Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware

Behrenbeck, Jan; Tayeb, Zied; Bhiri, Cyrine; Richter, Christoph; Rhodes, Oliver; Kasabov, Nikola; Espinosa-Ramos, Josafath I.; Furber, Steve; Cheng, Gordon; Conradt, Jörg

doi:10.1088/1741-2552/aafabc

Cited by 32 publications

(22 citation statements)

References 57 publications

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“…The spiking models developed using the NeuCube framework can be deployed on SpiNNaker [32,43], a neuromorphic hardware platform. Future research based on this study can take advantage of the hardware compatibility to further reduce the processing latency, and hence, a real-time low-power classification back-end for an artificial olfactory system can be envisaged.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Taking inspiration from the biological olfactory pathway and based on the aforementioned NeuCube modules, our model is comprised of three key stages of electronic nose data processing: transformation, learning, and classification. Promising results have been obtained using NeuCube models for various applications [27,30,32], providing evidence of the robust classification capabilities of the SNN framework, even under potentially noisy and multidimensional spatiotemporal data. These results make NeuCube one of the ideal candidates to explore the applicability of the brain-inspired SNN model for the classification of raw sensor responses.…”

Section: System Architecturementioning

confidence: 99%

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Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification

Vanarse

Espinosa-Ramos

Osseiran

et al. 2020

Sensors

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Existing methods in neuromorphic olfaction mainly focus on implementing the data transformation based on the neurobiological architecture of the olfactory pathway. While the transformation is pivotal for the sparse spike-based representation of odor data, classification techniques based on the bio-computations of the higher brain areas, which process the spiking data for identification of odor, remain largely unexplored. This paper argues that brain-inspired spiking neural networks constitute a promising approach for the next generation of machine intelligence for odor data processing. Inspired by principles of brain information processing, here we propose the first spiking neural network method and associated deep machine learning system for classification of odor data. The paper demonstrates that the proposed approach has several advantages when compared to the current state-of-the-art methods. Based on results obtained using a benchmark dataset, the model achieved a high classification accuracy for a large number of odors and has the capacity for incremental learning on new data. The paper explores different spike encoding algorithms and finds that the most suitable for the task is the step-wise encoding function. Further directions in the brain-inspired study of odor machine classification include investigation of more biologically plausible algorithms for mapping, learning, and interpretation of odor data along with the realization of these algorithms on some highly parallel and low power consuming neuromorphic hardware devices for real-world applications.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: System Architecturementioning

confidence: 99%

Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification

Vanarse

Espinosa-Ramos

Osseiran

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

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“…SpiNNaker is used to implement massively parallel hardware SNNs in the litterature, such as NeuCube in [20], where a SNN is implemented on SpiNNaker to capture and classify spatio-temporal information from EEG (Electro-EncephaloGram). Notably, this architecture offers the possibility to pause classification process to learn new samples or classes, in an Incremental Learning [21] [22] fashion, which is an interesting property.…”

Section: Spinnakermentioning

confidence: 99%

Design Space Exploration of Hardware Spiking Neurons for Embedded Artificial Intelligence

2020

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A B S T R A C TMachine learning is yielding unprecedented interest in research and industry, due to recent success in many applied contexts such as image classification and object recognition. However, the deployment of these systems requires huge computing capabilities, thus making them unsuitable for embedded systems. To deal with this limitation, many researchers are investigating brain-inspired computing, which would be a perfect alternative to the conventional Von Neumann architecture based computers (CPU/GPU) that meet the requirements for computing performance, but not for energy-efficiency. Therefore, neuromorphic hardware circuits that are adaptable for both parallel and distributed computations need to be designed. In this paper, we focus on Spiking Neural Networks (SNNs) with a comprehensive study of information coding methods and hardware exploration. In this context, we propose a framework for neuromorphic hardware design space exploration, which allows to define a suitable architecture based on application-specific constraints and starting from a wide variety of possible architectural choices. For this framework, we have developed a behavioral level simulator for neuromorphic hardware architectural exploration named NAXT. Moreover, we propose modified versions of the standard Rate Coding technique to make trade-offs with the Time Coding paradigm, which is characterized by the low number of spikes propagating in the network. Thus, we are able to reduce the number of spikes while keeping the same neuron's model, which results in an SNN with fewer events to process. By doing so, we seek to reduce the amount of power consumed by the hardware. Furthermore, we present three neuromorphic hardware architectures in order to quantitatively study the implementation of SNNs. One of these architectures integrates a novel hybrid structure: a highly-parallel computation core for most solicited layers, and time-multiplexed computation units for deeper layers. These architectures are derived from a novel funnel-like Design Space Exploration framework for neuromorphic hardware.

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“…In [11] the authors used the NeuCube spiking model, to classify hand gestures. Recently, a neuromorphic implementation of NeuCube was proposed on SpiNNaker platform [12]. In [13], the authors presented a software SNN used for EMG feature extraction and classification with high accuracy that is trained with the back-propagation learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of EMG Signals Using a Neuromorphic Implementation of a Spiking Neural Network

Donati

Payvand

Risi

et al. 2019

IEEE Trans. Biomed. Circuits Syst.

105

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An accurate description of muscular activity plays an important role in the clinical diagnosis and rehabilitation research. The electromyography (EMG) is the most used technique to make accurate descriptions of muscular activity. The EMG is associated with the electrical changes generated by the activity of the motor neurons. Typically, to decode the muscular activation during different movements, a large number of individual motor neurons are monitored simultaneously, producing large amounts of data to be transferred and processed by the computing devices. In this paper, we follow an alternative approach that can be deployed locally on the sensor side. We propose a neuromorphic implementation of a spiking neural network (SNN) to extract spatio-temporal information of EMG signals locally and classify hand gestures with very low power consumption. We present experimental results on the input data stream using a mixed-signal analog/digital neuromorphic processor. We performed a thorough investigation on the performance of the SNN implemented on the chip, by: first, calculating PCA on the activity of the silicon neurons at the input and the hidden layers to show how the network helps in separating the samples of different classes; second, performing classification of the data using state-of-theart SVM and logistic regression methods and a hardware-friendly spike-based read-out. The traditional algorithm achieved a classification rate of 84% and 81%, respectively, and the spiking learning method achieved 74%. The power consumption of the SNN is 0.05 mW, showing the potential of this approach for ultra-low power processing.

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Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware

Cited by 32 publications

References 57 publications

Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification

Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification

Design Space Exploration of Hardware Spiking Neurons for Embedded Artificial Intelligence

Discrimination of EMG Signals Using a Neuromorphic Implementation of a Spiking Neural Network

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