Nota de repúdio à campanha difamatória da organização Wiki Educação Brasil

Em 6 de novembro, o Centro de Pesquisa, Inovação e Difusão em Neuromatemática (CEPID NeuroMat) foi surpreendido por uma campanha difamatória e um abaixo-assinado promovidos pela organização Wiki Educação Brasil contra a Universidade de São Paulo (USP) e o CEPID NeuroMat. O título do abaixo-assinado é: “USP, pare imediatamente de usar a justiça americana para tirar Wiki Brasil do ar!”; o documento está endereçado ao reitor da USP, a sua chefia de gabinete da USP, ao setor de Relações Internacionais da USP e a mim mesmo, enquanto coordenador do CEPID NeuroMat. A campanha e o abaixo-assinado foram difundidos globalmente em redes sociais, em fóruns de discussão, nos projetos Wikimedia e em cartas endereçadas a pesquisadores e militantes pela ciência aberta.

O CEPID NeuroMat repudia veementemente essa campanha e convoca a comunidade acadêmica a divulgar essa nota de repúdio.

Putting into discussion a research agenda: an assessment of the workshop on Random Structures in the Brain

NeuroMat held from October 16 to 20 the workshop "Random Structures in the Brain," that brought together most team members for presentations and discussions on the work the center engages with and for an effort to frame and consolidate directions for the years to come. The workshop was attended by over 60 NeuroMat members, including principal and associate investigators, senior and junior researchers and students. Leonardo Cohen (NIH/NINDS) and Wojciech Szpankowski (Purdue University), from NeuroMat's international advisory board, as well as international guest speakers Markus Diesmann (Research Centre Jüllich) and William Lytton (State University of New York) took part in the event.

1st AMPARO Workshop: First year of activities of the NeuroMat network to educate people with Parkinson's disease

Last October the International Parkinson and Movement Disorder Society published a text about the upcoming event of the NeuroMat initiative called "Rede Amparo". NeuroMat has a network to promote the collaboration of people living with Parkinson's disease, families and health professionals to face clinical and research challenges associated with Parkinson’s disease. This initiative, called AMPARO in Portuguese, is coordinated by the NeuroMat investigator Maria Elisa Pimentel Piemonte, a physical therapist and professor at the University of São Paulo in Brazil.

In its first year of activities AMPARO offered twelve lectures for people with Parkinson, their care partner and family about important issues an improve the quality of life. In parallel, AMPARO offered 12 lectures for health professionals about the interprofessional care in PD. All lectures are available in the AMPARO website and their key points are being published in the AMPARO Book 2017. International Parkinson and Movement Disorder Society (MDS) website, 23/10/2017. (In English)

On Sequence Learning Models: Open-loop Control Not Strictly Guided by Hick’s Law

Rodrigo Pavão, Joice P. Savietto, João R. Sato, Gilberto F. Xavier and André F. Helene

According to the Hick’s law, reaction times increase linearly with the uncertainty of target stimuli. We tested the generality of this law by measuring reaction times in a human sequence learning protocol involving serial target locations which differed in transition probability and global entropy. Our results showed that sigmoid functions better describe the relationship between reaction times and uncertainty when compared to linear functions. Sequence predictability was estimated by distinct statistical predictors: conditional probability, conditional entropy, joint probability and joint entropy measures. Conditional predictors relate to closed-loop control models describing that performance is guided by on-line access to past sequence structure to predict next location. Differently, joint predictors relate to open-loop control models assuming global access of sequence structure, requiring no constant monitoring. We tested which of these predictors better describe performance on the sequence learning protocol. Results suggest that joint predictors are more accurate than conditional predictors to track performance. In conclusion, sequence learning is better described as an open-loop process which is not precisely predicted by Hick’s law.

Nonparametric statistics of dynamic networks with distinguishable nodes

Daniel Fraiman, Nicolas Fraiman and Ricardo Fraiman

The study of random graphs and networks had an explosive development in the last couple of decades. Meanwhile, techniques for the statistical analysis of sequences of networks were less developed. In this paper, we focus on networks sequences with a fixed number of labeled nodes and study some statistical problems in a nonparametric framework. We introduce natural notions of center and a depth function for networks that evolve in time. We develop several statistical techniques including testing, supervised and unsupervised classification, and some notions of principal component sets in the space of networks. Some examples and asymptotic results are given, as well as two real data examples.

Pages

 

NeuroMat

The Research, Innovation and Dissemination Center for Neuromathematics is hosted by the University of São Paulo and funded by FAPESP (São Paulo Research Foundation).

 

User login

 

Contact

Address:
1010 Matão Street - Cidade Universitária - São Paulo - SP - Brasil. 05508-090. See map.

Phone:
55 11 3091-1717

General contact email:
neuromat@numec.prp.usp.br

Media inquiries email:
comunicacao@numec.prp.usp.br