scholarly journals Spike sorting: new trends and challenges of the era of high-density probes

2022 ◽  
Author(s):  
Alessio Buccino ◽  
Samuel Garcia ◽  
Pierre Yger
Keyword(s):  
Electrode Array ◽  
Neuronal Population ◽  
High Density ◽  
Spike Sorting ◽  
Learning Approaches ◽  
Personal View ◽  
Community Based ◽  
Advantages And Disadvantages ◽  
Processing Step ◽  
Development And Validation

Recording from a large neuronal population of neurons is a crucial challenge to unravel how information is processed by the brain. In this review, we highlight the recent advances made in the field of “spike sorting”, which is arguably a very essential processing step to extract neuronal activity from extracellular recordings. We more specifically target the challenges faced by newly manufactured high-density multi-electrode array devices (HD-MEA), e.g. Neuropixels probes. Among them, we cover in depth the prominent problem of drifts (movements of the neurons with respect to the recording devices) and the current solutions to circumscribe it. In addition, we also review recent contributions making use of deep learning approaches for spike sorting, highlighting their advantages and disadvantages. Next, we highlight efforts and advances in unifying, validating, and benchmarking spike sorting tools. Finally, we discuss the spike sorting field in terms of its open and unsolved challenges, specifically regarding scalability and reproducibility. We conclude by providing our personal view on the future of spike sorting, calling for a community-based development and validation of spike sorting algorithms and fully automated, cloud-based spike sorting solutions for the neuroscience community.

scholarly journals A Review of Computer-Aided Expert Systems for Breast Cancer Diagnosis

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2764
Author(s):  
Xin Yu Liew ◽  
Nazia Hameed ◽  
Jeremie Clos
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Deep Learning ◽  
Main Process ◽  
Learning Approaches ◽  
Learning Methods ◽  
Advantages And Disadvantages ◽  
Computer Aided ◽  
Conventional Methods ◽  
The Impact

A computer-aided diagnosis (CAD) expert system is a powerful tool to efficiently assist a pathologist in achieving an early diagnosis of breast cancer. This process identifies the presence of cancer in breast tissue samples and the distinct type of cancer stages. In a standard CAD system, the main process involves image pre-processing, segmentation, feature extraction, feature selection, classification, and performance evaluation. In this review paper, we reviewed the existing state-of-the-art machine learning approaches applied at each stage involving conventional methods and deep learning methods, the comparisons within methods, and we provide technical details with advantages and disadvantages. The aims are to investigate the impact of CAD systems using histopathology images, investigate deep learning methods that outperform conventional methods, and provide a summary for future researchers to analyse and improve the existing techniques used. Lastly, we will discuss the research gaps of existing machine learning approaches for implementation and propose future direction guidelines for upcoming researchers.

scholarly journals Community-based Early Warning and Adaptive Response System (EWARS) for mosquito borne diseases: An open source/open community approach

2014 ◽  
Vol XL-8 ◽  
pp. 167-170 ◽  
Author(s):  
A.N. Babu ◽  
B. Soman ◽  
E. Niehaus ◽  
J. Shah ◽  
N.L. Sarda ◽  
...  
Keyword(s):  
Open Source ◽  
Pilot Project ◽  
Spatial Decision Support System ◽  
Spatial Decision Support ◽  
Community Based ◽  
Response System ◽  
Community Approach ◽  
Surveillance And Response ◽  
Development And Validation ◽  
Control And Eradication

A variety of studies around the world have evaluated the use of remote sensing with and without GIS in communicable diseases. The ongoing Ebola epidemic has highlighted the risks that can arise for the global community from rapidly spreading diseases which may outpace attempts at control and eradication. This paper presents an approach to the development, deployment, validation and wide-spread adoption of a GIS-based temporo-spatial decision support system which is being collaboratively developed in open source/open community mode by an international group that came together under UN auspices. The group believes in an open source/open community approach to make the fruits of knowledge as widely accessible as possible. A core initiative of the groups is the EWARS project. It proposes to strengthen existing public health systems by the development and validation a model for a community based surveillance and response system which will initially address mosquito borne diseases in the developing world. At present mathematical modeling to support EWARS is at an advanced state, and it planned to embark on a pilot project

scholarly journals EXPLORING ALS AND DIM DATA FOR SEMANTIC SEGMENTATION USING CNNS

2018 ◽  
Vol XLII-1 ◽  
pp. 347-354 ◽  
Author(s):  
F. Politz ◽  
M. Sester
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Good Alternative ◽  
Aerial Images ◽  
Learning Approaches ◽  
Advantages And Disadvantages ◽  
Sensing Applications ◽  
High Level

<p><strong>Abstract.</strong> Over the past years, the algorithms for dense image matching (DIM) to obtain point clouds from aerial images improved significantly. Consequently, DIM point clouds are now a good alternative to the established Airborne Laser Scanning (ALS) point clouds for remote sensing applications. In order to derive high-level applications such as digital terrain models or city models, each point within a point cloud must be assigned a class label. Usually, ALS and DIM are labelled with different classifiers due to their varying characteristics. In this work, we explore both point cloud types in a fully convolutional encoder-decoder network, which learns to classify ALS as well as DIM point clouds. As input, we project the point clouds onto a 2D image raster plane and calculate the minimal, average and maximal height values for each raster cell. The network then differentiates between the classes ground, non-ground, building and no data. We test our network in six training setups using only one point cloud type, both point clouds as well as several transfer-learning approaches. We quantitatively and qualitatively compare all results and discuss the advantages and disadvantages of all setups. The best network achieves an overall accuracy of 96<span class="thinspace"></span>% in an ALS and 83<span class="thinspace"></span>% in a DIM test set.</p>

scholarly journals Speech Enhancement Using Deep Learning Methods: A Review

2021 ◽  
Vol 21 (1) ◽  
pp. 19
Author(s):  
Asri Rizki Yuliani ◽  
M. Faizal Amri ◽  
Endang Suryawati ◽  
Ade Ramdan ◽  
Hilman Ferdinandus Pardede
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Speech Enhancement ◽  
Speech Signal ◽  
Research Field ◽  
Learning Technologies ◽  
Learning Approaches ◽  
Speech Signal Processing ◽  
Generative Adversarial Network ◽  
Advantages And Disadvantages

Speech enhancement, which aims to recover the clean speech of the corrupted signal, plays an important role in the digital speech signal processing. According to the type of degradation and noise in the speech signal, approaches to speech enhancement vary. Thus, the research topic remains challenging in practice, specifically when dealing with highly non-stationary noise and reverberation. Recent advance of deep learning technologies has provided great support for the progress in speech enhancement research field. Deep learning has been known to outperform the statistical model used in the conventional speech enhancement. Hence, it deserves a dedicated survey. In this review, we described the advantages and disadvantages of recent deep learning approaches. We also discussed challenges and trends of this field. From the reviewed works, we concluded that the trend of the deep learning architecture has shifted from the standard deep neural network (DNN) to convolutional neural network (CNN), which can efficiently learn temporal information of speech signal, and generative adversarial network (GAN), that utilize two networks training.

The Usage Analysis of Machine Learning Methods for Intrusion Detection in Software-Defined Networks

2021 ◽  
pp. 959-975
Author(s):  
Derya Yiltas-Kaplan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Network Architectures ◽  
Software Defined Networks ◽  
Learning Approaches ◽  
Advantages And Disadvantages ◽  
Machine Learning Methods ◽  
Network Problems ◽  
Usage Analysis

This chapter focuses on the process of the machine learning with considering the architecture of software-defined networks (SDNs) and their security mechanisms. In general, machine learning has been studied widely in traditional network problems, but recently there have been a limited number of studies in the literature that connect SDN security and machine learning approaches. The main reason of this situation is that the structure of SDN has emerged newly and become different from the traditional networks. These structural variances are also summarized and compared in this chapter. After the main properties of the network architectures, several intrusion detection studies on SDN are introduced and analyzed according to their advantages and disadvantages. Upon this schedule, this chapter also aims to be the first organized guide that presents the referenced studies on the SDN security and artificial intelligence together.

scholarly journals A Gel‐Free Ti 3 C 2 T x ‐Based Electrode Array for High‐Density, High‐Resolution Surface Electromyography

2020 ◽  
Vol 5 (8) ◽  
pp. 2000325 ◽  
Author(s):  
Brendan B. Murphy ◽  
Patrick J. Mulcahey ◽  
Nicolette Driscoll ◽  
Andrew G. Richardson ◽  
Gregory T. Robbins ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Electromyography ◽  
Electrode Array ◽  
High Density

Perspectives on Blended Open Distance Education Learning and Teaching in a South African Context

2010 ◽  
pp. 20-49
Author(s):  
Shawren Singh ◽  
Hsuan Lorraine Liang
Keyword(s):  
South Africa ◽  
Distance Education ◽  
Blended Learning ◽  
South African ◽  
Tertiary Education ◽  
Learning Approaches ◽  
Learning And Teaching ◽  
Advantages And Disadvantages ◽  
African Context ◽  
The University

In this chapter, we will discuss the blended learning approach that has been adopted by the University of South Africa (an open and distance learning tertiary education institute). We will discuss our perspectives on using these blended learning approaches and tools in order to facilitate our teaching. We will then provide a comparison on the advantages and disadvantages of some of the blended approaches we have used. We will also discuss the future trends of the use of blended approaches in the context of open distance education and learning. Lastly, we will conclude this chapter by providing our perspectives on the blended learning and teaching approaches adopted by the University of South Africa.

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