450 research outputs found
Handwritten digit recognition by bio-inspired hierarchical networks
The human brain processes information showing learning and prediction
abilities but the underlying neuronal mechanisms still remain unknown.
Recently, many studies prove that neuronal networks are able of both
generalizations and associations of sensory inputs. In this paper, following a
set of neurophysiological evidences, we propose a learning framework with a
strong biological plausibility that mimics prominent functions of cortical
circuitries. We developed the Inductive Conceptual Network (ICN), that is a
hierarchical bio-inspired network, able to learn invariant patterns by
Variable-order Markov Models implemented in its nodes. The outputs of the
top-most node of ICN hierarchy, representing the highest input generalization,
allow for automatic classification of inputs. We found that the ICN clusterized
MNIST images with an error of 5.73% and USPS images with an error of 12.56%
A review of learning in biologically plausible spiking neural networks
Artificial neural networks have been used as a powerful processing tool in various areas such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has encouraged researchers to improve artificial neural networks by investigating the biological brain. Neurological research has significantly progressed in recent years and continues to reveal new characteristics of biological neurons. New technologies can now capture temporal changes in the internal activity of the brain in more detail and help clarify the relationship between brain activity and the perception of a given stimulus. This new knowledge has led to a new type of artificial neural network, the Spiking Neural Network (SNN), that draws more faithfully on biological properties to provide higher processing abilities. A review of recent developments in learning of spiking neurons is presented in this paper. First the biological background of SNN learning algorithms is reviewed. The important elements of a learning algorithm such as the neuron model, synaptic plasticity, information encoding and SNN topologies are then presented. Then, a critical review of the state-of-the-art learning algorithms for SNNs using single and multiple spikes is presented. Additionally, deep spiking neural networks are reviewed, and challenges and opportunities in the SNN field are discussed
SuperSpike: Supervised learning in multi-layer spiking neural networks
A vast majority of computation in the brain is performed by spiking neural
networks. Despite the ubiquity of such spiking, we currently lack an
understanding of how biological spiking neural circuits learn and compute
in-vivo, as well as how we can instantiate such capabilities in artificial
spiking circuits in-silico. Here we revisit the problem of supervised learning
in temporally coding multi-layer spiking neural networks. First, by using a
surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based
three factor learning rule capable of training multi-layer networks of
deterministic integrate-and-fire neurons to perform nonlinear computations on
spatiotemporal spike patterns. Second, inspired by recent results on feedback
alignment, we compare the performance of our learning rule under different
credit assignment strategies for propagating output errors to hidden units.
Specifically, we test uniform, symmetric and random feedback, finding that
simpler tasks can be solved with any type of feedback, while more complex tasks
require symmetric feedback. In summary, our results open the door to obtaining
a better scientific understanding of learning and computation in spiking neural
networks by advancing our ability to train them to solve nonlinear problems
involving transformations between different spatiotemporal spike-time patterns
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
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