scholarly journals Still with Us after All These Years: Issues of Neuronal Classification Revisited

2012 ◽  
Vol 80 (1) ◽  
pp. 1-3
Author(s):  
Michael H. Rowe
2016 ◽  
Vol 83 ◽  
pp. 78-91 ◽  
Author(s):  
Mélanie Noyel ◽  
Philippe Thomas ◽  
André Thomas ◽  
Patrick Charpentier

2015 ◽  
Vol 9 (3) ◽  
pp. 261-278 ◽  
Author(s):  
Babatunde Oluleye ◽  
Armstrong Leisa ◽  
Diepeveen Dean ◽  
Leng Jinsong

2009 ◽  
Vol 182 (2) ◽  
pp. 272-278 ◽  
Author(s):  
Dušan Ristanović ◽  
Nebojša T. Milošević ◽  
Ivan B. Stefanović ◽  
Dušan Marić ◽  
Ivan Popov

2013 ◽  
Vol 77 (3) ◽  
pp. 189-200 ◽  
Author(s):  
Napamanee Kornthong ◽  
Yotsawan Tinikul ◽  
Kanjana Khornchatri ◽  
Jirawat Saeton ◽  
Sirilug Magerd ◽  
...  

2019 ◽  
Vol 6 (1) ◽  
pp. 49-60
Author(s):  
Mustapha Belaissaoui ◽  
József Jurassec

Malware classification and detection is an important factor in computer system security. However, signature-based methods currently used cannot provide an accurate detection of zero-day attacks and polymorphic viruses. This is why there is a need for detection based on machine learning. The purpose of this work is to present a deep neuronal classification method using convolutional and recurrent network layers in order to obtain the best features for classification. The proposed model achieves 98.73% accuracy on the Microsoft malware dataset.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Carolina Bengtsson Gonzales ◽  
Steven Hunt ◽  
Ana B. Munoz-Manchado ◽  
Chris J. McBain ◽  
Jens Hjerling-Leffler

Abstract Determining the cellular content of the nervous system in terms of cell types and the rules of their connectivity represents a fundamental challenge to the neurosciences. The recent advent of high-throughput techniques, such as single-cell RNA-sequencing has allowed for greater resolution in the identification of cell types and/or states. Although most of the current neuronal classification schemes comprise discrete clusters, several recent studies have suggested that, perhaps especially, within the striatum, neuronal populations exist in continua, with regards to both their molecular and electrophysiological properties. Whether these continua are stable properties, established during development, or if they reflect acute differences in activity-dependent regulation of critical genes is currently unknown. We set out to determine whether gradient-like molecular differences in the recently described Pthlh-expressing inhibitory interneuron population, which contains the Pvalb-expressing cells, correlate with differences in morphological and connectivity properties. We show that morphology and long-range inputs correlate with a spatially organized molecular and electrophysiological gradient of Pthlh-interneurons, suggesting that the processing of different types of information (by distinct anatomical striatal regions) has different computational requirements.


1997 ◽  
Vol 08 (03) ◽  
pp. 339-357 ◽  
Author(s):  
A. Lörincz

It is argued that a novel control architecture, the Static and Dynamic State (SDS) feedback scheme, which utilizes speed-field tracking, exhibits global stability, and allows on-line tuning by any adaptation mechanism without canceling stability if certain structural conditions are met, can be viewed as a model of basal ganglia-thalamocortical loops since (1) the SDS scheme predicts the neuronal groups that fit neuronal classification in the supplementary motor area, the motor cortex and the putamen, (2) the structural stability conditions require parallel channels, a feature that these loops provide, and (3) the SDS scheme predicts two major disorders that can be identified as Parkinson's and Huntington's diseases. Simulations suggests that the basal ganglia work outside the realm of the stability condition allowed by the robustness of the scheme and required for increased computation speeds.


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