Spike-based learning of transfer functions with the SpiNNaker neuromimetic simulator

Davies, Sergio; Stewart, Terry; Eliasmith, Chris; Furber, Steve

doi:10.1109/ijcnn.2013.6706962

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…However, such models were "static", the algorithm performed was defined at design time. In 2013 a paper [174] was published, in which a novel learning rule was presented, describing its implementation into the SpiNNaker system, which enables models designed with the NEF to learn the function to be performed using a supervised framework. The authors showed that the proposed learning rule, belonging to the Prescribed Error Sensitivity class, is able to learn effectively both linear and non-linear function.…”

Section: Spinnakermentioning

confidence: 99%

Parallel Computing for Brain Simulation

Pastur-Romay

Porto-Pazos

Cedrón

2017

CTMC

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This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing.

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Section: Spinnakermentioning

confidence: 99%

Parallel Computing for Brain Simulation

Pastur-Romay

Porto-Pazos

Cedrón

2017

CTMC

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“…The SpiNNaker was created simulate real-time models, but the algorithms had to be defined in the design process, therefore the models were static. In 2013, a paper [ 109 ] was published, in which a novel learning rule was presented, describing its implementation into the SpiNNaker system, which allows the use of the Neural Engineering Framework to establish a supervised framework to learn both linear and non-linear functions. The learning rule belongs to the Prescribed Error Sensitivity class.…”

Section: Neuromorphic Chipsmentioning

confidence: 99%

Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

Pastur-Romay

Cedrón

Pazos

et al. 2016

IJMS

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Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.

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On the Eve of Artificial Minds

Eliasmith¹

2015

Open MIND

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Spike-based learning of transfer functions with the SpiNNaker neuromimetic simulator

Cited by 3 publications

References 27 publications

Parallel Computing for Brain Simulation

Parallel Computing for Brain Simulation

Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

On the Eve of Artificial Minds

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