Neural mechanisms of vibrotactile categorization
نویسندگان
چکیده
منابع مشابه
Mechanisms of Interference in Vibrotactile Working Memory
In previous studies of interference in vibrotactile working memory, subjects were presented with an interfering distractor stimulus during the delay period between the target and probe stimuli in a delayed match-to-sample task. The accuracy of same/different decisions indicated feature overwriting was the mechanism of interference. However, the distractor was presented late in the delay period,...
متن کاملMechanisms of Categorization in Infancy
This paper presents a connectionist model of correlation based categorization by 10month-old infants (Younger, 1985). Simple autoencoder networks were exposed to the same stimuli used to test 10-month-olds. The familiarisation regime was kept as close as possible to that used with the infants. The model’s performance matched that of the infants. Both infants and networks used co-variation infor...
متن کاملCategorization of Programs Using Neural
This paper describes some experiments based on the use of neural networks for assistence in the quality assessment of programs, especially in connection with the reengineering of legacy systems. We use Koho-nen networks, or self-organizing maps, for the cat-egorization of programs: Programs with similar features are grouped together in atwo-dimensional neighbourhood , whereas dissimilar program...
متن کاملNeural correlates of vibrotactile working memory in the human brain.
Recent neurophysiological studies in macaques identified a network of brain regions related to vibrotactile working memory (WM), including somatosensory, motor, premotor, and prefrontal cortex. In these studies, monkeys decided which of two vibrotactile stimuli that were sequentially applied to their fingertips and separated by a short delay had the higher vibration frequency. Using the same ta...
متن کاملNeural mechanisms for learning of attention control and pattern categorization as basis for robot cognition
We present mechanisms for attention control and pattern categorization as the basis for robot cognition. For attention, we gather information from attentional feature maps extracted from sensory data constructing salience maps to decide where to foveate. For iden-tiication, multi-feature maps are used as input to an associative memory, allowing the system to classify a pattern representing a re...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Human Brain Mapping
سال: 2019
ISSN: 1065-9471,1097-0193
DOI: 10.1002/hbm.24581