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

A hierarchy of recurrent networks for speech recognition

Generative models for sequential data based on directed graphs of Restricted Boltzmann Machines (RBMs) are able to accurately model high dimensional sequences as recently shown. In these models, temporal dependencies in the input are discovered by either buffering previous visible variables or by recurrent connections of the hidden variables. Here we propose a modification of these models, the Temporal Reservoir Machine (TRM). It utilizes a recurrent artificial neural network (ANN) for integrating information from the input over time. This information is then fed into a RBM at each time step. To avoid difficulties of recurrent network learning, the ANN remains untrained and hence can be thought of as a random feature extractor. Using the architecture of multi-layer RBMs (Deep Belief Networks), the TRMs can be used as a building block for complex hierarchical models. This approach unifies RBM-based approaches for sequential data modeling and the Echo State Network, a powerful approach for black-box system identification. The TRM is tested on a spoken digits task under noisy conditions, and competitive performances compared to previous models are observed

HTM approach to image classification, sound recognition and time series forecasting

Dissertação de mestrado em Biomedical EngineeringThe introduction of Machine Learning (ML) on the orbit of the resolution of problems typically associated within the human behaviour has brought great expectations to the future. In fact, the possible development of machines capable of learning, in a similar way as of the humans, could bring grand perspectives to diverse areas like healthcare, the banking sector, retail, and any other area in which we could avoid the constant attention of a person dedicated to the solving of a problem; furthermore, there are those problems that are still not at the hands of humans to solve - these are now at the disposal of intelligent machines, bringing new possibilities to the humankind development. ML algorithms, specifically Deep Learning (DL) methods, lack a bigger acceptance by part of the community, even though they are present in various systems in our daily basis. This lack of confidence, mandatory to let systems make big, important decisions with great impact in the everyday life is due to the difficulty on understanding the learning mechanisms and previsions that result by the same - some algorithms represent themselves as ”black boxes”, translating an input into an output, while not being totally transparent to the outside. Another complication rises, when it is taken into account that the same algorithms are trained to a specific task and in accordance to the training cases found on their development, being more susceptible to error in a real environment - one can argue that they do not constitute a true Artificial Intelligence (AI). Following this line of thought, this dissertation aims at studying a new theory, Hierarchical Temporal Memory (HTM), that can be placed in the area of Machine Intelligence (MI), an area that studies the capacity of how the software systems can learn, in an identical way to the learning of a human being. The HTM is still a fresh theory, that lays on the present perception of the functioning of the human neocortex and assumes itself as under constant development; at the moment, the theory dictates that the neocortex zones are organized in an hierarchical structure, being a memory system, capable of recognizing spatial and temporal patterns. In the course of this project, an analysis was made to the functioning of the theory and its applicability to the various tasks typically solved with ML algorithms, like image classification, sound recognition and time series forecasting. At the end of this dissertation, after the evaluation of the different results obtained in various approaches, it was possible to conclude that even though these results were positive, the theory still needs to mature, not only in its theoretical basis but also in the development of libraries and frameworks of software, to capture the attention of the AI community.A introdução de ML na órbita da resolução de problemas tipicamente dedicados ao foro humano trouxe grandes expectativas para o futuro. De facto, o possível desenvolvimento de máquinas capazes de aprender, de forma semelhante aos humanos, poderia trazer grandes perspetivas para diversas áreas como a saúde, o setor bancário, retalho, e qualquer outra área em que se poderia evitar o constante alerta de uma pessoa dedicada a um problema; para além disso, problemas sem resolução humana passavam a estar a mercê destas máquinas, levando a novas possibilidades no desenvolvimento da humanidade. Apesar de se encontrar em vários sistemas no nosso dia-a-dia, estes algoritmos de ML, especificamente de DL, carecem ainda de maior aceitação por parte da comunidade, devido a dificuldade de perceber as aprendizagens e previsões resultantes, feitas pelos mesmos - alguns algoritmos apresentam-se como ”caixas negras”, traduzindo um input num output, não sendo totalmente transparente para o exterior - é necessária confiança nos sistemas que possam tomar decisões importantes e com grandes impactos no quotidiano; por outro lado, os mesmos algoritmos encontram-se treinados para uma tarefa específica e de acordo com os casos encontrados no desenvolvimento do seu treino, sendo mais suscetíveis a erros em ambientes reais, podendo se discutir que não constituem, por isso, uma verdadeira Inteligência Artificial. Seguindo este segmento, a presente dissertação procura estudar uma nova teoria, HTM, inserida na área de MI, que pretende dar a capacidade aos sistemas de software de aprenderem de uma forma idêntica a do ser humano. Esta recente teoria, assenta na atual perceção do funcionamento do neocórtex, estando por isso em constante desenvolvimento; no momento, e assumida como uma teoria que dita a hierarquização estrutural das zonas do neocórtex, sendo um sistema de memória, reconhecedor de padrões espaciais e temporais. Ao longo deste projeto, foi feita uma análise ao funcionamento da teoria, e a sua aplicabilidade a várias tarefas tipicamente resolvidas com algoritmos de ML, como classificação de imagem, reconhecimento de som e previsão de series temporais. No final desta dissertação, após uma avaliação dos diferentes resultados obtidos em várias abordagens, foi possível concluir que apesar dos resultadospositivos, a teoria precisa ainda de maturar, não só a nível teórico como a nível prático, no desenvolvimento de bibliotecas e frameworks de software, de forma a capturar a atenção da comunidade de Inteligência Artificial

Optoelectronic Reservoir Computing

Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input layer and a single output layer. Within these constraints many implementations are possible. Here we report an opto-electronic implementation of reservoir computing based on a recently proposed architecture consisting of a single non linear node and a delay line. Our implementation is sufficiently fast for real time information processing. We illustrate its performance on tasks of practical importance such as nonlinear channel equalization and speech recognition, and obtain results comparable to state of the art digital implementations.Comment: Contains main paper and two Supplementary Material

Durations of repeated non-words for children with cochlear implants

Durations of syllables for repeated non-words were calculated for 76 children with cochlear implants (CIs) and 16 children with normal hearing (NH). Average syllable durations did not differ significantly between the groups, however a final syllable lengthening ratio in CI children was significantly shorter than for their NH peers. Measures of hearing related demographics were not correlated with CI syllable measures

Processing speed, executive function, and age differences in remembering and knowing.

A group of young (n = 52, M = 23.27 years) and old (n = 52, M = 68.62 years) adults studied two lists of semantically unrelated nouns. For one list a time of 2 s was allowed for encoding, and for the other, 5 s. A recognition test followed where participants classified their responses according to Gardiner's (1988) remember-know procedure. Age differences for remembering and knowing were minimal in the faster 2-s encoding condition. However, in the longer 5-s encoding condition, younger persons produced significantly more remember responses, and older adults a greater number of know responses. This dissociation suggests that in the longer encoding condition, younger adults utilized a greater level of elaborative rehearsal governed by executive processes, whereas older persons employed maintenance rehearsal involving short-term memory. Statistical control procedures, however, found that independent measures of processing speed accounted for age differences in remembering and knowing and that independent measures of executive control had little influence. The findings are discussed in the light of contrasting theoretical accounts of recollective experience in old age