Research highlights by Collection

highlights

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool. Read more

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool. Read more

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool. Read more

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool. Read more

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false. Read more

people

publications

Um modelo concreto para o estudo da estabilidade nuclear no ensino médio

Published in A Física na Escola, 2010

Durante a aplicação de um minicurso intitulado “Energia Nuclear: Solução ou Problema?” para uma turma de Ensino Médio formada por alunos do primeiro ao terceiro ano, onde se debateu sobre as consequências da utilização de fissão nuclear, principalmente, para geração de energia elétrica, enfrentamos alguns problemas didáticos ao tratar dos conceitos da física nuclear que permeiam os reatores nucleares e os processos que neles ocorrem, dada a dificuldade de visualização e representação destes no mundo macroscópico e palpável dos alunos. Para superar estas barreiras, utilizamonos de diversos modelos e simulações computacionais, onde foi possível tornar acessível a teoria da física nuclear. Read more

Citation: Mauricio Girardi-Schappo (2010): Um modelo concreto para o estudo da estabilidade nuclear no ensino médio. A Física na Escola 11: 22--26.

A Simple Monte Carlo Approach to the Diffusion of Positrons in Gaseous Media

Published in Proceedings of the International Workshop on Positrons in Astrophysics (Astropositron), 2012

Read more

Citation: M. Girardi-Schappo, W. Tenfen, F. Arretche (2012): A Simple Monte Carlo Approach to the Diffusion of Positrons in Gaseous Media. Proceedings of the International Workshop on Positrons in Astrophysics (Astropositron). http://userpages.irap.omp.eu/~pvonballmoos/astropositron/presentations_files/Girardi-Schappo.pdf

A random walk approach to the diffusion of positrons in gaseous media

Published in Eur. Phys. J. D, 2013

In this work, we present a random walk model to study the positron diffusion in gaseous media. The positron-atom interaction is described through positron-target cross sections. The main idea is to obtain how much energy a positron transfer to the environment atoms, through ionizations and electronic excitations until its annihilation, taking the ratio between each energetically available collision channel to the total one as the probability for each process to occur. As a first application, we studied how the positron diffuse in gases of helium, neon, argon and their mixtures. To characterize the positron dynamics in each system, we calculated the radiation profile generated from the annihilation, their diffusion profiles and the most probable distances for excitation and ionization. Read more

Citation: M. Girardi-Schappo, W. Tenfen, F. Arretche (2013): A random walk approach to the diffusion of positrons in gaseous media. Eur. Phys. J. D 67: 123. https://dx.doi.org/10.1140/epjd/e2013-20508-2

A brief history of excitable map-based neurons and neural networks

Published in J. Neurosci. Methods, 2013

This review gives a short historical account of the excitable maps approach for modeling neurons and neuronal networks. Some early models, due to [Pasemann, 1993], [Chialvo, 1995] and Kinouchi and Tragtenberg (1996), are compared with more recent proposals by Rulkov (2002) and Izhikevich (2003). We also review map-based schemes for electrical and chemical synapses and some recent findings as critical avalanches in map-based neural networks. We conclude with suggestions for further work in this area like more efficient maps, compartmental modeling and close dynamical comparison with conductance-based models. Read more

Citation: M Girardi-Schappo, MHR Tragtenberg, O Kinouchi (2013): A brief history of excitable map-based neurons and neural networks. J. Neurosci. Methods 220: 116--130. https://dx.doi.org/10.1016/j.jneumeth.2013.07.014

Critical avalanches and subsampling in map-based neural networks coupled with noisy synapses

Published in Phys. Rev. E, 2013

Many different kinds of noise are experimentally observed in the brain. Among them, we study a model of noisy chemical synapse and obtain critical avalanches for the spatiotemporal activity of the neural network. Neurons and synapses are modeled by dynamical maps. We discuss the relevant neuronal and synaptic properties to achieve the critical state. We verify that networks of functionally excitable neurons with fast synapses present power-law avalanches, due to rebound spiking dynamics. We also discuss the measuring of neuronal avalanches by subsampling our data, shedding light on the experimental search for self-organized criticality in neural networks. Read more

Citation: Mauricio Girardi-Schappo, Osame Kinouchi, Marcelo Tragtenberg (2013): Critical avalanches and subsampling in map-based neural networks coupled with noisy synapses. Phys. Rev. E 88: 024701. https://dx.doi.org/10.1103/PhysRevE.88.024701

Information processing occurs via critical avalanches in a model of the primary visual cortex

Published in J. Phys. Conf. Ser., 2016

We study a new biologically motivated model for the Macaque monkey primary visual cortex which presents power-law avalanches after a visual stimulus. The signal propagates through all the layers of the model via avalanches that depend on network structure and synaptic parameter. We identify four different avalanche profiles as a function of the excitatory postsynaptic potential. The avalanches follow a size-duration scaling relation and present critical exponents that match experiments. The structure of the network gives rise to a regime of two characteristic spatial scales, one of which vanishes in the thermodynamic limit. Read more

Citation: Germano Bortolotto, Mauricio Girardi-Schappo, Jheniffer Gonsalves, Leonel Pinto, Marcelo Tragtenberg (2016): Information processing occurs via critical avalanches in a model of the primary visual cortex. J. Phys. Conf. Ser. 686: 012008. http://stacks.iop.org/1742-6596/686/i=1/a=012008

Griffiths phase and long-range correlations in a biologically motivated visual cortex model

Published in Sci. Rep., 2016

Activity in the brain propagates as waves of firing neurons, namely avalanches. These waves’ size and duration distributions have been experimentally shown to display a stable power-law profile, long-range correlations and 1/f$^b$ power spectrum in vivo and in vitro. We study an avalanching biologically motivated model of mammals visual cortex and find an extended critical-like region – a Griffiths phase – characterized by divergent susceptibility and zero order parameter. This phase lies close to the expected experimental value of the excitatory postsynaptic potential in the cortex suggesting that critical be-havior may be found in the visual system. Avalanches are not perfectly power-law distributed, but it is possible to collapse the distributions and define a cutoff avalanche size that diverges as the network size is increased inside the critical region. The avalanches present long-range correlations and 1/f$^b$ power spectrum, matching experiments. The phase transition is analytically determined by a mean-field approximation. Read more

Citation: Mauricio Girardi-Schappo, Germano Bortolotto, Jheniffer Gonsalves, Leonel Pinto, Marcelo Tragtenberg (2016): Griffiths phase and long-range correlations in a biologically motivated visual cortex model. Sci. Rep. 6: 29561. https://dx.doi.org/10.1038/srep29561

Phase diagrams and dynamics of a computationally efficient map-based neuron model

Published in PLoS ONE, 2017

We introduce a new map-based neuron model derived from the dynamical perceptron family that has the best compromise between computational efficiency, analytical tractability, reduced parameter space and many dynamical behaviors. We calculate bifurcation and phase diagrams analytically and computationally that underpins a rich repertoire of autonomous and excitable dynamical behaviors. We report the existence of a new regime of cardiac spikes corresponding to nonchaotic aperiodic behavior. We compare the features of our model to standard neuron models currently available in the literature. Read more

Citation: M. Girardi-Schappo, G. Bortolotto, R. Stenzinger, J. Gonsalves, M. Tragtenberg (2017): Phase diagrams and dynamics of a computationally efficient map-based neuron model. PLoS ONE 12: e0174621. https://dx.doi.org/10.1371/journal.pone.0174621

Measuring neuronal avalanches in disordered systems with absorbing states

Published in Phys. Rev. E, 2018

Power-law-shaped avalanche-size distributions are widely used to probe for critical behavior in many different systems, particularly in neural networks. The definition of avalanche is ambiguous. Usually, theoretical avalanches are defined as the activity between a stimulus and the relaxation to an inactive absorbing state. On the other hand, experimental neuronal avalanches are defined by the activity between consecutive silent states. We claim that the latter definition may be extended to some theoretical models to characterize their power-law avalanches and critical behavior. We study a system in which the separation of driving and relaxation time scales emerges from its structure. We apply both definitions of avalanche to our model. Both yield power-law-distributed avalanches that scale with system size in the critical point as expected. Nevertheless, we find restricted power-law-distributed avalanches outside of the critical region within the experimental procedure, which is not expected by the standard theoretical definition. We remark that these results are dependent on the model details. Read more

Citation: Mauricio Girardi-Schappo, Marcelo Tragtenberg (2018): Measuring neuronal avalanches in disordered systems with absorbing states. Phys. Rev. E 97: 042415. https://dx.doi.org/10.1103/PhysRevE.97.042415

Comment on ``Convergence towards asymptotic state in 1-D mappings: A scaling investigation''

Published in Phys. Lett. A, 2019

Nonequilibrium phase transitions are characterized by the so-called critical exponents, each of which is related to a different observable. Systems that share the same set of values for these exponents also share the same universality class. Thus, it is important that the exponents are named and treated in a standardized framework. In this comment, we reinterpret the exponents obtained by Teixeira et al. [1] for the logistic and cubic maps in order to correctly state the universality class of their bifurcations, since these maps may describe the mean-field solution of stochastic spreading processes. Read more

Citation: Mauricio Girardi-Schappo, M. Tragtenberg (2019): Comment on ``Convergence towards asymptotic state in 1-D mappings: A scaling investigation''. Phys. Lett. A 383(36): 126031. https://dx.doi.org/10.1016/j.physleta.2019.126031

[Física]2

Published in São José, SC, Brasil, Instituto Federal de Santa Catarina, 2020

A book of short comic cartoons about Physics, Nature and Science intended for science dissemination among kids and teenagers. Read more

Citation: V. Jacques, L. Hass, E.C.A. Trindade, J.V. Lima, H. Oliveira, M. Schappo, V. Gouveia, M. Girardi-Schappo (2020): [Física]2. São José, SC, Brasil, Instituto Federal de Santa Catarina. https://wiki.sj.ifsc.edu.br/images/4/40/Ebook_tirinhas_fsc_2020.pdf

Synaptic balance due to homeostatically self-organized quasicritical dynamics

Published in Phys. Rev. Research, 2020

Recent experiments suggested that a homeostatic regulation of synaptic balance leads the visual system to recover and maintain a regime of power-law avalanches. Here we study an excitatory/inhibitory (E/I) mean-field neuronal network that has a critical point with power-law avalanches and synaptic balance. When short-term depression in inhibitory synapses and firing threshold adaptation are added, the system hovers around the critical point. This homeostatically self-organized quasicritical (SOqC) dynamics generates E/I synaptic current cancellation in fast timescales, causing fluctuation-driven asynchronous-irregular (AI) firing. We present the full phase diagram of the model without adaptation varying external input versus synaptic coupling. This system has a rich dynamical repertoire of spiking patterns: synchronous regular (SR), asynchronous regular (AR), synchronous irregular (SI), slow oscillations (SO), and AI. It also presents dynamic balance of synaptic currents, since inhibitory currents try and compensate excitatory currents over time, resulting in both of them scaling linearly with external input. Our model thus unifies two different perspectives on cortical spontaneous activity: both critical avalanches and fluctuation-driven AI firing arise from SOqC homeostatic adaptation and are indeed two sides of the same coin. Read more

Citation: M. Girardi-Schappo, L. Brochini, A. Costa, T. Carvalho, O. Kinouchi (2020): Synaptic balance due to homeostatically self-organized quasicritical dynamics. Phys. Rev. Research 2: 012042(R). https://dx.doi.org/10.1103/PhysRevResearch.2.012042

Hints from statistical physics and graph theory to build synthetic connectomes: Comment on ``What would a synthetic connectome look like?'' by I. Rabinowitch

Published in Phys. Life Rev., 2020

The development of the first living being with a synthetic genome, the bacteria Mycoplasma mycoides JCVI-syn1.0 [1], generated a whole range of unfolding in science: from ethical and social implications, to biotechnological applications, to scientific understanding [2]. This was the inception of Synthetic Biology. Rabinowitch [3] proposed to do the same within Neuroscience: forward-engineering synthetic connectomes and building them inside living beings. The author lists three ways capable of changing brain connections in vivo: experience, brain activity, and genetics. The first one relies on sensorimotor experiences leading to the wiring/rewiring of brain connections (by neurofeedback, for example, in therapeutic applications). Activity-based manipulations may artificially shape connectivity through the insertion of spatiotemporal activation patterns that correlate with specific neurons firings, reinforcing synapses through spike-timing dependent plasticity (STDP). Finally, genetics is often used to manipulate the expression of protein and ionic channels throughout the membrane of the neurons, affecting its overall performance in propagating signals and making synapses. As opposed to correlation studies, the author argues that this approach could lead to understanding causal links between the structure and function of the brain. Read more

Citation: Mauricio Girardi-Schappo, Ariadne Andrade (2020): Hints from statistical physics and graph theory to build synthetic connectomes: Comment on ``What would a synthetic connectome look like?'' by I. Rabinowitch. Phys. Life Rev. 33: 19--21. https://dx.doi.org/10.1016/j.plrev.2020.03.001

Granger causality in the frequency domain: derivation and applications

Published in Rev. Bras. Ensino Fis., 2020

Physicists are starting to work in areas where noisy signal analysis is required. In these fields, such as Economics, Neuroscience, and Physics, the notion of causality should be interpreted as a statistical measure. We introduce to the lay reader the Granger causality between two time series and illustrate ways of calculating it: a signal X “Granger-causes” a signal Y if the observation of the past of X increases the predictability of the future of Y when compared to the same prediction done with the past of Y alone. In other words, for Granger causality between two quantities it suffices that information extracted from the past of one of them improves the forecast of the future of the other, even in the absence of any physical mechanism of interaction. We present derivations of the Granger causality measure in the time and frequency domains and give numerical examples using a non-parametric estimation method in the frequency domain. Parametric methods are addressed in the Appendix. We discuss the limitations and applications of this method and other alternatives to measure causality. Read more

Citation: Vinicius Lima, Fernanda Dellajustina, Renan Shimoura, Mauricio Girardi-Schappo, Nilton Kamiji, Rodrigo Pena, Antonio Roque (2020): Granger causality in the frequency domain: derivation and applications. Rev. Bras. Ensino Fis. 42: e20200007. https://dx.doi.org/10.1590/1806-9126-rbef-2020-0007

Um plano para arredondar a Terra

Published in Armadilhas camufladas de ciência: mitos e pseudociências em nossas vidas, Editora Autografia, 2021

A collection of texts where the authors explore the many pseudoscientific traps disguised as legit science, from Biology to Astronomy, Physics and Philosophy. Read more

Citation: Marcelo Schappo, Mauricio Girardi-Schappo (2021): Um plano para arredondar a Terra. Armadilhas camufladas de ciência: mitos e pseudociências em nossas vidas, Editora Autografia. https://www.professormarcelogs.com/armadilhas-camufladas-de-ciencia

Subsampled Directed-Percolation Models Explain Scaling Relations Experimentally Observed in the Brain

Published in Front. Neural Circuits, 2021

Recent experimental results on spike avalanches measured in the urethane-anesthetized rat cortex have revealed scaling relations that indicate a phase transition at a specific level of cortical firing rate variability. The scaling relations point to critical exponents whose values differ from those of a branching process, which has been the canonical model employed to understand brain criticality. This suggested that a different model, with a different phase transition, might be required to explain the data. Here we show that this is not necessarily the case. By employing two different models belonging to the same universality class as the branching process (mean-field directed percolation) and treating the simulation data exactly like experimental data, we reproduce most of the experimental results. We find that subsampling the model and adjusting the time bin used to define avalanches (as done with experimental data) are sufficient ingredients to change the apparent exponents of the critical point. Moreover, experimental data is only reproduced within a very narrow range in parameter space around the phase transition. Read more

Citation: Tawan Carvalho, Antonio Fontenele, Mauricio Girardi-Schappo, Thaís Feliciano, Leandro Aguiar, Thais Silva, Nivaldo Vasconcelos, Pedro Carelli, Mauro Copelli (2021): Subsampled Directed-Percolation Models Explain Scaling Relations Experimentally Observed in the Brain. Front. Neural Circuits 14: 576727. https://dx.doi.org/10.3389/fncir.2020.576727

Altered communication dynamics reflect cognitive deficits in temporal lobe epilepsy

Published in Epilepsia, 2021

Objective. Although temporal lobe epilepsy (TLE) is recognized as a system-level disorder, little work has investigated pathoconnectomics from a dynamic perspective. By leveraging computational simulations that quantify patterns of information flow across the connectome, we tested the hypothesis that network communication is abnormal in this condition, studied the interplay between hippocampal- and network-level disease effects, and assessed associations with cognition. Methods. We simulated signal spreading via a linear threshold model that temporally evolves on a structural graph derived from diffusion-weighted magnetic resonance imaging (MRI), comparing a homogeneous group of 31 patients with histologically proven hippocampal sclerosis to 31 age- and sex-matched healthy controls. We evaluated the modulatory effects of structural alterations of the neocortex and hippocampus on network dynamics. Furthermore, multivariate statistics addressed the relationship with cognitive parameters. Results. We observed a slowing of in- and out-spreading times across multiple areas bilaterally, indexing delayed information flow, with the strongest effects in ipsilateral frontotemporal regions, thalamus, and hippocampus. Effects were markedly reduced when controlling for hippocampal volume but not cortical thickness, underscoring the central role of the hippocampus in whole-brain disease expression. Multivariate analysis associated slower spreading time in frontoparietal, limbic, default mode, and subcortical networks with impairment across tasks tapping into sensorimotor, executive, memory, and verbal abilities. Significance. Moving beyond descriptions of static topology toward the formulation of brain dynamics, our work provides novel insight into structurally mediated network dysfunction and demonstrates that altered whole-brain communication dynamics contribute to common cognitive difficulties in TLE. Read more

Citation: Mauricio Girardi-Schappo, Fatemeh Fadaie, Hyo Lee, Benoit Caldairou, Viviane Sziklas, Joelle Crane, Boris Bernhardt, Andrea Bernasconi, Neda Bernasconi (2021): Altered communication dynamics reflect cognitive deficits in temporal lobe epilepsy. Epilepsia 62: 1022-1033. https://dx.doi.org/10.1111/epi.16864

Building a model of the brain: from detailed connectivity maps to network organization

Published in Eur. Phys. J. Spec. Top., 2021

The field of computational modeling of the brain is advancing so rapidly that now it is possible to model large scale networks representing different brain regions with a high level of biological detail in terms of numbers of neurons and synapses. For a theoretician approaching a neurobiological question, it is important to analyze the pros and cons of each of the models available. Here, we provide a tutorial review on recent models for different brain circuits, which are based on experimentally obtained connectivity maps. We discuss particularities that may be relevant to the modeler when choosing one of the reviewed models. The objective of this review is to give the reader a fair notion of the computational models covered, with emphasis on the corresponding connectivity maps, and how to use them. Read more

Citation: Renan Shimoura, Rodrigo Pena, Vinicius Lima, Nilton Kamiji, Mauricio Girardi-Schappo, Antonio Roque (2021): Building a model of the brain: from detailed connectivity maps to network organization. Eur. Phys. J. Spec. Top. 230: 2887--2909. https://dx.doi.org/10.1140/epjs/s11734-021-00152-7

Brain criticality beyond avalanches: open problems and how to approach them

Published in J. Phys. Complex., 2021

A homeostatic mechanism that keeps the brain highly susceptible to stimuli and optimizes many of its functions – although this is a compelling theoretical argument in favor of the brain criticality hypothesis, the experimental evidence accumulated during the last two decades is still not entirely convincing, causing the idea to be seemingly unknown in the more clinically-oriented neuroscience community. In this perspective review, we will briefly review the theoretical framework underlying such bold hypothesis, and point to where theory and experiments agree and disagree, highlighting potential ways to try and bridge the gap between them. Finally, we will discuss how the stand point of statistical physics could yield practical applications in neuroscience and help with the interpretation of what is a healthy or unhealthy brain, regardless of being able to validate the critical brain hypothesis. Read more

Citation: Mauricio Girardi-Schappo (2021): Brain criticality beyond avalanches: open problems and how to approach them. J. Phys. Complex. 2: 031003. https://dx.doi.org/10.1088/2632-072x/ac2071

A unified theory of E/I synaptic balance, quasicritical neuronal avalanches and asynchronous irregular spiking

Published in J. Phys. Complex., 2021

Neuronal avalanches and asynchronous irregular (AI) firing patterns have been thought to represent distinct frameworks to understand the brain spontaneous activity. The former is typically present in systems where there is a balance between the slow accumulation of tension and its fast dissipation, whereas the latter is accompanied by the balance between synaptic excitation and inhibition (E/I). Here, we develop a new theory of E/I balance that relies on two homeostatic adaptation mechanisms: the short-term depression of inhibition and the spike-dependent threshold increase. First, we turn off the adaptation and show that the so-called static system has a typical critical point commonly attributed to self-organized critical models. Then, we turn on the adaptation and show that the network evolves to a dynamic regime in which: (I) E/I synapses balance for large recovery time scales; (II) an AI firing pattern emerges; and (III) neuronal avalanches display power laws. This is the first time that these three phenomena appear simultaneously in the same network activity. Thus, we show that AI activity and PL avalanches may coexist into a single dynamics, provided that adaptation mechanisms are in place. In our model, the AI firing pattern is a direct consequence of the hovering close to the critical line where external inputs are compensated by threshold growth, creating synaptic balance for any E/I weight ratio. Read more

Citation: Mauricio Girardi-Schappo, Emilio Galera, Tawan Carvalho, Ludmila Brochini, Nilton Kamiji, Antonio Roque, Osame Kinouchi (2021): A unified theory of E/I synaptic balance, quasicritical neuronal avalanches and asynchronous irregular spiking. J. Phys. Complex. 2: 045001. https://dx.doi.org/10.1088/2632-072x/ac2792

Homeostatic Criticality in Neuronal Networks

Published in Chaos Solitons Fractals, 2022

In self-organized criticality (SOC) models, as well as in standard phase transitions, criticality is only present for vanishing external fields. Considering that this is rarely the case for natural systems, such a restriction poses a challenge to the explanatory power of these models. Besides that, in models of dissipative systems like earthquakes, forest fires, and neuronal networks, there is no true critical behavior, as expressed in clean power laws obeying finite-size scaling, but a scenario called “dirty” criticality or self-organized quasi-criticality (SOqC). Here, we propose simple homeostatic mechanisms which promote self-organization of coupling strengths, gains, and firing thresholds in neuronal networks. We show that with an adequate separation of the timescales for the coupling strength and firing threshold dynamics, near criticality (SOqC) can be reached and sustained even in the presence of significant external input. The firing thresholds adapt to and cancel the inputs ( decreases towards zero). Similar mechanisms can be proposed for the couplings and local thresholds in spin systems and cellular automata, which could lead to applications in earthquake, forest fire, stellar flare, voting, and epidemic modeling. Read more

Citation: Gustavo Menesse, Bóris Marin, Mauricio Girardi-Schappo, Osame Kinouchi (2022): Homeostatic Criticality in Neuronal Networks. Chaos Solitons Fractals 156: 111877. https://dx.doi.org/10.1016/j.chaos.2022.111877

Shortcutting from self-motion signals: quantifying trajectories and active sensing in an open maze

Published in bioRxiv, 2023

Animals navigate by learning the spatial layout of their environment. We investigated spatial learning of mice in an open maze where food was hidden in one of a hundred holes. Mice leaving from a stable entrance learned to efficiently navigate to the food without the need for landmarks. We develop a quantitative framework to reveal how the mice estimate the food location based on analyses of trajectories and active hole checks. After learning, the computed {\textquotedblleft}target estimation vector{\textquotedblright} (TEV) closely approximated the mice{\textquoteright}s trajectory and its hole check distribution. We propose that the TEV can be precisely connected to the properties of hippocampal place cells. Finally, we provide the first demonstration that, after learning the location of two food sites, the mice took a shortcut between the sites, demonstrating that they had generated a cognitive map.Teaser Mice can learn a cognitive map using only a fixed start location and self-motion signals.Competing Interest StatementThe authors have declared no competing interest. Read more

Citation: Jiayun Xu, Mauricio Girardi-Schappo, Jean-Claude B'e"ique, Andr'e Longtin, Leonard Maler (2023): Shortcutting from self-motion signals: quantifying trajectories and active sensing in an open maze. bioRxiv. https://www.biorxiv.org/content/early/2023/12/28/2023.02.24.529984

Adaptive spike threshold dynamics associated with sparse spiking of hilar mossy cells are captured by a simple model

Published in J. Physiol., 2023

Hilar mossy cells (hMCs) in the dentate gyrus (DG) receive inputs from DG granule cells (GCs), CA3 pyramidal cells and inhibitory interneurons, and provide feedback input to GCs. Behavioural and in vivo recording experiments implicate hMCs in pattern separation, navigation and spatial learning. Our experiments link hMC intrinsic excitability to their synaptically evoked in vivo spiking outputs. We performed electrophysiological recordings from DG neurons and found that hMCs displayed an adaptative spike threshold that increased both in proportion to the intensity of injected currents, and in response to spiking itself, returning to baseline over a long time scale, thereby instantaneously limiting their firing rate responses. The hMC activity is additionally limited by a prominent medium after-hyperpolarizing potential (AHP) generated by small conductance K+ channels. We hypothesize that these intrinsic hMC properties are responsible for their low in vivo firing rates. Our findings extend previous studies that compare hMCs, CA3 pyramidal cells and hilar inhibitory cells and provide novel quantitative data that contrast the intrinsic properties of these cell types. We developed a phenomenological exponential integrate-and-fire model that closely reproduces the hMC adaptive threshold nonlinearities with respect to their threshold dependence on input current intensity, evoked spike latency and long-lasting spike-induced increase in spike threshold. Our robust and computationally efficient model is amenable to incorporation into large network models of the DG that will deepen our understanding of the neural bases of pattern separation, spatial navigation and learning. Read more

Citation: Anh-Tuan Trinh, Mauricio Girardi-Schappo, Jean-Claude Béïque, André Longtin, Leonard Maler (2023): Adaptive spike threshold dynamics associated with sparse spiking of hilar mossy cells are captured by a simple model. J. Physiol. 601: 4397--4422. https://dx.doi.org/10.1113/JP283728

Optimal input reverberation and homeostatic self-organization towards the edge of synchronization

Published in arXiv:2402.05032 [nlin.AO], 2024

Transient or partial synchronization can be used to do computations, although a fully synchronized network is frequently related to epileptic seizures. Here, we propose a homeostatic mechanism that is capable of maintaining a neuronal network at the edge of a synchronization transition, thereby avoiding the harmful consequences of a fully synchronized network. We model neurons by maps since they are dynamically richer than integrate-and-fire models and more computationally efficient than conductance-based approaches. We first describe the synchronization phase transition of a dense network of neurons with different tonic spiking frequencies coupled by gap junctions. We show that at the transition critical point, inputs optimally reverberate through the network activity through transient synchronization. Then, we introduce a local homeostatic dynamic in the synaptic coupling and show that it produces a robust self-organization toward the edge of this phase transition. We discuss the potential biological consequences of this self-organization process, such as its relation to the Brain Criticality hypothesis, its input processing capacity, and how its malfunction could lead to pathological synchronization. Read more

Citation: Sue Rhâmidda, Mauricio Girardi-Schappo, Osame Kinouchi (2024): Optimal input reverberation and homeostatic self-organization towards the edge of synchronization. arXiv:2402.05032 [nlin.AO] . https://dx.doi.org/10.48550/arXiv.2402.05032

talks

teaching

Nonlinear dynamics in Neuroscience

Graduate course, Latin American School on Computational Neuroscience - LASCON, Universidade de São Paulo (USP), 2020

Explore the connection of dynamical systems in neural modeling. Delve into reduced one- and two-dimensional neural models, examining their intricacies and behavior in phase-space analysis. Uncover the phenomena of subthreshold oscillations, the canard phenomenon, and resonance, both subthreshold and suprathreshold. The course also delves into the intriguing dynamics of bursting in neural systems. Read more

Synaptic plasticity and learning

Graduate course, Latin American School on Computational Neuroscience - LASCON, Universidade de São Paulo (USP), 2020

Dive into the intricate world of “Synaptic Plasticity and Learning,” unraveling the mechanisms that underlie the adaptability of neural connections. From the classic Hebb rule to modern models of Hebbian learning, explore spike-timing dependent potentiation and depression, as well as long and short term synaptic changes. Understand the principles of unsupervised learning, delve into the Hopfield model, and explore the fascinating realm of reward-based learning. Read more

Fundamentals of Physics 2

Undergraduate course, Universidade Federal de Santa Catarina (UFSC), 2022

This course covers the fundamentals of rotational mechanics, oscillations, waves (including sound), and the principles of thermodynamics. We study the kinematics and dynamics of rigid body rotation, and grasp fundamental concepts related to temperature, heat, entropy, thermodynamic cycles and the kinetic theory of gases. Read more

Vector Mechanics for Engineering: Statics

Undergraduate course, Universidade Federal de Santa Catarina (UFSC), 2022

Study of the equilibrium conditions of particles and rigid bodies (structures, beams, trusses, etc.) in the 2D and 3D, involving the calculation of reactions in standard connections and supports used in engineering; calculation of axial forces, shear forces, and bending moments in structures and beams; calculation of centroids of areas and volumes of simple and composite figures; calculation of moments of inertia of simple and composite flat plates and simple and composite solids; cable equilibrium. Read more