R. F. O. Pena, V. Lima, R. O. Shimoura, C. C. Ceballos, H. G. Rotstein and A. C. Roque
The conventional impedance profile of a neuron can identify the presence of resonance and other properties of the neuronal response to oscillatory inputs, such as nonlinear response amplifications, but it cannot distinguish other nonlinear properties such as asymmetries in the shape of the voltage response envelope. Experimental observations have shown that the response of neurons to oscillatory inputs preferentially enhances either the upper or lower part of the voltage envelope in different frequency bands. These asymmetric voltage responses arise in a neuron model when it is submitted to high enough amplitude oscillatory currents of variable frequencies. We show how the nonlinearities associated to different ionic currents or present in the model as captured by its voltage equation lead to asymmetrical response and how high amplitude oscillatory currents emphasize this response. We propose a geometrical explanation for the phenomenon where asymmetries result not only from nonlinearities in their activation curves but also from nonlinearites captured by the nullclines in the phase-plane diagram and from the system’s time-scale separation. In addition, we identify an unexpected frequency-dependent pattern which develops in the gating variables of these currents and is a product of strong nonlinearities in the system as we show by controlling such behavior by manipulating the activation curve parameters. The results reported in this paper shed light on the ionic mechanisms by which brain embedded neurons process oscillatory information.
Renan Hiroshi Matsuda, Gabriela Pazin Tardelli, Carlos Otávio Guimarães, Victor Hugo Souza and Oswaldo Baffa Filho
Transcranial magnetic stimulation is a noninvasive method of the human cortex stimulation. Known as TMS, the technique was introduced by Barker et al. in 1985. Its operation is based on the Faraday’s Law, in which an intense magnetic feld that varies rapidly is able to induce an electric feld in the surface of the brain, depolarizing the neurons in the cerebral cortex. Due to its versatility, TMS is currently used for both research and clinical applications. Among the clinical applications, TMS is used as a diagnostic tool and also as a therapeutic technique for some neurodegenerative diseases and psychiatric disorders such as depression, Parkinson’s disease and tinnitus. As for the diagnostic tool, motor mapping is a technique to delineate the area of representation of the target muscle in its cortical surface, whose applicability may be in studies of the cerebral physiology to evaluate damage to the motor cortex and corticospinal tract. This review aims to introduce the physics, the basic elements, the biological principles and the main applications of transcranial magnetic stimulation.
News
| Nov 07, 2019
This week "Agência FAPESP" website featured an article about the new RIDC NeuroMat podcast, specially addressing the publication of the second episode of the program. The aim of the podcast is to spread the research developed by NeuroMat, at the interface between neurobiology and mathematics.
The program addresses three main themes: the neuronal spiking systems model developed by the CEPID NeuroMat team; the statistical framework needed to rigorously address the “statistical brain” conjecture; and the processes of construction and production of the state-of-the science in Brazil right now.
The NeuroMat dissemination team has launched a series of short videos on which young researchers describe briefly questions they tackle and their ongoing work. As of now, four videos were distributed with junior NeuroMat researchers Fernando Araujo Najman, Fernanda Torres, Arthur Lopes da Silva Valencio, and Luiggi Lustosa.
Morgan André
We consider a countably infinite system of spiking neurons. In this model each neuron has a membrane potential which takes value in the non-negative integers. Each neuron is also associated with two point processes. The first one is a Poisson process of some parameter γ, representing the \textit{leak times}, that is the times at which the membrane potential of the neuron is spontaneously reset to 0. The second point process, which represents the \textit{spiking times}, has a non-constant rate which depends on the membrane potential of the neuron at time t. This model was previously proven to present a phase transition with respect to the parameter γ. It was also proven that the renormalized time of extinction of a finite version of the system converges in law toward an exponential random variable when the number of neurons goes to infinity, which indicates a metastable behavior. Here we prove a result which is in some sense the symmetrical of this last result: we prove that when γ>1 (super-critical) the renormalized time of extinction converges in probability to 1.
