arXiv subjects

G. Lattanzi

Publications and source records attributed to G. Lattanzi.

3 recordsLinked to original sources

arXiv2021

School reopening should be guided by solid evidence and mitigation measures against Covid-19

The debate on the role of school closures as a mitigation strategy against the spread of Covid-19 is gaining relevance due to emerging variants in Europe. According to WHO, decisions on schools "should be guided by a risk-based approach". However, risk evaluation requires sound methods, transparent data and careful consideration of the context at the local level. We review a recent study by Gandini et al., on the role of school opening as a driver of the second COVID-19 wave in Italy, which concluded that there was no connection between school openings/closures and SARS-CoV-2 incidence. infections. This analysis has been widely commented in Italian media as conclusive proof that "schools are safe". However the study presents severe oversights and careless interpretation of data.

q-bio.PE↗

arXiv2004

Phase shifts of synchronized oscillators and the systolic/diastolic blood pressure relation

We study the phase-synchronization properties of systolic and diastolic arterial pressure in healthy subjects. We find that delays in the oscillatory components of the time series depend on the frequency bands that are considered, in particular we find a change of sign in the phase shift going from the Very Low Frequency band to the High Frequency band. This behavior should reflect a collective behavior of a system of nonlinear interacting elementary oscillators. We prove that some models describing such systems, e.g. the Winfree and the Kuramoto models offer a clue to this phenomenon. For these theoretical models there is a linear relationship between phase shifts and the difference of natural frequencies of oscillators and a change of sign in the phase shift naturally emerges.

physics.med-ph↗

arXiv1997

Stochastic learning in a neural network with adapting synapses

We consider a neural network with adapting synapses whose dynamics can be analitically computed. The model is made of $N$ neurons and each of them is connected to $K$ input neurons chosen at random in the network. The synapses are $n$-states variables which evolve in time according to Stochastic Learning rules; a parallel stochastic dynamics is assumed for neurons. Since the network maintains the same dynamics whether it is engaged in computation or in learning new memories, a very low probability of synaptic transitions is assumed. In the limit $N\to\infty$ with $K$ large and finite, the correlations of neurons and synapses can be neglected and the dynamics can be analitically calculated by flow equations for the macroscopic parameters of the system.

cond-mat.dis-nn↗