scholarly journals Locally embedded presages of global network bursts

2017 ◽  
Vol 114 (36) ◽  
pp. 9517-9522 ◽  
Author(s):  
Satohiro Tajima ◽  
Takeshi Mita ◽  
Douglas J. Bakkum ◽  
Hirokazu Takahashi ◽  
Taro Toyoizumi
Keyword(s):  
Mean Field ◽  
Global Network ◽  
Neural Population ◽  
Reconstruction Method ◽  
Local Dynamics ◽  
Self Organized ◽  
Field Activity ◽  
Potential Applications ◽  
Network Bursts ◽  
The Brain

Spontaneous, synchronous bursting of neural population is a widely observed phenomenon in nervous networks, which is considered important for functions and dysfunctions of the brain. However, how the global synchrony across a large number of neurons emerges from an initially nonbursting network state is not fully understood. In this study, we develop a state-space reconstruction method combined with high-resolution recordings of cultured neurons. This method extracts deterministic signatures of upcoming global bursts in “local” dynamics of individual neurons during nonbursting periods. We find that local information within a single-cell time series can compare with or even outperform the global mean-field activity for predicting future global bursts. Moreover, the intercell variability in the burst predictability is found to reflect the network structure realized in the nonbursting periods. These findings suggest that deterministic local dynamics can predict seemingly stochastic global events in self-organized networks, implying the potential applications of the present methodology to detecting locally concentrated early warnings of spontaneous seizure occurrence in the brain.

scholarly journals Modules or Mean-Fields?

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 552 ◽  
Author(s):  
Thomas Parr ◽  
Noor Sajid ◽  
Karl J. Friston
Keyword(s):  
Steady State ◽  
Message Passing ◽  
Mean Field Theory ◽  
Functional Anatomy ◽  
Mean Field ◽  
State Density ◽  
Stochastic Dynamical Systems ◽  
Conditional Independency ◽  
The Brain ◽  
Non Equilibrium

The segregation of neural processing into distinct streams has been interpreted by some as evidence in favour of a modular view of brain function. This implies a set of specialised ‘modules’, each of which performs a specific kind of computation in isolation of other brain systems, before sharing the result of this operation with other modules. In light of a modern understanding of stochastic non-equilibrium systems, like the brain, a simpler and more parsimonious explanation presents itself. Formulating the evolution of a non-equilibrium steady state system in terms of its density dynamics reveals that such systems appear on average to perform a gradient ascent on their steady state density. If this steady state implies a sufficiently sparse conditional independency structure, this endorses a mean-field dynamical formulation. This decomposes the density over all states in a system into the product of marginal probabilities for those states. This factorisation lends the system a modular appearance, in the sense that we can interpret the dynamics of each factor independently. However, the argument here is that it is factorisation, as opposed to modularisation, that gives rise to the functional anatomy of the brain or, indeed, any sentient system. In the following, we briefly overview mean-field theory and its applications to stochastic dynamical systems. We then unpack the consequences of this factorisation through simple numerical simulations and highlight the implications for neuronal message passing and the computational architecture of sentience.

Thymidine kinase-mediated killing of rat brain tumors

1993 ◽  
Vol 79 (5) ◽  
pp. 729-735 ◽  
Author(s):  
David Barba ◽  
Joseph Hardin ◽  
Jasodhara Ray ◽  
Fred H. Gage
Keyword(s):  
Gene Therapy ◽  
Brain Tumors ◽  
Tumor Cells ◽  
Wild Type ◽  
Cns Disorders ◽  
Content Type ◽  
Potential Applications ◽  
Ganciclovir Treatment ◽  
The Brain ◽  
Fine Print

✓ Gene therapy has many potential applications in central nervous system (CNS) disorders, including the selective killing of tumor cells in the brain. A rat brain tumor model was used to test the herpes simplex virus (HSV)-thymidine kinase (TK) gene for its ability to selectively kill C6 and 9L tumor cells in the brain following systemic administration of the nucleoside analog ganciclovir. The HSV-TK gene was introduced in vitro into tumor cells (C6-TK and 9L-TK), then these modified tumor cells were evaluated for their sensitivity to cell killing by ganciclovir. In a dose-response assay, both C6-TK and 9L-TK cells were 100 times more sensitive to killing by ganciclovir (median lethal dose: C6-TK, 0.1 µg ganciclovir/ml; C6, 5.0 µg ganciclovir/ml) than unmodified wild-type tumor cells or cultured fibroblasts. In vivo studies confirmed the ability of intraperitoneal ganciclovir administration to kill established brain tumors in rats as quantified by both stereological assessment of brain tumor volumes and studies of animal survival over 90 days. Rats with brain tumors established by intracerebral injection of wild-type or HSV-TK modified tumor cells or by a combination of wild-type and HSV-TK-modified cells were studied with and without ganciclovir treatments. Stereological methods determined that ganciclovir treatment eliminated tumors composed of HSV-TK-modified cells while control tumors grew as expected (p < 0.001). In survival studies, all 10 rats with 9L-TK tumors treated with ganciclovir survived 90 days while all untreated rats died within 25 days. Curiously, tumors composed of combinations of 9L and 9L-TK cells could be eliminated by ganciclovir treatments even when only one-half of the tumor cells carried the HSV-TK gene. While not completely understood, this additional tumor cell killing appears to be both tumor selective and local in nature. It is concluded that HSV-TK gene therapy with ganciclovir treatment does selectively kill tumor cells in the brain and has many potential applications in CNS disorders, including the treatment of cancer.

scholarly journals PHENOMENOLOGICAL ARCHITECTURE OF A MIND AND OPERATIONAL ARCHITECTONICS OF THE BRAIN: THE UNIFIED METASTABLE CONTINUUM

2009 ◽  
Vol 05 (01) ◽  
pp. 221-244 ◽  
Author(s):  
ANDREW A. FINGELKURTS ◽  
ALEXANDER A. FINGELKURTS ◽  
CARLOS F. H. NEVES
Keyword(s):  
Distributed Computing ◽  
Brain Activity ◽  
Brain Diseases ◽  
Machine Consciousness ◽  
Self Organized ◽  
Conscious Level ◽  
The Brain ◽  
Activity Organization

In our contribution we will observe phenomenal architecture of a mind and operational architectonics of the brain and will show their intimate connectedness within a single integrated metastable continuum. The notion of operation of different complexity is the fundamental and central one in bridging the gap between brain and mind: it is precisely by means of this notion that it is possible to identify what at the same time belongs to the phenomenal conscious level and to the neurophysiological level of brain activity organization, and what mediates between them. Implications for linguistic semantics, self-organized distributed computing algorithms, artificial machine consciousness, and diagnosis of dynamic brain diseases will be discussed briefly.

Variables That Influence the Generation of Random Sequences: An Update

1997 ◽  
Vol 84 (2) ◽  
pp. 627-661 ◽  
Author(s):  
Peter Brugger
Keyword(s):  
Short Term Memory ◽  
Human Subjects ◽  
Complete Suppression ◽  
Random Generation ◽  
Short Term ◽  
Random Sequences ◽  
Potential Applications ◽  
Basic Experiments ◽  
The Brain ◽  
Relevant Work

This article updates Tune's 1964 review of variables influencing human subjects' attempts at generating random sequences of alternatives. It also covers aspects not included in the original review such as randomization behavior by patients with neurological and psychiatric disorders. Relevant work from animal research (spontaneous alternation paradigm) is considered as well. It is conjectured that Tune's explanation of sequential nonrandomness in terms of a limited capacity of short-term memory can no longer be maintained. Rather, interdependence among consecutive choices is considered a consequence of an organism's natural susceptibility to interference. Random generation is thus a complex action which demands complete suppression of any rule-governed behavior. It possibly relies on functions of the frontal lobes but cannot otherwise be “localized” to restricted regions of the brain. Possible developments in the field are briefly discussed, both with respect to basic experiments regarding the nature of behavioral nonrandomness and to potential applications of random-generation tasks.

scholarly journals BrainNetVis: An Open-Access Tool to Effectively Quantify and Visualize Brain Networks

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Eleni G. Christodoulou ◽  
Vangelis Sakkalis ◽  
Vassilis Tsiaras ◽  
Ioannis G. Tollis
Keyword(s):  
Open Access ◽  
Brain Network ◽  
Global Network ◽  
Control Group ◽  
Network Modelling ◽  
Network Information ◽  
Network Measures ◽  
Graph Metrics ◽  
Network Metrics ◽  
The Brain

This paper presents BrainNetVis, a tool which serves brain network modelling and visualization, by providing both quantitative and qualitative network measures of brain interconnectivity. It emphasizes the needs that led to the creation of this tool by presenting similar works in the field and by describing how our tool contributes to the existing scenery. It also describes the methods used for the calculation of the graph metrics (global network metrics and vertex metrics), which carry the brain network information. To make the methods clear and understandable, we use an exemplar dataset throughout the paper, on which the calculations and the visualizations are performed. This dataset consists of an alcoholic and a control group of subjects.

scholarly journals The Geometry of Information Coding in Correlated Neural Populations

2021 ◽  
Vol 44 (1) ◽  
Author(s):  
Rava Azeredo da Silveira ◽  
Fred Rieke
Keyword(s):  
Neural Coding ◽  
Neural Population ◽  
Annual Review ◽  
Publication Date ◽  
Information Coding ◽  
Population Code ◽  
New Approach ◽  
Neural Populations ◽  
The Brain ◽  
Theoretical Developments

Neurons in the brain represent information in their collective activity. The fidelity of this neural population code depends on whether and how variability in the response of one neuron is shared with other neurons. Two decades of studies have investigated the influence of these noise correlations on the properties of neural coding. We provide an overview of the theoretical developments on the topic. Using simple, qualitative, and general arguments, we discuss, categorize, and relate the various published results. We emphasize the relevance of the fine structure of noise correlation, and we present a new approach to the issue. Throughout this review, we emphasize a geometrical picture of how noise correlations impact the neural code. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Multiphase Complex Coacervate Droplets

2019 ◽  
Author(s):  
Tie-Mei Lu ◽  
Evan Spruijt
Keyword(s):  
Liquid Phase ◽  
General Feature ◽  
Mean Field ◽  
Condensed Phases ◽  
Cellular Organization ◽  
Field Interaction ◽  
Guest Molecules ◽  
Self Organized ◽  
Complex Coacervate ◽  
Liquid Liquid Phase Separation

<div>Liquid-liquid phase separation plays an important role in cellular organization. Many subcellular condensed bodies are hierarchically organized into multiple coexisting domains or layers. However, our molecular understanding of the assembly and internal organization of these multicomponent droplets is still incomplete, and rules for the coexistence of condensed phases are lacking. Here, we show that the formation of hierarchically organized multiphase droplets with up to three coexisting layers is a generic phenomenon in mixtures of complex coacervates, which serve as models of charge-driven liquid-liquid phase separated systems. We present simple theoretical guidelines to explain both the emergence and stability of multiphase droplets using the interfacial tension and mean-field interaction parameter as inputs. Coexistence implies differences in macromolecular density, which can be inferred from critical salt concentrations. We show that the coexisting coacervates present distinct chemical environments by concentrating guest molecules in different domains of the multiphase droplets. Our findings suggest that condensate immiscibility may be a very general feature in biological systems, which could be exploited to design self-organized synthetic compartments to control biomolecular processes.</div>

scholarly journals Cortical state transitions and stimulus response evolve along stiff and sloppy parameter dimensions, respectively

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Adrian Ponce-Alvarez ◽  
Gabriela Mochol ◽  
Ainhoa Hermoso-Mendizabal ◽  
Jaime de la Rocha ◽  
Gustavo Deco
Keyword(s):  
Collective Behavior ◽  
Neural Population ◽  
State Transitions ◽  
Population Activity ◽  
Sensory Inputs ◽  
Stimulus Response ◽  
Spontaneous Neuronal Activity ◽  
The Brain ◽  
Shed Light ◽  
Neural Population Activity

Previous research showed that spontaneous neuronal activity presents sloppiness: the collective behavior is strongly determined by a small number of parameter combinations, defined as ‘stiff’ dimensions, while it is insensitive to many others (‘sloppy’ dimensions). Here, we analyzed neural population activity from the auditory cortex of anesthetized rats while the brain spontaneously transited through different synchronized and desynchronized states and intermittently received sensory inputs. We showed that cortical state transitions were determined by changes in stiff parameters associated with the activity of a core of neurons with low responses to stimuli and high centrality within the observed network. In contrast, stimulus-evoked responses evolved along sloppy dimensions associated with the activity of neurons with low centrality and displaying large ongoing and stimulus-evoked fluctuations without affecting the integrity of the network. Our results shed light on the interplay among stability, flexibility, and responsiveness of neuronal collective dynamics during intrinsic and induced activity.

scholarly journals Cortical state transitions and stimulus response evolve along stiff and sloppy parameter dimensions, respectively

2019 ◽  
Author(s):  
Adrián Ponce-Alvarez ◽  
Gabriela Mochol ◽  
Ainhoa Hermoso-Mendizabal ◽  
Jaime de la Rocha ◽  
Gustavo Deco
Keyword(s):  
Collective Behavior ◽  
Neural Population ◽  
State Transitions ◽  
Population Activity ◽  
Sensory Inputs ◽  
Stimulus Response ◽  
Spontaneous Neuronal Activity ◽  
The Brain ◽  
Shed Light ◽  
Neural Population Activity

SummaryPrevious research showed that spontaneous neuronal activity presents sloppiness: the collective behavior is strongly determined by a small number of parameter combinations, defined as “stiff” dimensions, while it is insensitive to many others (“sloppy” dimensions). Here, we analyzed neural population activity from the auditory cortex of anesthetized rats while the brain spontaneously transited through different synchronized and desynchronized states and intermittently received sensory inputs. We showed that cortical state transitions were determined by changes in stiff parameters associated with the activity of a core of neurons with low responses to stimuli and high centrality within the observed network. In contrast, stimulus-evoked responses evolved along sloppy dimensions associated with the activity of neurons with low centrality and displaying large ongoing and stimulus-evoked fluctuations without affecting the integrity of the network. Our results shed light on the interplay among stability, flexibility, and responsiveness of neuronal collective dynamics during intrinsic and induced activity.

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