arXiv subjects

E. E. N. Macau

Publications and source records attributed to E. E. N. Macau.

9 recordsLinked to original sources

arXiv2023

Plastic neural network with transmission delays promotes equivalence between function and structure

The brain is formed by cortical regions that are associated with different cognitive functions. Neurons within the same region are more likely to connect than neurons in distinct regions, making the brain network to have characteristics of a network of subnetworks. The values of synaptic delays between neurons of different subnetworks are greater than those of the same subnetworks. This difference in communication time between neurons has consequences on the firing patterns observed in the brain, which is directly related to changes in neural connectivity, known as synaptic plasticity. In this work, we build a plastic network of Hodgkin-Huxley neurons in which the connectivity modifications follow a spike-time dependent rule. We define an internal-delay among neurons communicating within the same subnetwork, an external-delay for neurons belonging to distinct subnetworks, and study how these communicating delays affect the entire network dynamics. We observe that the neuronal network exhibits a specific connectivity configuration for each synchronised pattern. Our results show how synaptic delays and plasticity work together to promote the formation of structural coupling among the neuronal subnetworks. We conclude that plastic neuronal networks are able to promote equivalence between function and structure meaning that topology emerges from behaviour and behaviour emerges from topology, creating a complex dynamical process where topology adapts to conform with the plastic rules and firing patterns reflect the changes on the synaptic weights.

q-bio.NC↗

arXiv2021

Formation of Earth-sized planets within the Kepler-1647 System Habitable Zone

The Kepler-1647 is a binary system with two Sun-type stars (approximately 1.22 and 0.97 Solar mass). It has the most massive circumbinary planet (1.52 Jupiter mass) with the longest orbital period (1,107.6 days) detected by the Kepler probe and is located within the habitable zone (HZ) of the system. In this work, we investigated the ability to form and house an Earth-sized planet within its HZ. First, we computed the limits of its HZ and performed numerical stability tests within that region. We found that HZ has three sub-regions that show stability, one internal, one co-orbital, and external to the host planet Kepler-1647b. Within the limits of these three regions, we performed numerical simulations of planetary formation. In the regions inner and outer to the planet, we used two different density profiles to explore different conditions of formation. In the co-orbital region, we used eight different values of total disc mass. We showed that many resonances are located within regions causing much of the disc material to be ejected before a planet is formed. Thus, the system might have two asteroid belts with Kirkwood gaps, similar to the Solar Systemś main belt of asteroids. The co-orbital region proved to be extremely sensitive, not allowing the planet formation, but showing that this binary system has the capacity to have Trojan bodies. Finally, we looked for regions of stability for an Earth-sized moon. We found that there is stability for a moon with this mass up to 0.4 Hillś radius from the host planet.

astro-ph.EP↗

arXiv2020

Earth-size planet formation in the habitable zone of circumbinary stars

In this work is investigated the possibility of close-binary star systems having Earth-size planets within their habitable zones. First, we selected all known close-binary systems with confirmed planets (totaling 22 systems) to calculate the boundaries of their respective habitable zones (HZ). However, only eight systems had all the data necessary for the computation of the HZ. Then, we numerically explored the stability within the habitable zones for each one of the eight systems using test particles. From the results, we selected five systems that have stable regions inside the habitable zones (HZ), namely Kepler-34, 35, 38, 413 and 453. For these five cases of systems with stable regions in the HZ, we perform a series of numerical simulations for planet formation considering disks composed of planetary embryos and planetesimals, with two distinct density profiles, in addition to the stars and host planets of each system. We found that in the case of Kepler-34 and 453 systems no Earth-size planet is formed within the habitable zones. Although planets with Earth-like masses were formed in the Kepler-453, but they were outside the HZ. In contrast, for Kepler-35 and 38 systems, the results showed that potentially habitable planets are formed in all simulations. In the case of the Kepler-413 system, in just one simulation a terrestrial planet was formed within the habitable zone.

astro-ph.EP↗

arXiv2019

Bistable firing pattern in a neural network model

Excessively high, neural synchronisation has been associated with epileptic seizures, one of the most common brain diseases worldwide. A better understanding of neural synchronisation mechanisms can thus help control or even treat epilepsy. In this paper, we study neural synchronisation in a random network where nodes are neurons with excitatory and inhibitory synapses, and neural activity for each node is provided by the adaptive exponential integrate-and-fire model. In this framework, we verify that the decrease in the influence of inhibition can generate synchronisation originating from a pattern of desynchronised spikes. The transition from desynchronous spikes to synchronous bursts of activity, induced by varying the synaptic coupling, emerges in a hysteresis loop due to bistability where abnormal (excessively high synchronous) regimes exist. We verify that, for parameters in the bistability regime, a square current pulse can trigger excessively high (abnormal) synchronisation, a process that can reproduce features of epileptic seizures. Then, we show that it is possible to suppress such abnormal synchronisation by applying a small-amplitude external current on less than 10% of the neurons in the network. Our results demonstrate that external electrical stimulation not only can trigger synchronous behaviour, but more importantly, it can be used as a means to reduce abnormal synchronisation and thus, control or treat effectively epileptic seizures.

physics.bio-ph↗

arXiv2018

Search and Return Model for Stochastic Path Integrators

We extend a recently introduced prototypical stochastic model describing uniformly the search and return of objects looking for new food sources around a given home. The model describes the kinematic motion of the object with constant speed in two dimensions. The angular dynamics is driven by noise and describes a "pursuit" and "escape" behavior of the heading and the position vectors. Pursuit behavior ensures the return to the home and the escaping between the two vectors realizes exploration of space in the vicinity of the given home. Noise is originated by environmental influences and during decision making of the object. We take symmetric α-stable noise since such noise is observed in experiments. We now investigate for the simplest possible case, the consequences of limited knowledge of the position angle of the home. We find that both noise type and noise strength can significantly increase the probability of returning to the home. First, we review shortly main findings of the model presented in the former manuscript. These are the stationary distance distribution of the noise driven conservative dynamics and the observation of an optimal noise for finding new food sources. Afterwards, we generalize the model by adding a constant shift γ within the interaction rule between the two vectors. The latter might be created by a permanent uncertainty of the correct home position. Non vanishing shifts transform the kinematics of the searcher to a dissipative dynamics. For the latter we discuss the novel deterministic properties and calculate the stationary spatial distribution around the home.

cond-mat.stat-mech↗

arXiv2018

Optimal Noise in a Stochastic Model for Local Search

We develop a prototypical stochastic model for local search around a given home. The stochastic dynamic model is motivated by experimental findings of the motion of a fruit fly around a given spot of food but shall generally describe local search behavior. The local search consists of a sequence of two epochs. In the first the searcher explores new space around the home whereas it returns to the home during the second epoch. In the proposed two dimensional model both tasks are described by the same stochastic dynamics. The searcher moves with constant speed and its angular dynamics is driven by a symmetric α-stable noise source. The latter stands for the uncertainty to decide the new direction of motion. The main ingredient of the model is the nonlinear interaction dynamics of the searcher with its home. In order to determine the new heading direction, the angles of its position to the home and of the heading vector need to be known. A bound state to the home is realized by a permanent switch of a repulsive and attractive forcing of the heading direction from the position direction corresponding to search and return epochs. Our investigation elucidates the analytic tractability of the deterministic and stochastic dynamics. The noise enables a faster finding of a target distinct from the home with optimal intensity. This optimal situation is related to the noise dependent relaxation time. It is uniquely defined for all α and distinguishes between the stochastic dynamics before and after its value. For times large compared to this we derive the corresponding Smoluchowski equation and find diffusive spreading of searcher in the space. As result of its simplicity the model aims to reproduce the local search behavior of simple units during their exploration of surrounding space and their quasi-periodic return to a home.

cond-mat.stat-mech↗

arXiv2017

Alterations in brain connectivity due to plasticity and synaptic delay

Brain plasticity refers to brain's ability to change neuronal connections, as a result of environmental stimuli, new experiences, or damage. In this work, we study the effects of the synaptic delay on both the coupling strengths and synchronisation in a neuronal network with synaptic plasticity. We build a network of Hodgkin-Huxley neurons, where the plasticity is given by the Hebbian rules. We verify that without time delay the excitatory synapses became stronger from the high frequency to low frequency neurons and the inhibitory synapses increases in the opposite way, when the delay is increased the network presents a non-trivial topology. Regarding the synchronisation, only for small values of the synaptic delay this phenomenon is observed.

q-bio.NC↗

arXiv2017

Inference of topology and the nature of synapses, and the flow of information in neuronal networks

The characterisation of neuronal connectivity is one of the most important matters in neuroscience. In this work, we show that a recently proposed informational quantity, the causal mutual information, employed with an appropriate methodology, can be used not only to correctly infer the direction of the underlying physical synapses, but also to identify their excitatory or inhibitory nature, considering easy to handle and measure bivariate time-series. The success of our approach relies on a surprising property found in neuronal networks by which non-adjacent neurons do "understand" each other (positive mutual information), however this exchange of information is not capable of causing effect (zero transfer entropy). Remarkably, inhibitory connections, responsible for enhancing synchronisation, transfer more information than excitatory connections, known to enhance entropy in the network. We also demonstrate that our methodology can be used to correctly infer directionality of synapses even in the presence of dynamic and observational Gaussian noise, and is also successful in providing the effective directionality of inter modular connectivity, when only mean fields can be measured.

q-bio.NC↗

arXiv2017

How synapses can enhance sensibility of a neural network

In this work, we study the dynamic range in a neuronal network modelled by cellular automaton. We consider deterministic and non-deterministic rules to simulate electrical and chemical synapses. Chemical synapses have an intrinsic time-delay and are susceptible to parameter variations guided by learning Hebbian rules of behaviour. Our results show that chemical synapses can abruptly enhance sensibility of the neural network, a manifestation that can become even more predominant if learning rules of evolution are applied to the chemical synapses.

physics.bio-ph↗