Natalia B. Mota, Janaina Weissheimer, Marina Ribeiro, Mizziara de Paiva, Juliana D'Avila, Gabriela Simabucuru, Monica F. Chaves, Lucas Cecchi, Jaime Cirne, Guillermo Cecchi, Cilene Rodrigues, Mauro Copelli, Sidarta Ribeiro
Neuroscience and psychology agree that dreaming helps to cope with negative emotions and learn from experience. The current global threat related to the COVID-19 pandemic led to widespread social isolation. Does dreaming change and/or reflect mental suffering? To address these questions, we applied natural language processing tools to study 239 dream reports from 67 individuals either before the Covid-19 outbreak or during March-April, 2020, when quarantine was imposed in Brazil following the pandemic announcement by the WHO. Pandemic dreams showed a higher proportion of anger and sadness words and higher average semantic similarities to the terms contamination and cleanness. These features were associated with mental suffering related to social isolation, as they explained 39% of the variance in PANSS negative subscale (p=0.0092). These results corroborate the hypothesis that pandemic dreams reflect mental suffering, fear of contagion, and important changes in daily habits.
João Alexandre Peschanski, Cassiano Reinert Novais dos Santos, Carlos Eduardo Ribas
The Neuroscience Experiments System (NES) was developed to manage information originated from neuroscience experiments. Through the NES export module, a researcher is able to download experimental data and metadata in interoperable formats; nevertheless, the understanding of what is downloaded is not always a simple task. In accordance with the agile methodology guidelines, we have worked within the Frictionless Data philosophical and technical framework in order to decrease friction that is commonly associated with understanding data and metadata. Working with Frictionless Data may lead to improving research efficiency; it is also an opportunity to create scripts and softwares to improve data analysis.
Morgan André and Léo Planche
We consider a continuous-time stochastic model of spiking neurons. In this model, we have a finite or countable number of neurons which are vertices in some graph G where the edges indicate the synaptic connection between them. We focus on metastability, understood as the property for the time of extinction of the network to be asymptotically memory-less, and we prove that this model exhibits two different behaviors depending on the nature of the specific underlying graph of interaction G that is chosen. This model depends on a leakage parameter γ, and it was previously proven that when the graph G is the infinite one-dimensional lattice, this model presents a phase transition with respect to γ. It was also proven that, when γ is small enough, the renormalized time of extinction (the first time at which all neurons have a null membrane potential) of a finite version of the system converges in law toward an exponential random variable when the number of neurons goes to infinity. The present article is divided into two parts. First we prove that, in the finite one-dimensional lattice, this last result doesn't hold if γ is not small anymore, in fact we prove that for γ>1 the renormalized time of extinction is asymptotically deterministic. Then we prove that conversely, if G is the complete graph, the result of metastability holds for any positive γ.
Cecilia Romaro, Fernando Araujo Najman, William W Lytton, Antonio C. Roque and Salvador Dura-Bernal
The Potjans-Diesmann cortical microcircuit model is a widely used model originally implemented in NEST. Here, we re-implemented the model using NetPyNE, a high-level Python interface to the NEURON simulator, and reproduced the findings of the original publication. We also implemented a method for rescaling the network size which preserves first and second-order statistics, building on existing work on network theory. The new implementation enables using more detailed neuron models with multi-compartment morphologies and multiple biophysically realistic channels. This opens the model to new research, including the study of dendritic processing, the influence of individual channel parameters, and generally multiscale interactions in the network. The rescaling method provides flexibility to increase or decrease the network size if required when running these more realistic simulations. Finally, NetPyNE facilitates modifying or extending the model using its declarative language; optimizing model parameters; running efficient large-scale parallelized simulations; and analyzing the model through built-in methods, including local field potential calculation and information flow measures.
A conjecture in neurobiology that dates back to Helmholtz in the XIX century states that the brain can unconsciously identify statistical regularities in sequences of stimuli. Motivated by this claim, a NeuroMat group led by Antonio Galves and Claudia Vargas, with the participation of Aline Duarte, Ricardo Fraiman and Guilherme Ost, have successfully applied mathematical techniques to retrieve from EEG measurements in people the structure of stochastic chains with memory of variable length (called context trees) that generate auditory input stimuli (Duarte et al., 2019). The brains of the experiment subjects had ongoing spiking activity patterns (arguably somehow related to the EEG signals) that were perturbed by the input stimuli in a way that allowed the mathematical retrieval tools to work satisfactorily.
Thus, from a theoretical point of view the following question can be posed: is the brain machinery, with all its intricate web of molecular and cellular processes, necessary for the efficient retrieval of context trees? Or simpler, brain-inspired networks of spiking elements can also encode in their spiking activity a signature of the context tree that can be identified by the same mathematical tools?
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