scholarly journals The temporal binding deficit hypothesis of autism

2002 ◽  
Vol 14 (2) ◽  
pp. 209-224 ◽  
Author(s):  
JON BROCK ◽  
CAROLINE C. BROWN ◽  
JILL BOUCHER ◽  
GINA RIPPON

Frith has argued that people with autism show “weak central coherence,” an unusual bias toward piecemeal rather than configurational processing and a reduction in the normal tendency to process information in context. However, the precise cognitive and neurological mechanisms underlying weak central coherence are still unknown. We propose the hypothesis that the features of autism associated with weak central coherence result from a reduction in the integration of specialized local neural networks in the brain caused by a deficit in temporal binding. The visuoperceptual anomalies associated with weak central coherence may be attributed to a reduction in synchronization of high-frequency gamma activity between local networks processing local features. The failure to utilize context in language processing in autism can be explained in similar terms. Temporal binding deficits could also contribute to executive dysfunction in autism and to some of the deficits in socialization and communication.

2015 ◽  
Vol 27 (8) ◽  
pp. 1542-1551 ◽  
Author(s):  
Kristof Strijkers ◽  
Daisy Bertrand ◽  
Jonathan Grainger

We investigated how linguistic intention affects the time course of visual word recognition by comparing the brain's electrophysiological response to a word's lexical frequency, a well-established psycholinguistic marker of lexical access, when participants actively retrieve the meaning of the written input (semantic categorization) versus a situation where no language processing is necessary (ink color categorization). In the semantic task, the ERPs elicited by high-frequency words started to diverge from those elicited by low-frequency words as early as 120 msec after stimulus onset. On the other hand, when categorizing the colored font of the very same words in the color task, word frequency did not modulate ERPs until some 100 msec later (220 msec poststimulus onset) and did so for a shorter period and with a smaller scalp distribution. The results demonstrate that, although written words indeed elicit automatic recognition processes in the brain, the speed and quality of lexical processing critically depends on the top–down intention to engage in a linguistic task.


2020 ◽  
Vol 16 (11) ◽  
pp. e1008342
Author(s):  
Zhewei Zhang ◽  
Huzi Cheng ◽  
Tianming Yang

The brain makes flexible and adaptive responses in a complicated and ever-changing environment for an organism’s survival. To achieve this, the brain needs to understand the contingencies between its sensory inputs, actions, and rewards. This is analogous to the statistical inference that has been extensively studied in the natural language processing field, where recent developments of recurrent neural networks have found many successes. We wonder whether these neural networks, the gated recurrent unit (GRU) networks in particular, reflect how the brain solves the contingency problem. Therefore, we build a GRU network framework inspired by the statistical learning approach of NLP and test it with four exemplar behavior tasks previously used in empirical studies. The network models are trained to predict future events based on past events, both comprising sensory, action, and reward events. We show the networks can successfully reproduce animal and human behavior. The networks generalize the training, perform Bayesian inference in novel conditions, and adapt their choices when event contingencies vary. Importantly, units in the network encode task variables and exhibit activity patterns that match previous neurophysiology findings. Our results suggest that the neural network approach based on statistical sequence learning may reflect the brain’s computational principle underlying flexible and adaptive behaviors and serve as a useful approach to understand the brain.


2018 ◽  
Vol 10 (3) ◽  
pp. 6-13 ◽  
Author(s):  
N. D. Sorokina ◽  
S. S. Pertsov ◽  
G. V. Selitsky

Recent studies show that the brain gamma activity includes both the gamma rhythm (standard EEG) and high frequency (100-1000 Hz) as well as super-high (>1000 Hz) frequency oscillations, as recorded by electrocorticography. As reported in the literature, the high-frequency oscillations (80-500 Hz) are highly informative markers of an epileptic focus. In this review, we analyze features of high-frequency activity associated with the epileptiform activity, and its relation to the seizure onset range. Further study of high-frequency bioelectric activity of the brain is of interest to researchers and clinicians, and may improve the EEG differential diagnosis of epilepsy.


2019 ◽  
Vol 2 (12) ◽  
pp. 9-12
Author(s):  
G. V. Selitsky ◽  
S. S. Pertsov ◽  
N. D. Sorokina

Modern studies of gamma rhythm indicate that gamma activity (30–80 Hz in standard EEG), and high-frequency (80–1000 Hz) and ultra-frequency oscillations (more than 1000 Hz), recorded by ECOG, are highly informative markers of epileptic focus. Further study of high-frequency bioelectric activity of the brain is of interest to researchers and clinicians in order to improve the electroencephalographic differential diagnosis in epilepsy.


2021 ◽  
Vol 15 ◽  
Author(s):  
Anup Tuladhar ◽  
Jasmine A. Moore ◽  
Zahinoor Ismail ◽  
Nils D. Forkert

Deep neural networks, inspired by information processing in the brain, can achieve human-like performance for various tasks. However, research efforts to use these networks as models of the brain have primarily focused on modeling healthy brain function so far. In this work, we propose a paradigm for modeling neural diseases in silico with deep learning and demonstrate its use in modeling posterior cortical atrophy (PCA), an atypical form of Alzheimer’s disease affecting the visual cortex. We simulated PCA in deep convolutional neural networks (DCNNs) trained for visual object recognition by randomly injuring connections between artificial neurons. Results showed that injured networks progressively lost their object recognition capability. Simulated PCA impacted learned representations hierarchically, as networks lost object-level representations before category-level representations. Incorporating this paradigm in computational neuroscience will be essential for developing in silico models of the brain and neurological diseases. The paradigm can be expanded to incorporate elements of neural plasticity and to other cognitive domains such as motor control, auditory cognition, language processing, and decision making.


2017 ◽  
Author(s):  
Toshitake Asabuki ◽  
Naoki Hiratani ◽  
Tomoki Fukai

AbstractInterpretation and execution of complex sequences is crucial for various cognitive tasks such as language processing and motor control. The brain solves this problem arguably by dividing a sequence into discrete chunks of contiguous items. While chunking has been accounted for by predictive uncertainty, alternative mechanisms have also been suggested, and the mechanism underlying chunking is poorly understood. Here, we propose a class of unsupervised neural networks for learning and identifying repeated patterns in sequence input with various degrees of complexity. In this model, a pair of reservoir computing modules, each of which comprises a recurrent neural network and readout units, supervise each other to consistently predict others’ responses to frequently recurring segments. Interestingly, this system generates neural responses similar to those formed in the basal ganglia during habit formation. Our model extends reservoir computing to higher cognitive function and demonstrates its resemblance to sequence processing by cortico-basal ganglia loops.


2020 ◽  
Vol 44 (3) ◽  
pp. 241-249
Author(s):  
Yoshiaki Omura

While a visiting Professor at the University of Paris, VI (formerly Sorvonne) more than 40 years ago, the Author became very good friends with Dr. Paul Nogier who periodically gave seminars and workshops in Paris. After the author diagnosed his cervical problem & offered him simple help, Dr. Nogier asked the Author to present lectures and demonstrations on the effects of ear stimulation, namely the effects of acupuncture & electrical stimulation of the ear lobules. It is only now, in 2019 that we have discovered 2–5 minute high frequency stimulation of the ear lobule inhibits cancer activity for 1– 4 hours post stimulation. Although the procedure is extremely simple. First take optimal dose of Vitamin D3, which has the most essential 10 unique beneficial factors required for every human cell activity. Next, apply high frequency stimulation to ear lobule while the worst ear lobule is held by all fingers with vibrator directly touching the surface of the worst ear lobule, preferably after patient repeatedly takes optimal dose of Vitamin D3. When the worst ear lobule is held between thumb & index fingers and applying mechanical stimulation of 250 ~ 500 mechanical vibration/second for 2 ~ 5 minutes using an electrical vibrator, there is rapid disappearance of cancer activity in both the brain and rest of the body for short time duration 1 ~ 4 hours. The effect often increases by additional pressure by holding fingers. As of May 2019, the Author found that many people from various regions of the world developed early stages of multiple cancers. For evaluation of this study, U. S. patented Bi-Digital O-Ring Test (BDORT) was used which was developed by the Author while doing his Graduate experimental physics research at Colombia University. BDORT was found to be most essential for determining the beneficial effects as well as harmful effects of any substance or treatment. Using BDORT, Author was the first to recognize severe increasing mid-backache was an early sign of pancreatic cancer of President of New York State Board of Medicine after top pain specialists failed to detect the cause after 3 years of effort, while the BDORT showed early stages of cancer whereas conventional X-Ray of the pancreas did not show any cancer image until 2 months after Author detected with BDORT. For example, the optimal dose of the banana is usually about 2.0 - 2.5 millimeters cross section of the banana. A whole banana is more than 50 ~ 100 times the optimal dose. Any substance eaten in more than 25 times of its optimal dose becomes highly toxic and creates DNA mutations which can cause multiple malignancies in the presence of strong electro-magnetic field. With standard medication given by doctor, patients often become sick and they are unable to reduce body weight, unless medication is reduced or completely stopped. When the amount of zinc is very high, DNA often becomes unstable and multiple cancers can grow rapidly in the presence of strong electromagnetic field. Large amount of Vitamin C from regular orange or orange juice inhibit the most important Vitamin D3 effects. At least 3 kinds of low Vitamin C oranges will not inhibit Vitamin D3. Since B12 particularly methyl cobalamin which is a red small tablet is known to improve brain circulation very significantly we examined its effect within 20 seconds of oral intake we found the following very significant changes. Acetylcholine in both sides of the brain often increases over 4,500 ng. Longevity gene Sirtuin 1 level increases significantly for short time of few hours. Thymosin α1 and Thymosinβ4 both increase to over 1500 ng from 20 ng or less.


Author(s):  
Riitta Salmelin ◽  
Jan Kujala ◽  
Mia Liljeström

When seeking to uncover the brain correlates of language processing, timing and location are of the essence. Magnetoencephalography (MEG) offers them both, with the highest sensitivity to cortical activity. MEG has shown its worth in revealing cortical dynamics of reading, speech perception, and speech production in adults and children, in unimpaired language processing as well as developmental and acquired language disorders. The MEG signals, once recorded, provide an extensive selection of measures for examination of neural processing. Like all other neuroimaging tools, MEG has its own strengths and limitations of which the user should be aware in order to make the best possible use of this powerful method and to generate meaningful and reliable scientific data. This chapter reviews MEG methodology and how MEG has been used to study the cortical dynamics of language.


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