scholarly journals Error processing network dynamics in schizophrenia

NeuroImage ◽  
2011 ◽  
Vol 54 (2) ◽  
pp. 1495-1505 ◽  
Author(s):  
Karla E. Becerril ◽  
Grega Repovs ◽  
Deanna M. Barch
Keyword(s):  
Network Dynamics ◽  
Error Processing ◽  
Processing Network

scholarly journals Transient neural network dynamics in cognitive ageing

2021 ◽  
Author(s):  
Roni Tibon ◽  
Kamen A. Tsvetanov ◽  
Darren Price ◽  
David Nesbitt ◽  
Cam CAN ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Dynamics ◽  
Cognitive Ageing ◽  
Neural Network Dynamics

scholarly journals Alzheimer’s Disease Progressively Reduces Visual Functional Network Connectivity

2021 ◽  
pp. 1-14
Author(s):  
Jie Huang ◽  
Paul Beach ◽  
Andrea Bozoki ◽  
David C. Zhu
Keyword(s):  
Visual Cortex ◽  
Lower Order ◽  
Resting State ◽  
Visual Processing ◽  
Network Connectivity ◽  
Higher Order ◽  
Functional Network ◽  
Functional Networks ◽  
The Face ◽  
Processing Network

Background: Postmortem studies of brains with Alzheimer’s disease (AD) not only find amyloid-beta (Aβ) and neurofibrillary tangles (NFT) in the visual cortex, but also reveal temporally sequential changes in AD pathology from higher-order association areas to lower-order areas and then primary visual area (V1) with disease progression. Objective: This study investigated the effect of AD severity on visual functional network. Methods: Eight severe AD (SAD) patients, 11 mild/moderate AD (MAD), and 26 healthy senior (HS) controls undertook a resting-state fMRI (rs-fMRI) and a task fMRI of viewing face photos. A resting-state visual functional connectivity (FC) network and a face-evoked visual-processing network were identified for each group. Results: For the HS, the identified group-mean face-evoked visual-processing network in the ventral pathway started from V1 and ended within the fusiform gyrus. In contrast, the resting-state visual FC network was mainly confined within the visual cortex. AD disrupted these two functional networks in a similar severity dependent manner: the more severe the cognitive impairment, the greater reduction in network connectivity. For the face-evoked visual-processing network, MAD disrupted and reduced activation mainly in the higher-order visual association areas, with SAD further disrupting and reducing activation in the lower-order areas. Conclusion: These findings provide a functional corollary to the canonical view of the temporally sequential advancement of AD pathology through visual cortical areas. The association of the disruption of functional networks, especially the face-evoked visual-processing network, with AD severity suggests a potential predictor or biomarker of AD progression.

scholarly journals Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Erik Buhmann ◽  
Sascha Diefenbacher ◽  
Engin Eren ◽  
Frank Gaede ◽  
Gregor Kasieczka ◽  
...  
Keyword(s):  
Particle Physics ◽  
High Speed ◽  
Deep Neural Networks ◽  
High Fidelity ◽  
High Fidelity Simulation ◽  
Physical Processes ◽  
Electromagnetic Showers ◽  
Information Bottleneck ◽  
First Time ◽  
Processing Network

AbstractAccurate simulation of physical processes is crucial for the success of modern particle physics. However, simulating the development and interaction of particle showers with calorimeter detectors is a time consuming process and drives the computing needs of large experiments at the LHC and future colliders. Recently, generative machine learning models based on deep neural networks have shown promise in speeding up this task by several orders of magnitude. We investigate the use of a new architecture—the Bounded Information Bottleneck Autoencoder—for modelling electromagnetic showers in the central region of the Silicon-Tungsten calorimeter of the proposed International Large Detector. Combined with a novel second post-processing network, this approach achieves an accurate simulation of differential distributions including for the first time the shape of the minimum-ionizing-particle peak compared to a full Geant4 simulation for a high-granularity calorimeter with 27k simulated channels. The results are validated by comparing to established architectures. Our results further strengthen the case of using generative networks for fast simulation and demonstrate that physically relevant differential distributions can be described with high accuracy.

scholarly journals Phonological transfer effects in novice learners: A learner's brain detects grammar errors only if the language sounds familiar

2021 ◽  
pp. 1-14
Author(s):  
Sabine Gosselke Berthelsen ◽  
Merle Horne ◽  
Yury Shtyrov ◽  
Mikael Roll
Keyword(s):  
Second Language ◽  
Second Language Acquisition ◽  
Language Acquisition ◽  
Spoken Word ◽  
Error Processing ◽  
Transfer Effects ◽  
Association Learning ◽  
Novice Learners ◽  
Grammar Rules ◽  
Related Response

Abstract Many aspects of a new language, including grammar rules, can be acquired and accessed within minutes. In the present study, we investigate how initial learners respond when the rules of a novel language are not adhered to. Through spoken word-picture association-learning, tonal and non-tonal speakers were taught artificial words. Along with lexicosemantic content expressed by consonants, the words contained grammatical properties embedded in vowels and tones. Pictures that were mismatched with any of the words’ phonological cues elicited an N400 in tonal learners. Non-tonal learners only produced an N400 when the mismatch was based on a word's vowel or consonants, not the tone. The emergence of the N400 might indicate that error processing in L2 learners (unlike canonical processing) does not initially differentiate between grammar and semantics. Importantly, only errors based on familiar phonological cues evoked a mismatch-related response, highlighting the importance of phonological transfer in initial second language acquisition.

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