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The quantum model of the brain proposed by Ricciardi and Umezawa is extended to dissipative dynamics in order to study the problem of memory capacity. It is shown that infinitely many vacua are accessible to memory printing in a way that in…

Quantum Physics · Physics 2015-06-26 Giuseppe Vitiello

Spontaneous brain activity generically displays transient spatiotemporal coherent structures, which can selectively be affected in various neurological and psychiatric pathologies. Here we model the full brain's electroencephalographic…

Neurons and Cognition · Quantitative Biology 2025-02-25 Annalisa Caligiuri , David Papo , Görsev Yener , Bahar Güntekin , Tobias Galla , Lucas Lacasa , Massimiliano Zanin

The cerebral cortex spontaneously displays different patterns of activity that evolve over time according to the brain state. Sleep, wakefulness, resting states, and attention are examples of a wide spectrum of physiological states that can…

Inspired by the dissipative quantum model of brain, we model the states of neural nets in terms of collective modes by the help of the formalism of Quantum Field Theory. We exhibit an explicit neural net model which allows to memorize a…

Quantum Physics · Physics 2007-05-23 Eliano Pessa , Giuseppe Vitiello

In a series of two papers, we investigate the large deviations and asymptotic behavior of stochastic models of brain neural networks with random interaction coefficients. In this first paper, we take into account the spatial structure of…

Probability · Mathematics 2017-01-05 Tanguy Cabana , Jonathan Touboul

In general, the behavior of large and complex aggregates of elementary components can not be understood nor extrapolated from the properties of a few components. The brain is a good example of this type of networked systems where some…

Chaotic Dynamics · Physics 2012-08-02 Ricardo Lopez-Ruiz , Daniele Fournier-Prunaret

Functional brain networks can change rapidly as a function of stimuli or cognitive shifts. Tracking dynamic functional connectivity is particularly challenging as it requires estimating the structure of the network at each moment as well as…

Methodology · Statistics 2024-04-30 Wan-Chi Hsin , Uri T. Eden , Emily P. Stephen

The brain is characterized by a strong heterogeneity of inhibitory neurons. We report that spiking neural networks display a resonance to the heterogeneity of inhibitory neurons, with optimal input/output responsiveness occurring for levels…

Neurons and Cognition · Quantitative Biology 2021-09-30 Matteo di Volo , Alain Destexhe

Converging research suggests that the resting brain operates at the cusp of dynamic instability signified by scale-free temporal correlations. We asked if the scaling properties of these correlations differ between amplitude and phase…

Neurons and Cognition · Quantitative Biology 2015-12-09 Robert Ton , Gustavo Deco , Morten L Kringelbach , Mark Woolrich , Andreas Daffertshofer

When brain signals are recorded in an electroencephalogram or some similar large-scale record of brain activity, oscillatory patterns are typically observed that are thought to reflect the aggregate electrical activity of the underlying…

Neurons and Cognition · Quantitative Biology 2013-06-04 Andre Nathan , Valmir C. Barbosa

A central challenge in the computational modeling of neural dynamics is the trade-off between accuracy and simplicity. At the level of individual neurons, nonlinear dynamics are both experimentally established and essential for neuronal…

High-dimensional compartmental dynamical systems have been introduced to model brain metabolism. In this article, an approach is proposed to their mathematical analysis. Reductions of these models are obtained by replacing several…

Dynamical Systems · Mathematics 2013-08-05 Marion Lahutte-Auboin , Robert Costalat , Jean-Pierre Françoise , Remy Guillevin

We consider transformations of deterministic and random signals governed by simple dynamical mappings. It is shown that the resulting signal can be a random process described in terms of fractal distributions and fractal domain integrals.…

Mathematical Physics · Physics 2007-11-22 Vladimir Zverev , Boris Rubinstein

How does the brain predict physical outcomes while acting in the world? Machine learning world models compress visual input into latent spaces, discarding the spatial structure that characterizes sensory cortex. We propose isomorphic world…

Neurons and Cognition · Quantitative Biology 2026-02-24 Joshua Nunley

Whole-brain computational modeling based on structural connectivity has shown great promise in successfully simulating fMRI BOLD signals with temporal co-activation patterns that are highly similar to empirical functional connectivity…

Neurons and Cognition · Quantitative Biology 2019-11-14 Neda Kaboodvand , Martijn P van den Heuvel , Peter Fransson

The cognitive functions of human and non-human primates rely on the dynamic interplay of distributed neural assemblies. As such, it seems unlikely that cognition can be supported by macroscopic brain dynamics at the proximity of…

Effective connectivity analysis provides an understanding of the functional organization of the brain by studying how activated regions influence one other. We propose a nonparametric Bayesian approach to model effective connectivity…

Applications · Statistics 2011-07-22 Sourabh Bhattacharya , Ranjan Maitra

The presence of both critical behavior and oscillating patterns in brain dynamics is a very interesting issue. In this paper, we consider a model for a neuron population, where each neuron is modeled by an over-damped rotator. We find that…

Disordered Systems and Neural Networks · Physics 2020-11-12 Amin Safaeesirat , Saman Moghimi-Araghi

The relation between spontaneous and stimulated brain activity is a fundamental question in neuroscience, which has received wide attention in experimental studies. Recently, it has been suggested that the evoked response to external…

Disordered Systems and Neural Networks · Physics 2023-06-16 M. K. Nandi , A. De Candia , A. Sarracino , H. J. Herrmann , L. de Arcangelis

Topological data analyses are rapidly turning into key tools for quantifying large volumes of neurobiological data, e.g., for organizing the spiking outputs of large neuronal ensembles and thus gaining insights into the information produced…

Neurons and Cognition · Quantitative Biology 2019-09-18 Yuri Dabaghian