Publications

Tight Hamilton cycles in random hypergraphs, Random Structures Algorithms

Allen, P.; Böttcher, J.; Kohayakawa, Y. and Person Y.

We give an algorithmic proof for the existence of tight Hamilton cycles in a random r-uniform hypergraph with edge probability p=n^{-1+eps} for every eps>0. This partly answers a question of Dudek and Frieze [Random Structures Algorithms], who used a second moment method to show that tight Hamilton cycles exist even for p=omega(n)/n (r>2) where omega(n) tends to infinity arbitrary slowly, and for p=(e+o(1))/n (r>3). The method we develop for proving our result applies to related problems as well.

Stochastically Perturbed Chains of Variable Memory

Garcia, N. L. and Moreira, L. - In this paper, we study inference for chains of variable order under two distinct contamination regimes. Consider a chain of variable memory on a finite alphabet containing zero, at each instant of time an independent coin is flipped and if it turns head a contamination occurs.

Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches

Ribeiro, T. L. ; Ribeiro, S. ; Belchior, H. ; Caixeta, F. and Copelli, M. - The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent τ=3/2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally.

Hydrodynamic limit for interacting neurons

De Masi, A. ; Galves, A. ; Löcherbach, E. and Presutti, E. - This paper studies the hydrodynamic limit of a stochastic process describing the time evolution of a system with N neurons with mean-field interactions produced both by chemical and by electrical synapses.

Independence tests for continuous random variables based on the longest increasing subsequence

García, J. E. and González-López, V. A. - We propose a new class of nonparametric tests for the supposition of independence between two continuous random variables X and Y. Given a size n sample, let π be the permutation which maps the ranks of the X observations on the rank of the Y observations.

Modeling of acoustic signal energies with a generalized Frank copula. A linguistic conjecture is reviewed

García, J. E. and González-López, V. A. - In this paper was selected a generalized Frank copula to model the dependence between the energy on two frequency bands of the speech signal, coming from eight languages. Was developed an algorithm that uses maximum likelihood to choose the best fitting copula's parameters.


Loss of Memory of Hidden Markov Models and Lyapunov Exponents

Collet, P. and Leonardi, F. - In this paper we prove that the asymptotic rate of exponential loss of memory of a finite state hidden Markov model is bounded above by the difference of the first two Lyapunov exponents of a certain product of matrices.


Biological Motion Coding in the Brain: Analysis of Visually Driven EEG Functional Networks

Fraiman, D. ; Saunier, G. ; Martins, E. F. and Vargas, C. D. - Herein, we address the time evolution of brain functional networks computed from electroencephalographic activity driven by visual stimuli. We describe how these functional network signatures change in fast scale when confronted with point-light display stimuli depicting biological motion (BM) as opposed to scrambled motion (SM).

Graph analysis of dream reports is especially informative about psychosis

Mota, N. B. ; Furtado, R. ; Maia, P. P. C. ; Copelli, M. and Ribeiro, S. - Early psychiatry investigated dreams to understand psychopathologies. Contemporary psychiatry, which neglects dreams, has been criticized for lack of objectivity. In search of quantitative insight into the structure of psychotic speech, we investigated speech graph attributes (SGA) in patients with schizophrenia, bipolar disorder type I, and non-psychotic controls as they reported waking and dream contents.

Infinite Systems of Interacting Chains with Memory of Variable Length—A Stochastic Model for Biological Neural Nets

Galves, A. and Löcherbach, E. - We consider a new class of non Markovian processes with a countable number of interacting components. At each time unit, each component can take two values, indicating if it has a spike or not at this precise moment.

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