Artificial neural networks analysis of sleep features and cognitive-behavioral profile in sleep-related hypermotor epilepsy and disorders of arousal

2021 ◽  
Vol 429 ◽  
pp. 118651
Author(s):  
Elisa Casaglia ◽  
Michela Figorilli ◽  
Antonella Gagliano ◽  
Ludovica Tamburrino ◽  
Roberta Coa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cognitive Behavioral ◽  
Behavioral Profile ◽  
Networks Analysis ◽  
Artificial Neural ◽  
Disorders Of Arousal

scholarly journals Computational Principles of Supervised Learning in the Cerebellum

2018 ◽  
Vol 41 (1) ◽  
pp. 233-253 ◽  
Author(s):  
Jennifer L. Raymond ◽  
Javier F. Medina
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Supervised Learning ◽  
Brain Area ◽  
Future Research ◽  
Brain Areas ◽  
Adaptive Mechanisms ◽  
Networks Analysis ◽  
Artificial Neural ◽  
Organizational Principles

Supervised learning plays a key role in the operation of many biological and artificial neural networks. Analysis of the computations underlying supervised learning is facilitated by the relatively simple and uniform architecture of the cerebellum, a brain area that supports numerous motor, sensory, and cognitive functions. We highlight recent discoveries indicating that the cerebellum implements supervised learning using the following organizational principles: ( a) extensive preprocessing of input representations (i.e., feature engineering), ( b) massively recurrent circuit architecture, ( c) linear input–output computations, ( d) sophisticated instructive signals that can be regulated and are predictive, ( e) adaptive mechanisms of plasticity with multiple timescales, and ( f) task-specific hardware specializations. The principles emerging from studies of the cerebellum have striking parallels with those in other brain areas and in artificial neural networks, as well as some notable differences, which can inform future research on supervised learning and inspire next-generation machine-based algorithms.

Discovering gastronomic tourists’ profiles through artificial neural networks: analysis, opinions and attitudes

2021 ◽  
pp. 1-12
Author(s):  
Salvador Moral-Cuadra ◽  
Miguel Á. Solano-Sánchez ◽  
Antonio Menor-Campos ◽  
Tomás López-Guzmán
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Networks Analysis ◽  
Artificial Neural

scholarly journals Recognition of Morphometric Vertebral Fractures by Artificial Neural Networks: Analysis from GISMO Lombardia Database

PLoS ONE ◽  
2011 ◽  
Vol 6 (11) ◽  
pp. e27277 ◽  
Author(s):  
Cristina Eller-Vainicher ◽  
Iacopo Chiodini ◽  
Ivana Santi ◽  
Marco Massarotti ◽  
Luca Pietrogrande ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Vertebral Fractures ◽  
Networks Analysis ◽  
Artificial Neural

Investigation of preparation of Ca-α-Sialon powders via artificial neural networks analysis

2014 ◽  
Vol 40 (1) ◽  
pp. 2287-2293 ◽  
Author(s):  
Haijun Zhang ◽  
Shuang Du ◽  
Yingnan Cao ◽  
Lilin Lu ◽  
Shaowei Zhang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Networks Analysis ◽  
Artificial Neural
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