arXiv subjects

Sergio A. Cannas

Publications and source records attributed to Sergio A. Cannas.

At least 19 recordsLinked to original sources

arXiv2026

Susceptibility for extremely low external fluctuations and critical behaviour of Greenberg-Hastings neuronal model

We consider the scaling behaviour of the fluctuation susceptibility associated with the average activation in the Greenberg-Hastings neural network model and its relation to microscopic spontaneous activation. We found that, as the spontaneous activation probability tends to zero, a clear finite size scaling behaviour in the susceptibility emerges, characterized by critical exponents which follow already known scaling laws. This shows that the spontaneous activation probability plays the role of an external field conjugated to the order parameter of the dynamical activation transition. The roles of different kinds of activation mechanisms around the different dynamical phase transitions exhibited by the model are characterized numerically and using a mean field approximation.

cond-mat.dis-nn↗

arXiv2023

Mean-field solution of the neural dynamics in a Greenberg-Hastings model with excitatory and inhibitory units

We present a mean field solution of the dynamics of a Greenberg-Hastings neural network with both excitatory and inhibitory units. We analyse the dynamical phase transitions that appear in the stationary state as the model parameters are varied. Analytical solutions are compared with numerical simulations of the microscopic model defined on a fully connected network. We found that the stationary state of this system exhibits a first order dynamical phase transition (with the associated hysteresis) when the fraction of inhibitory units $f< f_t \leq 1/2$, even for a finite system. In finite systems, when $f > f_t$ the first order transition is replaced by a pseudo critical one, namely a continuous crossover between regions of low and high activity that resembles the finite size behaviour of a continuous phase transition order parameter. However, in the thermodynamic limit, we found that $f_t\to 1/2$ and the activity for $f\geq f_t$ becomes negligible for any value of $T>0$, while the first order transition for $f<f_t$ remains.

cond-mat.dis-nn↗

arXiv2023

Scale-free correlations in the dynamics of a small (N ~ 10000) cortical network

The advent of novel opto-genetics technology allows the recording of brain activity with a resolution never seen before. The characterisation of these very large data sets offers new challenges as well as unique theory-testing opportunities. Here we discuss whether the spatial and temporal correlation of the collective activity of thousands of neurons are tangled as predicted by the theory of critical phenomena. The analysis shows that both, the correlation length $ξ$ and the correlation time $τ$ scale as predicted as a function of the system size. With some peculiarities that we discuss, the analysis uncovers new evidence consistent with the view that the large scale brain cortical dynamics corresponds to critical phenomena.

q-bio.NC↗

arXiv2023

Finite-size correlation behavior near a critical point: a simple metric for monitoring the state of a neural network

In this article, a correlation metric $κ_C$ is proposed for the inference of the dynamical state of neuronal networks. $κ_C$ is computed from the scaling of the correlation length with the size of the observation region, which shows qualitatively different behavior near and away from the critical point of a continuous phase transition. The implementation is first studied on a neuronal network model, where the results of this new metric coincide with those obtained from neuronal avalanche analysis, thus well characterizing the critical state of the network. The approach is further tested with brain optogenetic recordings in behaving mice from a publicly available database. Potential applications and limitations for its use with currently available optical imaging techniques are discussed.

cond-mat.dis-nn↗

arXiv2022

Tricritical behavior in a neural model with excitatory and inhibitory units

While the support for the relevance of critical dynamics to brain function is increasing, there is much less agreement on the exact nature of the advocated critical point. Thus, a considerable number of theoretical efforts are currently concentrated on which mechanisms and what type/s of transition can be exhibited by neuronal networks models. In that direction, the present work describes the effect of incorporating a fraction of inhibitory neurons on the collective dynamics. As we show, this results in the appearence of a tricritical point for highly connected networks and non-zero fraction of inhibitory neurons. We discuss the relation of the present results with relevant experimental evidence.

cond-mat.dis-nn↗

arXiv2021

Apparently similar neuronal dynamics may lead to different collective repertoire

This report is concerned with the relevance of the microscopic rules, that implement individual neuronal activation, in determining the collective dynamics, under variations of the network topology. To fix ideas we study the dynamics of two cellular automaton models, commonly used, rather in-distinctively, as the building blocks of large scale neuronal networks. One model, due to Greenberg \& Hastings, (GH) can be described by evolution equations mimicking an integrate-and-fire process, while the other model, due to Kinouchi \& Copelli, (KC) represents an abstract branching process, where a single active neuron activates a given number of postsynaptic neurons according to a prescribed "activity" branching ratio. Despite the apparent similarity between the local neuronal dynamics of the two models, it is shown that they exhibit very different collective dynamics as a function of the network topology. The GH model shows qualitatively different dynamical regimes as the network topology is varied, including transients to a ground (inactive) state, continuous and discontinuous dynamical phase transitions. In contrast, the KC model only exhibits a continuous phase transition, independently of the network topology. These results highlight the importance of paying attention to the microscopic rules chosen to model the inter-neuronal interactions in large scale numerical simulations, in particular when the network topology is far from a mean field description. One such case is the extensive work being done in the context of the Human Connectome, where a wide variety of types of models are being used to understand the brain collective dynamics.

cond-mat.dis-nn↗

arXiv2020

Linking the connectome to action: Emergent dynamics in a robotic model of C. elegans

We analyse the neural dynamics and its relation with the emergent behaviour of a robotic vehicle that is controlled by a neural network numerical simulation based on the nervous system of the nematode Caenorhabditis elegans. The robot interacts with the environment through a sensor, that transmits the information to sensory neurons, while motor neurons outputs are connected to wheels. This is enough to allow robot movement in complex environments, avoiding collisions with obstacles. Working with a robotic model makes it possible to keep track simultaneously of the detailed microscopic dynamics of all the neurons and also register the actions of the robot in the environment in real time. This allowed us to study the interplay between connectome and complex behaviors. We found that some basic features of the global neural dynamics and their correlation with behaviour observed in the worm appear spontaneously in the robot, suggesting they are just an emergent property of the connectome.

q-bio.NC↗

arXiv2020

Box-scaling as a proxy of finite-size correlations

The scaling of correlations as a function of system size provides important hints to understand critical phenomena on a variety of systems. Its study in biological systems offers two challenges: usually they are not of infinite size, and in the majority of cases sizes can not be varied at will. Here we discuss how finite-size scaling can be approximated in an experimental system of fixed and relatively small size by computing correlations inside of a reduced field of view of various sizes (i.e., "box-scaling"). Numerical simulations of a neuronal network are used to verify such approximation, as well as the ferromagnetic 2D Ising model. The numerical results support the validity of the heuristic approach, which should be useful to characterize relevant aspects of critical phenomena in biological systems.

cond-mat.dis-nn↗

arXiv2019

Universal and non-universal neural dynamics on small world connectomes: a finite size scaling analysis

Evidence of critical dynamics has been recently found in both experiments and models of large scale brain dynamics. The understanding of the nature and features of such critical regime is hampered by the relatively small size of the available connectome, which prevent among other things to determine its associated universality class. To circumvent that, here we study a neural model defined on a class of small-world network that share some topological features with the human connectome. We found that varying the topological parameters can give rise to a scale-invariant behavior belonging either to mean field percolation universality class or having non universal critical exponents. In addition, we found certain regions of the topological parameters space where the system presents a discontinuous (i.e., non critical) dynamical phase transition into a percolated state. Overall these results shed light on the interplay of dynamical and topological roots of the complex brain dynamics.

cond-mat.dis-nn↗

arXiv2019

Controlling a complex system near its critical point via temporal correlations

A wide variety of complex systems exhibit large fluctuations both in space and time that often can be attributed to the presence of some kind of critical phenomena. Under such critical scenario it is well known that the properties of the correlation functions in space and time are two sides of the same coin. Here we test wether systems exhibiting a phase transition could self-tune to its critical point taking advantage of such correlation properties. We describe results in three models: the 2D Ising ferromagnetic model, the 3D Vicsek flocking model and a small-world neuronal network model. We illustrate how the feedback of the autocorrelation function of the order parameter fluctuations is able to shift the system towards its critical point. Since the results rely on universal properties they are expected to be relevant to a variety of other settings.

cond-mat.dis-nn↗

arXiv2018

A three-state model with competing antiferromagnetic and pairing interactions

Motivated by the rich phase diagram of the high temperature superconductors, we introduce a toy model with three state variables which can be interpreted as two state particles and holes. The Hamiltonian has a term which favors antiferromagnetism and an additional competing interaction which favors bonding between pairs of antiparallel spins mediated by holes. For low concentration of holes the dominant interaction between particles has antiferromagnetic character, leading to an antiferromagnetic phase in the temperature-hole concentration phase diagram, qualitatively similar to the antiferromagnetic phase of doped Mott insulators. For growing concentration of holes antiferromagnetic order is weakend and a phase with a different kind of order mediated by holes appears. This last phase has the form of a dome in the T-hole concentration plane. The whole phase diagram resembles those of some families of high $T_c$ superconductors. We compute the phase diagram in the mean field approximation and characterize the different phase transitions through Monte Carlo simulations.

cond-mat.supr-con↗

arXiv2018

On the coexistence of dipolar frustration and criticality in ferromagnets

In real magnets the tendency towards ferromagnetism, promoted by exchange coupling, is usually frustrated by dipolar interaction. As a result, the uniformly ordered phase is replaced by modulated (multi-domain) phases, characterized by different order parameters rather than the global magnetization. The transitions occurring within those modulated phases and towards the disordered phase are generally not of second-order type. Nevertheless, strong experimental evidence indicates that a standard critical behavior is recovered when comparatively small fields are applied that stabilize the uniform phase. The resulting power laws are observed with respect to a putative critical point that falls in the portion of the phase diagram occupied by modulated phases, in line with an avoided-criticality scenario. Here we propose a generalization of the scaling hypothesis for ferromagnets, which explains this observation assuming that the dipolar interaction acts as a relevant field, in the sense of renormalization group.

cond-mat.stat-mech↗

arXiv2018

Mitochondrial network complexity emerges from fission/fusion dynamics

Mitochondrial networks exhibit a variety of complex behaviors, including coordinated cell-wide oscillations of energy states as well as a phase transition (depolarization) in response to oxidative stress. Since functional and structural properties are often interwinded, here we characterize the structure of mitochondrial networks in mouse embryonic fibroblasts using network tools and percolation theory. Subsequently we perturbed the system either by promoting the fusion of mitochondrial segments or by inducing mitochondrial fission. Quantitative analysis of mitochondrial clusters revealed that the structural parameters of healthy mitochondria lay in between the extremes of highly fragmented and completely fusioned networks. We confirmed our results by contrasting our emprirical findings with the predictions of a recently described computational model of mitochondrial network emergence based on fission-fusion kinetics. Altogether these results not only offer an objective methodology to parametrize the complexity of this organelle but add weight to the idea that mitochondrial networks behave as critical systems and undergo structural phase transitions.

physics.bio-ph↗

arXiv2016

Modulated phases in external fields: when is reentrant behavior to be expected?

We introduce a new coarse grain model capable of describing the phase behavior of two dimensional ferromagnetic systems with competing exchange and dipolar interactions, as well as an external magnetic field. An improved expression for the mean field entropic contribution allows to compute the phase diagram in the whole temperature versus external field plane. We find that the topology of the phase diagram may be qualitatively different depending on the ratio between the strength of the competing interactions. In the regime relevant for ultrathin ferromagnetic films with perpendicular anisotropy we confirm the presence of inverse symmetry breaking from a modulated phase to a homogenous one as the temperature is lowered at constant magnetic field, as reported in experiments. For other values of the competing interactions we show that reentrance may be absent. Comparing thermodynamic quantities in both cases, as well as the evolution of magnetization profiles in the modulated phases, we conclude that the reentrant behavior is a consequence of the suppression of domain wall degrees of freedom at low temperatures at constant fields.

cond-mat.stat-mech↗

arXiv2016

Magnetization reversal in mixed ferrite-chromite perovskites with non magnetic cation on the A-site

In this work, we have performed Monte Carlo simulations in a classical model for RFe$_{1-x}$Cr$_x$O$_3$ with R=Y and Lu, comparing the numerical simulations with experiments and mean field calculations. In the analyzed compounds, the antisymmetric exchange or Dzyaloshinskii-Moriya (DM) interaction induced a weak ferromagnetism due to a canting of the antiferromagnetically ordered spins. This model is able to reproduce the magnetization reversal (MR) observed experimentally in a field cooling process for intermediate $x$ values and the dependence with $x$ of the critical temperatures. We also analyzed the conditions for the existence of MR in terms of the strength of DM interactions between Fe$^{3+}$ and Cr$^{3+}$ ions with the x values variations.

cond-mat.mtrl-sci↗

arXiv2016

FPGA Hardware Acceleration of Monte Carlo Simulations for the Ising Model

A two-dimensional Ising model with nearest-neighbors ferromagnetic interactions is implemented in a Field Programmable Gate Array (FPGA) board.Extensive Monte Carlo simulations were carried out using an efficient hardware representation of individual spins and a combined global-local LFSR random number generator. Consistent results regarding the descriptive properties of magnetic systems, like energy, magnetization and susceptibility are obtained while a speed-up factor of approximately 6 times is achieved in comparison to previous FPGA-based published works and almost $10^4$ times in comparison to a standard CPU simulation. A detailed description of the logic design used is given together with a careful analysis of the quality of the random number generator used. The obtained results confirm the potential of FPGAs for analyzing the statistical mechanics of magnetic systems.

cs.AR↗

arXiv2013

Disorder-induced mechanism for positive exchange bias fields

We propose a mechanism to explain the phenomenon of positive exchange bias on magnetic bilayered systems. The mechanism is based on the formation of a domain wall at a disordered interface during field cooling (FC) which induces a symmetry breaking of the antiferromagnet, without relying on any ad hoc assumption about the coupling between the ferromagnetic (FM) and antiferromagnetic (AFM) layers. The domain wall is a result of the disorder at the interface between FM and AFM, which reduces the effective anisotropy in the region. We show that the proposed mechanism explains several known experimental facts within a single theoretical framework. This result is supported by Monte Carlo simulations on a microscopic Heisenberg model, by micromagnetic calculations at zero temperature and by mean field analysis of an effective Ising like phenomenological model.

cond-mat.mes-hall↗

arXiv2012

Finite temperature phase diagram of ultrathin magnetic films without external fields

We analyze the finite temperature phase diagram of ultrathin magnetic films by introducing a mean field theory, valid in the low anisotropy regime, i.e., close to de Spin Reorientation Transition. The theoretical results are compared with Monte Carlo simulations carried out on a microscopic Heisenberg model. Connections between the finite temperature behavior and the ground state properties of the system are established. Several properties of the stripes pattern, such as the presence of canted states, the stripes width variation phenomenon and the associated magnetization profiles are also analyzed.

cond-mat.stat-mech↗