned features, they are used in a hidden Markov model (HMM) for spoken digit recognition. The experimental results show performance above 96% that compares favorably with popular statistical feature extraction methods. Our results provide a novel demonstration of unsupervised feature acquisition in an SNN

Metonymization Procedures in Artificial Intelligence (in English and Romanian Languages)

In this article, we address term creation in 3 terms from the domain of artificial intelligence, which are representative and revelatory for the study of reterminologization in the triad of the specialized domains of emotional intelligence, cognitive intelligence and artificial intelligence. The study of reterminologization in the announced triad traces conceptual interferences to state-of-the-art terms created in the domain of artificial intelligence, in particular, through metonymization. The domain of artificial intelligence is a dynamic and prolific one, with a terminological variety that allows the study of conceptual interferences inspired from the domain of human intelligence. The terms that are subject to analysis in the article have a high degree of complexity and conceptually encompass the triad of emotional, cognitive and artificial intelligence