Real-Time Classification of Multivariate Olfaction Data Using Spiking Neural Networks

Self Cite

In several application domains, electronic nose systems employing conventional data processing approaches incur substantial power and computational costs and limitations, such as significant latency and poor accuracy for classification. Recent developments in spike-based bio-inspired approaches have delivered solutions for the highly accurate classification of multivariate sensor data with minimized computational and power requirements. Although these methods have addressed issues related to efficient data processing and classification accuracy, other areas, such as reducing the processing latency to support real-time application and deploying spike-based solutions on supported hardware, have yet to be studied in detail. Through this investigation, we proposed a spiking neural network (SNN)-based classifier, implemented in a chip-emulation-based development environment, that can be seamlessly deployed on a neuromorphic system-on-a-chip (NSoC). Under three different scenarios of increasing complexity, the SNN was determined to be able to classify real-valued sensor data with greater than 90% accuracy and with a maximum latency of 3 s on the software-based platform. Highlights of this work included the design and implementation of a novel encoder for artificial olfactory systems, implementation of unsupervised spike-timing-dependent plasticity (STDP) for learning, and a foundational study on early classification capability using the SNN-based classifier.

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Hardware-Deployable Neuromorphic Solution for Encoding and Classification of Electronic Nose Data

Vanarse

Osseiran

Rassau

et al. 2019

Self Cite

“…Recent developments in neuromorphic olfaction have focused on leveraging the inherent advantages of the spike-based data representation to develop practical e-nose systems where key aspects such as data-to-spike encoding techniques, utilization of SNNs for pattern-recognition, and implementation of these models on low-power hardware are emphasized [ 17 , 18 , 19 , 20 , 21 , 22 ]. However, these neuromorphic models have mainly focused on data transformation based on biological spike encoding architectures, while overlooking the overall performance of the system to identify target odors with minimum computational resources and latency.…”

Section: Introductionmentioning

confidence: 99%

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

Vanarse

Espinosa-Ramos

Osseiran

et al. 2020

Self Cite

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.

“…These advantages are precisely the functions that we want in the odor interaction research. Machine learning methods have also attracted the attention of odor researchers and have been applied in various forms [20]. Szulczynski et al proposed an electronic nose and the odor intensity was directly linked to the results of analytical air monitoring with a fuzzy logic algorithm [21].…”

Section: Introductionmentioning

confidence: 99%

“…Sensors 2020,20, x FOR PEER REVIEW 3 of 12 procedure and environmental requirements were described in these references. In this study, the collected dataset contains 31 samples of binary mixture EA+BA, 21 samples of EA+EB, 22 samples of BA+EB, 24 samples of PA+VA, 24 samples of PA+HEP, 24 samples of VA+HEP, 34 samples of B+T, 31 samples of B+E, and 24 samples of T+E.…”

mentioning

confidence: 99%

Visual Analysis of Odor Interaction Based on Support Vector Regression Method

Yan

Liu

2020

The complex odor interaction between odorants makes it difficult to predict the odor intensity of their mixtures. The analysis method is currently one of the factors limiting our understanding of the odor interaction laws. We used a support vector regression algorithm to establish odor intensity prediction models for binary esters, aldehydes, and aromatic hydrocarbon mixtures, respectively. The prediction accuracy to both training samples and test samples demonstrated the high prediction capacity of the support vector regression model. Then the optimized model was used to generate extra odor data by predicting the odor intensity of more simulated samples with various mixing ratios and concentration levels. Based on these olfactory measured and model predicted data, the odor interaction was analyzed in the form of contour maps. This intuitive method showed more details about the odor interaction pattern in the binary mixture. We found that that the antagonism effect was commonly observed in these binary mixtures and the interaction degree was more intense when the components' mixing ratio was close. Meanwhile, the odor intensity level of the odor mixture barely influenced the interaction degree. The machine learning algorithms were considered promising tools in odor researches.3 of 12 standard in odor intensity evaluation [26]. More details about the testing procedure and environmental requirements were described in these references. In this study, the collected dataset contains 31 samples of binary mixture EA+BA, 21 samples of EA+EB, 22 samples of BA+EB, 24 samples of PA+VA, 24 samples of PA+HEP, 24 samples of VA+HEP, 34 samples of B+T, 31 samples of B+E, and 24 samples of T+E.