scholarly journals Information Processing in the Brain as Optimal Entropy Transport: A Theoretical Approach

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1231
Author(s):  
Carlos Islas ◽  
Pablo Padilla ◽  
Marco Antonio Prado
Keyword(s):  
Information Processing ◽  
Information Flow ◽  
Optimal Transport ◽  
Brain Activity ◽  
Entropy Condition ◽  
Informational Entropy ◽  
Information Theoretic ◽  
Entropy Transport ◽  
Monge Ampere ◽  
The Brain

We consider brain activity from an information theoretic perspective. We analyze the information processing in the brain, considering the optimality of Shannon entropy transport using the Monge–Kantorovich framework. It is proposed that some of these processes satisfy an optimal transport of informational entropy condition. This optimality condition allows us to derive an equation of the Monge–Ampère type for the information flow that accounts for the branching structure of neurons via the linearization of this equation. Based on this fact, we discuss a version of Murray’s law in this context.

Information Processing in the Mental Workspace Is Fundamentally Distributed

2016 ◽  
Vol 28 (2) ◽  
pp. 295-307 ◽  
Author(s):  
Alexander Schlegel ◽  
Prescott Alexander ◽  
Peter U. Tse
Keyword(s):  
Information Processing ◽  
Information Flow ◽  
The Novel ◽  
Neural Basis ◽  
Distributed Information ◽  
Network Support ◽  
Component Processes ◽  
High Level ◽  
Flow Of Information ◽  
The Brain

The brain is a complex, interconnected information processing network. In humans, this network supports a mental workspace that enables high-level abilities such as scientific and artistic creativity. Do the component processes underlying these abilities occur in discrete anatomical modules, or are they distributed widely throughout the brain? How does the flow of information within this network support specific cognitive functions? Current approaches have limited ability to answer such questions. Here, we report novel multivariate methods to analyze information flow within the mental workspace during visual imagery manipulation. We find that mental imagery entails distributed information flow and shared representations throughout the cortex. These findings challenge existing, anatomically modular models of the neural basis of higher-order mental functions, suggesting that such processes may occur at least in part at a fundamentally distributed level of organization. The novel methods we report may be useful in studying other similarly complex, high-level informational processes.

scholarly journals Macroscopic quantities of collective brain activity during wakefulness and anesthesia

2021 ◽  
Author(s):  
Adrián Ponce-Alvarez ◽  
Lynn Uhrig ◽  
Nikolas Deco ◽  
Camilo M. Signorelli ◽  
Morten L. Kringelbach ◽  
...  
Keyword(s):  
Brain Activity ◽  
Brain Regions ◽  
Brain State ◽  
Loss Of Consciousness ◽  
Information Theoretic ◽  
Maximum Entropy Models ◽  
Information Theoretic Measures ◽  
Brain States ◽  
Parietal Cortices ◽  
The Brain

AbstractThe study of states of arousal is key to understand the principles of consciousness. Yet, how different brain states emerge from the collective activity of brain regions remains unknown. Here, we studied the fMRI brain activity of monkeys during wakefulness and anesthesia-induced loss of consciousness. Using maximum entropy models, we derived collective, macroscopic properties that quantify the system’s capabilities to produce work, to contain information and to transmit it, and that indicate a phase transition from critical awake dynamics to supercritical anesthetized states. Moreover, information-theoretic measures identified those parameters that impacted the most the network dynamics. We found that changes in brain state and in state of consciousness primarily depended on changes in network couplings of insular, cingulate, and parietal cortices. Our findings suggest that the brain state transition underlying the loss of consciousness is predominantly driven by the uncoupling of specific brain regions from the rest of the network.

scholarly journals Linking Models with Brain Measures

2020 ◽  
Author(s):  
Bradley C. Love
Keyword(s):  
Information Flow ◽  
Cognitive Processes ◽  
Brain Activity ◽  
Cognitive Model ◽  
Brain Regions ◽  
Learning Models ◽  
Model Based ◽  
Theoretical Significance ◽  
Brain Data ◽  
The Brain

Linking models and brain measures offers a number of advantages over standard analyses. Models that have been evaluated on previous datasets can provide theoretical constraints and assist in integrating findings across studies. Model-based analyses can be more sensitive and allow for evaluation of hypotheses that would not otherwise be addressable. For example, a cognitive model that is informed from several behavioural studies could be used to examine how multiple cognitive processes unfold across time in the brain. Models can be linked to brain measures in a number of ways. The information flow and constraints can be from model to brain, brain to model, or reciprocal. Likewise, the linkage from model and brain can be univariate or multivariate, as in studies that relate patterns of brain activity with model states. Models have multiple aspects that can be related to different facets of brain activity. This is well illustrated by deep learning models that have multiple layers or representations that can be aligned with different brain regions. Model-based approaches offer a lens on brain data that is complementary to popular multivariate decoding and representational similarity analysis approaches. Indeed, these approaches can realise greater theoretical significance when situated within a model-based approach.

scholarly journals The brain-bases of responsiveness variability under moderate anaesthesia

2020 ◽  
Author(s):  
Feng Deng ◽  
Nicola Taylor ◽  
Adrian M. Owen ◽  
Rhodri Cusack ◽  
Lorina Naci
Keyword(s):  
Information Processing ◽  
Executive Control ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Reaction Times ◽  
Grey Matter Volume ◽  
Clinical Anaesthesia ◽  
Powerful Approach ◽  
First Time ◽  
The Brain

AbstractAnaesthesia combined with functional neuroimaging provides a powerful approach for understanding the brain mechanisms that change as consciousness fades. Although propofol is used ubiquitously in clinical interventions that reversibly suppress consciousness, its effect varies substantially between individuals, and the brain bases of this variability remain poorly understood. We asked whether three networks that are primary sites of propofol-induced sedation and key to conscious cognition — the dorsal attention (DAN), executive control (ECN), and default mode (DMN) network — underlie responsiveness variability under anaesthesia. Healthy participants (N=17) underwent propofol sedation inside the fMRI scanner at dosages of ‘moderate’ anaesthesia, and behavioural responsiveness was measured with a target detection task. To assess information processing, participants were scanned during an active engagement condition comprised of a suspenseful auditory narrative, in addition to the resting state. A behavioural investigation in a second group of non-anesthetized participants (N=25) qualified the attention demands of narrative understanding, which we then related to the brain activity of participants who underwent sedation. 30% of participants showed no delay in reaction times relative to wakefulness, whereas the others, showed significantly delayed and fragmented responses, or full omission of responses. These responsiveness differences did not relate to information processing differences. Rather, only the functional connectivity within the ECN during wakefulness differentiated the participants’ responsiveness level, with significantly stronger connectivity in the fast relative to slow responders. Consistent with this finding, fast responders had significantly higher grey matter volume in the frontal cortex aspect of the ECN. For the first time, these results show that responsiveness variability during propofol anaesthesia relates to inherent differences in brain function and structure within the executive control network, which can be predicted prior to sedation. These results shed light on the brain bases of responsiveness differences and highlight novel markers that may help to improve the accuracy of awareness monitoring during clinical anaesthesia.

scholarly journals Information dynamics in neuromorphic nanowire networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruomin Zhu ◽  
Joel Hochstetter ◽  
Alon Loeffler ◽  
Adrian Diaz-Alvarez ◽  
Tomonobu Nakayama ◽  
...  
Keyword(s):  
Information Processing ◽  
Information Flow ◽  
Short Term Memory ◽  
Information Storage ◽  
Information Dynamics ◽  
Information Theoretic ◽  
Neuromorphic Systems ◽  
Processing Resources ◽  
Active Information Storage ◽  
Nanowire Networks

AbstractNeuromorphic systems comprised of self-assembled nanowires exhibit a range of neural-like dynamics arising from the interplay of their synapse-like electrical junctions and their complex network topology. Additionally, various information processing tasks have been demonstrated with neuromorphic nanowire networks. Here, we investigate the dynamics of how these unique systems process information through information-theoretic metrics. In particular, Transfer Entropy (TE) and Active Information Storage (AIS) are employed to investigate dynamical information flow and short-term memory in nanowire networks. In addition to finding that the topologically central parts of networks contribute the most to the information flow, our results also reveal TE and AIS are maximized when the networks transitions from a quiescent to an active state. The performance of neuromorphic networks in memory and learning tasks is demonstrated to be dependent on their internal dynamical states as well as topological structure. Optimal performance is found when these networks are pre-initialised to the transition state where TE and AIS are maximal. Furthermore, an optimal range of information processing resources (i.e. connectivity density) is identified for performance. Overall, our results demonstrate information dynamics is a valuable tool to study and benchmark neuromorphic systems.

scholarly journals Functional determinants of enhanced and depressed inter-areal information flow in NREM sleep between neuronal ensembles in rat cortex and hippocampus

2018 ◽  
Author(s):  
Umberto Olcese ◽  
Jeroen J. Bos ◽  
Martin Vinck ◽  
Cyriel M.A. Pennartz
Keyword(s):  
Information Processing ◽  
Time Scales ◽  
Information Flow ◽  
Rem Sleep ◽  
Transfer Entropy ◽  
Behavioral Task ◽  
Related Information ◽  
Long Time ◽  
The Brain ◽  
Spiking Activity

AbstractCompared to wakefulness, neuronal activity during non-REM sleep is characterized by a decreased ability to integrate information, but also by the re-emergence of task-related information patterns. To investigate the mechanisms underlying these seemingly opposing phenomena, we measured directed information flow by computing transfer entropy between neuronal spiking activity in three cortical regions and the hippocampus of rats across brain states. State-dependent information flow resulted to be jointly determined by the anatomical distance between neurons and by their functional specialization. We distinguished two regimes, operating at short and long time scales, respectively. From wakefulness to non-REM sleep, transfer entropy at short time scales increased for inter-areal connections between neurons showing behavioral task correlates. Conversely, transfer entropy at long time scales became stronger between non-task modulated neurons and weaker between task- modulated neurons. These results may explain how, during non-REM sleep, a global inter-areal disconnection is compatible with highly specific task-related information transfer.Author SummaryThe brain remains active during deep sleep, yet we still do not know which rules govern information processing between neurons across wakefulness and sleep. Here we provide a first study of how information flow at the level of spiking activity varies as a function of brain state, temporal scale, brain area and behavioral task correlates of single neurons. We found that inter-areal communication at millisecond time scales is enhanced during sleep compared to wakefulness between neurons that code for task information. Conversely, non-modulated neurons showed more prominent communication at longer time scales. These results indicate that multiple, functionally determined communicative architectures coexist in the brain, and provide a novel framework to understand information processing and its consequences during sleep.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

Relation Between Level of Prefrontal Activity and Subject's Performance

1999 ◽  
Vol 13 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Laurence Casini ◽  
Françoise Macar ◽  
Marie-Hélène Giard
Keyword(s):  
Brain Activity ◽  
Sensory Information ◽  
Practice Phase ◽  
Trained Subjects ◽  
Anagram Task ◽  
Economic Information ◽  
Long Duration ◽  
Level Of Activity ◽  
The Brain ◽  
Processing Mechanism

Abstract The experiment reported here was aimed at determining whether the level of brain activity can be related to performance in trained subjects. Two tasks were compared: a temporal and a linguistic task. An array of four letters appeared on a screen. In the temporal task, subjects had to decide whether the letters remained on the screen for a short or a long duration as learned in a practice phase. In the linguistic task, they had to determine whether the four letters could form a word or not (anagram task). These tasks allowed us to compare the level of brain activity obtained in correct and incorrect responses. The current density measures recorded over prefrontal areas showed a relationship between the performance and the level of activity in the temporal task only. The level of activity obtained with correct responses was lower than that obtained with incorrect responses. This suggests that a good temporal performance could be the result of an efficacious, but economic, information-processing mechanism in the brain. In addition, the absence of this relation in the anagram task results in the question of whether this relation is specific to the processing of sensory information only.

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