scholarly journals Couching the brain's multiple-demand and language-specific systems within a macroscale gradient of cortical connectivity

2021 ◽  
Author(s):  
Rocco Chiou ◽  
Elizabeth Jefferies ◽  
John Duncan ◽  
Gina F. Humphreys ◽  
Matthew Lambon Ralph
Keyword(s):  
Information Processing ◽  
Control System ◽  
Semantic Processing ◽  
Cortical Connectivity ◽  
Functional Activities ◽  
Cognitive Operations ◽  
Reduced Gradient ◽  
Novel Approaches ◽  
Functional Profiles ◽  
The Brain

The cerebrum comprises a set of specialised systems that tile across the cortical sheet, forming a tapestry-like configuration. For example, the multiple-demand and language-specific systems occupy largely separate neural estates and exhibit disparate functional profiles. Although delimiting the boundary between systems informs where cortical sheet functionally fractionates, it remains unclear why different systems' topographical placements are spatially configured in typical manners and how a macroscale architecture arises from this topography. Novel approaches have tackled this challenge by condensing the topography into a principal gradient, which represents the workflow of information processing from sensory-motoric to abstract-cognitive. To understand how the multiple-demand and language-specific systems are accommodated in the gradient framework, here we used fMRI to probe cognitive operations in semantic vs. visuospatial domains and projected functional activities onto the principal gradient. We found that the two systems showed distinct trajectories of distribution along gradient tiers, suggesting different roles in the transition from sensation to cognition. Critically, when semantic processing became difficult, the brain recruited a specialised 'semantic-control' system that was a functional and anatomical 'hybrid' juxtaposed between the multi-demand and language systems. We discuss how the brain's modular division can be better understood through the lens of a dimensionality-reduced gradient-like architecture.

Event-Related Potentials in Psychiatry: Approaches to Research and Clinical Applications

1983 ◽  
Vol 17 (4) ◽  
pp. 307-318 ◽  
Author(s):  
H. G. Stampfer
Keyword(s):  
Information Processing ◽  
Psychiatric Disorders ◽  
Rapid Development ◽  
Event Related Potentials ◽  
Clinical Applications ◽  
Direct Access ◽  
Practical Application ◽  
Clinical Methods ◽  
Related Potentials ◽  
The Brain

This article suggests that the potential usefulness of event-related potentials in psychiatry has not been fully explored because of the limitations of various approaches to research adopted to date, and because the field is still undergoing rapid development. Newer approaches to data acquisition and methods of analysis, combined with closer co-operation between medical and physical scientists, will help to establish the practical application of these signals in psychiatric disorders and assist our understanding of psychophysiological information processing in the brain. Finally, it is suggested that psychiatrists should seek to understand these techniques and the data they generate, since they provide more direct access to measures of complex cerebral processes than current clinical methods.

Coding of Temporally Varying Signals in Networks of Spiking Neurons with Global Delayed Feedback

2005 ◽  
Vol 17 (10) ◽  
pp. 2139-2175 ◽  
Author(s):  
Naoki Masuda ◽  
Brent Doiron ◽  
André Longtin ◽  
Kazuyuki Aihara
Keyword(s):  
Information Processing ◽  
Stimulus Frequency ◽  
Spiking Neurons ◽  
Feedforward Networks ◽  
Dynamical Characteristics ◽  
Neural Activities ◽  
Inherent Frequency ◽  
Types Of Information ◽  
The Brain

Oscillatory and synchronized neural activities are commonly found in the brain, and evidence suggests that many of them are caused by global feedback. Their mechanisms and roles in information processing have been discussed often using purely feedforward networks or recurrent networks with constant inputs. On the other hand, real recurrent neural networks are abundant and continually receive information-rich inputs from the outside environment or other parts of the brain. We examine how feedforward networks of spiking neurons with delayed global feedback process information about temporally changing inputs. We show that the network behavior is more synchronous as well as more correlated with and phase-locked to the stimulus when the stimulus frequency is resonant with the inherent frequency of the neuron or that of the network oscillation generated by the feedback architecture. The two eigenmodes have distinct dynamical characteristics, which are supported by numerical simulations and by analytical arguments based on frequency response and bifurcation theory. This distinction is similar to the class I versus class II classification of single neurons according to the bifurcation from quiescence to periodic firing, and the two modes depend differently on system parameters. These two mechanisms may be associated with different types of information processing.

scholarly journals Context-Based Filtering for Assisted Brain-Actuated Wheelchair Driving

2007 ◽  
Vol 2007 ◽  
pp. 1-12 ◽  
Author(s):  
Gerolf Vanacker ◽  
José del R. Millán ◽  
Eileen Lew ◽  
Pierre W. Ferrez ◽  
Ferran Galán Moles ◽  
...  
Keyword(s):  
Control System ◽  
Shared Control ◽  
Eeg Signals ◽  
Intelligent Processing ◽  
Intelligent Wheelchair ◽  
Brain Signals ◽  
Brain Interface ◽  
The Subject ◽  
Electroencephalogram Eeg ◽  
The Brain

Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased. This paper introduces a shared control system that helps the subject in driving an intelligent wheelchair with a noninvasive brain interface. The subject's steering intentions are estimated from electroencephalogram (EEG) signals and passed through to the shared control system before being sent to the wheelchair motors. Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it. These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.

scholarly journals Management of Mathematics Anxiety through Behaviour Modification, Super brain Yoga and JPMR in Ninth Standard Student

2017 ◽  
Vol 4 (2) ◽  
Author(s):  
Dr. Rajesh Ganesan ◽  
Pankaj Singh
Keyword(s):  
Information Processing ◽  
Mathematics Anxiety ◽  
Math Anxiety ◽  
Rating Scale ◽  
Emotional Reactions ◽  
Progressive Muscle Relaxation ◽  
Hemispheric Dominance ◽  
Human Information Processing ◽  
Behaviour Modification ◽  
The Brain

Mathematics Anxiety is an irrational fear of Mathematics. Mathematics Anxiety is defined as “the presence of a syndrome of emotional reactions to arithmetic and mathematics” (Dreger & Aiken, 1957, p.344). It creates a feeling of tension, apprehension, or fear that interferes with performance in Mathematics and also results in ‘Mathematics-Avoidance’. Further, ‘Mathematics-Avoidance’ leads to less competency, exposure and practice of Mathematics, leaving students more anxious and mathematically, unprepared to achieve. Math anxiety is a learned response that inhibits cognitive performance in the math classroom. It is widespread among students from elementary age through college. Students suffering from math anxiety have difficulty performing calculations and maintaining a positive outlook on mathematics. Math anxiety is the result of a cycle of math avoidance that begins with negative experiences regarding mathematics. These students avoid Mathematic courses and tend to feel negative towards Mathematics and this also affects student’s overall confidence level. However, Behaviour Modification techniques have proven instruments that can reduce various types of anxieties and Super Brain Yoga for improving integration of the brain. This is a case study of a student of IX standard, Kendriya Vidalaya, Who was referred by his Mathematics teacher and parent complaining that the student becomes anxious whenever he encounters Mathematic problems. After taking Math autobiography it was revealed that the anxiety began due to harsh handling by father while teaching Mathematics. Students score in recent Mathematic exam was noted very low i.e 12/40. His Mathematics Anxiety was assessed by using Suri, Monroe and Koc’s (2012) short Mathematics Anxiety Rating Scale. Student’s hemispheric dominance of the brain was measured by using Taggart and Torrance’s Human Information Processing Survey (1984). This student was treated with Behaviour Modification techniques and Super Brain Yoga for six weeks. Interventions used are: (i) Reduction of Rate of Breathing (Ganesan, 2012). (ii) Jacobson Progressive Muscle Relaxation (Jacobson, 1938) (iii) Laughter Technique (Ganesan, 2008b). (iv) Develpoment of Alternate Emotional Responses to the Threatening Stimulus (Ganesan, 2008a). (v) Super Brain Yoga (Sui, 2005). The anxiety level and performance in Mathematics exam was reassessed after six weeks. Results showed that Mathematics Anxiety was significantly reduced (60 to 20, 40%) and he performed better in the Mathematics exam (12/40 to 24/40, 30%). After reassessing student on Human Information Processing Survey by Taggart and Torrance (1984), it was found that student’s dominant information processing mode was ‘Integrated’ and this shows that Behaviour Modification techniques and Super Brain Yoga are efficient in treating Mathematics Anxiety.

Capacity of a Single Spiking Neuron Channel

2009 ◽  
Vol 21 (6) ◽  
pp. 1714-1748 ◽  
Author(s):  
Shiro Ikeda ◽  
Jonathan H. Manton
Keyword(s):  
Information Processing ◽  
Channel Capacity ◽  
Information Transfer ◽  
Communication Channel ◽  
Discrete Distribution ◽  
Random Variable ◽  
Temporal Coding ◽  
Reasonable Assumption ◽  
Rate Coding ◽  
The Brain

Information transfer through a single neuron is a fundamental component of information processing in the brain, and computing the information channel capacity is important to understand this information processing. The problem is difficult since the capacity depends on coding, characteristics of the communication channel, and optimization over input distributions, among other issues. In this letter, we consider two models. The temporal coding model of a neuron as a communication channel assumes the output is τ where τ is a gamma-distributed random variable corresponding to the interspike interval, that is, the time it takes for the neuron to fire once. The rate coding model is similar; the output is the actual rate of firing over a fixed period of time. Theoretical studies prove that the distribution of inputs, which achieves channel capacity, is a discrete distribution with finite mass points for temporal and rate coding under a reasonable assumption. This allows us to compute numerically the capacity of a neuron. Numerical results are in a plausible range based on biological evidence to date.

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