scholarly journals Serendipity hints at a non-linear thinking mechanism

2021 ◽  
Author(s):  
Tam-Tri Le
Keyword(s):  
Information Processing ◽  
Non Linear ◽  
Natural Mechanism ◽  
The Brain

The structure of serendipity suggest simultaneous multidimensional processing. I argue that non-linear thinking is a natural mechanism of information processing in the brain, regardless of the degree of nonlinearity deemed by the observer.

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 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.

scholarly journals Dendrites enable a robust mechanism for neuronal stimulus selectivity

2015 ◽  
Author(s):  
Romain D. Cazé ◽  
Sarah Jarvis ◽  
Amanda J. Foust ◽  
Simon R. Schultz
Keyword(s):  
Spatial Distribution ◽  
Information Processing ◽  
Linear Model ◽  
Cortical Neurons ◽  
General Mechanism ◽  
Starting Point ◽  
Dendritic Processing ◽  
Non Linear ◽  
Equivalent Linear Model ◽  
Preferred Stimulus

AbstractHearing, vision, touch-underlying all of these senses is stimulus selectivity, a robust information processing operation in which cortical neurons respond more to some stimuli than to others. Previous models assume that these neurons receive the highest weighted input from an ensemble encoding the preferred stimulus, but dendrites enable other possibilities. Non-linear dendritic processing can produce stimulus selectivity based on the spatial distribution of synapses, even if the total preferred stimulus weight does not exceed that of non-preferred stimuli. Using a multi-subunit non-linear model, we demonstrate that stimulus selectivity can arise from the spatial distribution of synapses. We propose this as a general mechanism for information processing by neurons possessing dendritic trees. Moreover, we show that this implementation of stimulus selectivity increases the neuron's robustness to synaptic and dendritic failure. Importantly, our model can maintain stimulus selectivity for a larger range of synapses or dendrites loss than an equivalent linear model. We then use a layer 2/3 biophysical neuron model to show that our implementation is consistent with two recent experimental observations: (1) one can observe a mixture of selectivities in dendrites, that can differ from the somatic selectivity, and (2) hyperpolarization can broaden somatic tuning without affecting dendritic tuning. Our model predicts that an initially non-selective neuron can become selective when depolarized. In addition to motivating new experiments, the model's increased robustness to synapses and dendrites loss provides a starting point for fault-resistant neuromorphic chip development.

scholarly journals FMRI Functional Connectivity Evaluation in Alzheimer’s Stages: Linear and Non-Linear Approaches

2021 ◽  
Author(s):  
Hessam Ahmadi ◽  
Emad Fatemizadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Permutation Test ◽  
Pearson Correlation ◽  
Functional Integration ◽  
Clustering Coefficient ◽  
Kernel Trick ◽  
Non Linear ◽  
The Brain

Abstract Neuroimaging data analysis reveals the underlying interactions in the brain. It is essential, yet controversial, to choose a proper tool to manifest brain functional connectivity. In this regard, researchers have not reached a definitive conclusion between the linear and non-linear approaches, as both have pros and cons. In this study, to evaluate this concern, the functional Magnetic Resonance Imaging (fMRI) data of different stages of Alzheimer’s disease are investigated. In the linear approach, the Pearson Correlation Coefficient (PCC) is employed as a common technique to generate brain functional graphs. On the other hand, for non-linear approaches, two methods including Distance Correlation (DC) and the kernel trick are utilized. By the use of the three mentioned routines and graph theory, functional brain networks of all stages of Alzheimer’s disease (AD) are constructed and then sparsed. Afterwards, graph global measures are calculated over the networks and a non-parametric permutation test is conducted. Results reveal that the non-linear approaches have more potential to discriminate groups in all stages of AD. Moreover, the kernel trick method is more powerful in comparison to the DC technique. Nevertheless, AD degenerates the brain functional graphs more at the beginning stages of the disease. At the first phase, both functional integration and segregation of the brain degrades, and as AD progressed brain functional segregation further declines. The most distinguishable feature in all stages is the clustering coefficient that reflects brain functional segregation.

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.

scholarly journals Supportive or information-processing functions of the mature protoplasmic astrocyte in the mammalian CNS? A critical appraisal

2007 ◽  
Vol 3 (3) ◽  
pp. 181-189 ◽  
Author(s):  
Harold K. Kimelberg
Keyword(s):  
Information Processing ◽  
Blood Vessel ◽  
Critical Appraisal ◽  
Vessel Diameter ◽  
Synaptic Activity ◽  
Metabolic Substrates ◽  
Processing Properties ◽  
New Evidence ◽  
The Brain ◽  
Domain Concept

AbstractIt has been proposed that astrocytes should no longer be viewed purely as support cells for neurons, such as providing a constant environment and metabolic substrates, but that they should also be viewed as being involved in affecting synaptic activity in an active way and, therefore, an integral part of the information-processing properties of the brain. This essay discusses the possible differences between a support and an instructive role, and concludes that any distinction has to be blurred. In view of this, and a brief overview of the nature of the data, the new evidence seems insufficient to conclude that the physiological roles of mature astrocytes go beyond a general support role. I propose a model of mature protoplasmic astrocyte function that is drawn from the most recent data on their structure, the domain concept and their syncytial characteristics, of an independent rather than integrative functioning of the ends of each process where the activities that affect synaptic activity and blood vessel diameter will be concentrated.

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