Identifying mutual information transfer in the brain with differential-algebraic modeling: Evidence for fast oscillatory coupling between cortical somatosensory areas 3b and 1

Information transfer in the brain relies upon energetically expensive spiking activity of neurons. Rates of information flow should therefore be carefully optimized, but mechanisms to control this parameter are poorly understood. We address this deficit in the visual system, where ambient light (irradiance) is predictive of the amount of information reaching the eye and ask whether a neural measure of irradiance can therefore be used to proactively control information flow along the optic nerve. We first show that firing rates for the retina’s output neurons [retinal ganglion cells (RGCs)] scale with irradiance and are positively correlated with rates of information and the gain of visual responses. Irradiance modulates firing in the absence of any other visual signal confirming that this is a genuine response to changing ambient light. Irradiance-driven changes in firing are observed across the population of RGCs (including in both ON and OFF units) but are disrupted in mice lacking melanopsin [the photopigment of irradiance-coding intrinsically photosensitive RGCs (ipRGCs)] and can be induced under steady light exposure by chemogenetic activation of ipRGCs. Artificially elevating firing by chemogenetic excitation of ipRGCs is sufficient to increase information flow by increasing the gain of visual responses, indicating that enhanced firing is a cause of increased information transfer at higher irradiance. Our results establish a retinal circuitry driving changes in RGC firing as an active response to alterations in ambient light to adjust the amount of visual information transmitted to the brain.

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Capacity of a Single Spiking Neuron Channel

Neural Computation ◽

10.1162/neco.2009.05-08-792 ◽

2009 ◽

Vol 21 (6) ◽

pp. 1714-1748 ◽

Cited By ~ 32

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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Decomposing information into copying versus transformation

Journal of The Royal Society Interface ◽

10.1098/rsif.2019.0623 ◽

2020 ◽

Vol 17 (162) ◽

pp. 20190623 ◽

Cited By ~ 1

Author(s):

Artemy Kolchinsky ◽

Bernat Corominas-Murtra

Keyword(s):

Amino Acid ◽

Mutual Information ◽

Information Transfer ◽

World Systems ◽

Information Theoretic ◽

Versus Transformation ◽

Information Theoretic Measures ◽

Functional Consequences ◽

Standard Information ◽

Minimal Work

In many real-world systems, information can be transmitted in two qualitatively different ways: by copying or by transformation . Copying occurs when messages are transmitted without modification, e.g. when an offspring receives an unaltered copy of a gene from its parent. Transformation occurs when messages are modified systematically during transmission, e.g. when mutational biases occur during genetic replication. Standard information-theoretic measures do not distinguish these two modes of information transfer, although they may reflect different mechanisms and have different functional consequences. Starting from a few simple axioms, we derive a decomposition of mutual information into the information transmitted by copying versus the information transmitted by transformation. We begin with a decomposition that applies when the source and destination of the channel have the same set of messages and a notion of message identity exists. We then generalize our decomposition to other kinds of channels, which can involve different source and destination sets and broader notions of similarity. In addition, we show that copy information can be interpreted as the minimal work needed by a physical copying process, which is relevant for understanding the physics of replication. We use the proposed decomposition to explore a model of amino acid substitution rates. Our results apply to any system in which the fidelity of copying, rather than simple predictability, is of critical relevance.

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Brain Network Modeling Based on Mutual Information and Graph Theory for Predicting the Connection Mechanism in the Progression of Alzheimer’s Disease

Entropy ◽

10.3390/e21030300 ◽

2019 ◽

Vol 21 (3) ◽

pp. 300 ◽

Cited By ~ 4

Author(s):

Shuaizong Si ◽

Bin Wang ◽

Xiao Liu ◽

Chong Yu ◽

Chao Ding ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Graph Theory ◽

Mutual Information ◽

Brain Networks ◽

Brain Network ◽

Network Efficiency ◽

Characteristic Path Length ◽

Anatomical Space ◽

The Brain

Alzheimer’s disease (AD) is a progressive disease that causes problems of cognitive and memory functions decline. Patients with AD usually lose their ability to manage their daily life. Exploring the progression of the brain from normal controls (NC) to AD is an essential part of human research. Although connection changes have been found in the progression, the connection mechanism that drives these changes remains incompletely understood. The purpose of this study is to explore the connection changes in brain networks in the process from NC to AD, and uncovers the underlying connection mechanism that shapes the topologies of AD brain networks. In particular, we propose a mutual information brain network model (MINM) from the perspective of graph theory to achieve our aim. MINM concerns the question of estimating the connection probability between two cortical regions with the consideration of both the mutual information of their observed network topologies and their Euclidean distance in anatomical space. In addition, MINM considers establishing and deleting connections, simultaneously, during the networks modeling from the stage of NC to AD. Experiments show that MINM is sufficient to capture an impressive range of topological properties of real brain networks such as characteristic path length, network efficiency, and transitivity, and it also provides an excellent fit to the real brain networks in degree distribution compared to experiential models. Thus, we anticipate that MINM may explain the connection mechanism for the formation of the brain network organization in AD patients.

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EEG TRANSFER ENTROPY TRACKS CHANGES IN INFORMATION TRANSFER ON THE ONSET OF VISION

International Journal of Modern Physics Conference Series ◽

10.1142/s201019451200788x ◽

2012 ◽

Vol 17 ◽

pp. 9-18 ◽

Cited By ~ 1

Author(s):

M. D. MADULARA ◽

P. A. B. FRANCISCO ◽

S. NAWANG ◽

D. C. AROGANCIA ◽

C. J. CELLUCCI ◽

...

Keyword(s):

Mutual Information ◽

Information Transfer ◽

Occipital Lobe ◽

Transfer Entropy ◽

The Other ◽

Information Measures ◽

Eyes Closed ◽

Eyes Open ◽

Almost All ◽

Nonlinear Correlations

We investigate the pairwise mutual information and transfer entropy of ten-channel, free-running electroencephalographs measured from thirteen subjects under two behavioral conditions: eyes open resting and eyes closed resting. Mutual information measures nonlinear correlations; transfer entropy determines the directionality of information transfer. For all channel pairs, mutual information is generally lower with eyes open compared to eyes closed indicating that EEG signals at different scalp sites become more dissimilar as the visual system is engaged. On the other hand, transfer entropy increases on average by almost two-fold when the eyes are opened. The largest one-way transfer entropies are to and from the Oz site consistent with the involvement of the occipital lobe in vision. The largest net transfer entropies are from F3 and F4 to almost all the other scalp sites.

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Stochastic, structural and functional factors influencing AMPA and NMDA synaptic response variability: a review

Neuronal Signaling ◽

10.1042/ns20160051 ◽

2017 ◽

Vol 1 (3) ◽

Cited By ~ 3

Author(s):

Vito Di Maio ◽

Francesco Ventriglia ◽

Silvia Santillo

Keyword(s):

Nervous System ◽

Synaptic Transmission ◽

Learning And Memory ◽

Information Transfer ◽

Basic Mechanism ◽

Response Variability ◽

Synaptic Response ◽

Factors Influencing ◽

Functional Factors ◽

The Brain

Synaptic transmission is the basic mechanism of information transfer between neurons not only in the brain, but along all the nervous system. In this review we will briefly summarize some of the main parameters that produce stochastic variability in the synaptic response. This variability produces different effects on important brain phenomena, like learning and memory, and, alterations of its basic factors can cause brain malfunctioning.

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Conditional Mutual Information Maps as Descriptors of Net Connectivity Levels in the Brain

Frontiers in Neuroinformatics ◽

10.3389/fninf.2010.00115 ◽

2010 ◽

Vol 4 ◽

Cited By ~ 23

Author(s):

Raymond Salvador ◽

Maria Anguera ◽

Jesús J. Gomar ◽

Edward T. Bullmore ◽

Edith Pomarol-Clotet

Keyword(s):

Mutual Information ◽

Conditional Mutual Information ◽

The Brain

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The Use of a Brain Computer Interface Remote Control to Navigate a Recreational Device

Mathematical Problems in Engineering ◽

10.1155/2013/823736 ◽

2013 ◽

Vol 2013 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Shih Chung Chen ◽

Aaron Raymond See ◽

Yeou Jiunn Chen ◽

Chia Hong Yeng ◽

Chih Kuo Liang

Keyword(s):

Remote Control ◽

Information Transfer ◽

Brain Computer Interface ◽

Computer Interface ◽

Brain Response ◽

Brain Signals ◽

Information Transfer Rate ◽

Cord Injury ◽

Remote Controller ◽

The Brain

People suffering from paralysis caused by serious neural disorder or spinal cord injury also need to be given a means of recreation other than general living aids. Although there have been a proliferation of brain computer interface (BCI) applications, developments for recreational activities are scarcely seen. The objective of this study is to develop a BCI-based remote control integrated with commercial devices such as the remote controlled Air Swimmer. The brain is visually stimulated using boxes flickering at preprogrammed frequencies to activate a brain response. After acquiring and processing these brain signals, the frequency of the resulting peak, which corresponds to the user’s selection, is determined by a decision model. Consequently, a command signal is sent from the computer to the wireless remote controller via a data acquisition (DAQ) module. A command selection training (CST) and simulated path test (SPT) were conducted by 12 subjects using the BCI control system and the experimental results showed a recognition accuracy rate of 89.51% and 92.31% for the CST and SPT, respectively. The fastest information transfer rate demonstrated a response of 105 bits/min and 41.79 bits/min for the CST and SPT, respectively. The BCI system was proven to be able to provide a fast and accurate response for a remote controller application.

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Alzheimer’s disease as the cerebral cortex disorder

Science and Innovations in Medicine ◽

10.35693/2500-1388-2019-4-2-16-20 ◽

2019 ◽

Vol 4 (2) ◽

pp. 16-20

Author(s):

Volobuev AN ◽

Romanchuk PI ◽

Bulgakova SV

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Information Transfer ◽

Brain Cortex ◽

Senile Dementia ◽

Memory Storage ◽

Neuronal Circuits ◽

Storage Element ◽

Dementia Of Alzheimer’S Type ◽

The Brain

Objectives – to highlight the structure, function and localization of Alzheimer’s disease and to specify cognitive impairments related to it. Material and methods. The anatomic data of human brain structure were used. Results. The patterns of memory formation in the brain cortex are investigated. The brain cortex is presented as a type of syncytium consisting of elementary neural structures – cyclic neuronal circuits – memory elements. All cyclic neuronal circuits in a brain cortex are functionally interconnected. The connections between the neuronal circuits can be determined (imprinted) and stochastic (random). The intensity of stochastic communications defines the person's potential for creativity. The impairment of cyclic neuronal circuit connections results in either Alzheimer’s disease or in senile dementia of Alzheimer’s type. Conclusion. In case the cortex is considered as the syncytium, the memory storage element, it can be the reason of the human creativity. It is shown that the failure of the information transfer in the cortex syncytium or neurons destruction in the neuronal network results in Alzheimer’s disease or in senile dementia of Alzheimer’s type.

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