Object and Character Recognition Using Spiking Neural Network

Bawane, Priyanka; Gadariye, Snehali; Chaturvedi, Soni; Khurshid, Aasim

doi:10.1016/j.matpr.2017.11.093

Cited by 19 publications

(7 citation statements)

References 3 publications

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“…Study in SNN as the powerful neural network approaches has motivated researcher to focus on bioinspired methods for pattern recognition [13], [14]. SNN also learn to convert speech signal into spike train signatures which are distinguishable for other speech signals to represent different words [15].…”

Section: Related Workmentioning

confidence: 99%

Smartphone Sensor Accelerometer Data for Human Activity Recognition Using Spiking Neural Network

Suriani¹,

Rashid²

2021

IJMLC

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Recognizing human actions is a challenging task and actively research in computer vision community. The task of human activity recognition has been widely used in various application such as human monitoring in a hospital or public spaces. This work applied open dataset of smartphones accelerometer data for various type of activities. The analogue input data is encoded into the spike trains using some form of a rate-based method. Spiking neural network is a simplified form of dynamic artificial network. Therefore, this network is expected to model and generate action potential from the leaky integrate-and-fire spike response model. The leaning rule is adaptive and efficient to present synapse exciting and inhibiting firing neuron. The result found that the proposed model presents the state-of-the-art performance at a low computational cost.

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Section: Related Workmentioning

confidence: 99%

Smartphone Sensor Accelerometer Data for Human Activity Recognition Using Spiking Neural Network

Suriani¹,

Rashid²

2021

IJMLC

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“…Despite training challenges due to the non-differentiable nature of spikes, significant progress has been made. Some noteworthy achievements include the use of SNNs in classic supervised learning applications like object recognition and biological signal classification [2], and the implementation of reinforcement and unsupervised learning applications using the Spike Timing Dependent Plasticity training method [4].…”

Section: Spiking Neural Networkmentioning

confidence: 99%

“…Designed to emulate the brain's dynamics with a higher degree of biological plausibility, Spiking Neural Networks (SNNs) are a strong candidate for future machine learning [1]. SNNs can obtain the same accuracy as classic artificial neural networks (ANNs) for a wide variety of applications, while only using a fraction of its power [2]- [4]. As a result, the popularity of SNNs is expected to increase in the near future, especially for low-power applications like IoT.…”

Section: Introductionmentioning

confidence: 99%

Revealing the Secrets of Spiking Neural Networks: The Case of Izhikevich Neuron

Garaffa

Aljuffri

Reinbrecht

et al. 2021

2021 24th Euromicro Conference on Digital System Design (DSD)

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“…A significant amount of research has been conducted on the adaptive neural control [23,39,50,. Moreover, thanks to recent years of technological advances, significant progress allows neural machine learning to enjoy a particular international notoriety with the use of deep learning and reinforcement learning approaches [78][79][80][81][82], offering new creative tools in control design [83,84]. Recently, several reviews about adaptive neural control have been conducted.…”

Section: Introductionmentioning

confidence: 99%