Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-- trained using model simulations-- to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features, and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics

Entanglement and Tensor Product Decomposition for Two Fermions

The problem of the choice of tensor product decomposition in a system of two fermions with the help of Bogoliubov transformations of creation and annihilation operators is discussed. The set of physical states of the composite system is restricted by the superselection rule forbidding the superposition of fermions and bosons. It is shown that the Wootters concurrence is not proper entanglement measure in this case. The explicit formula for the entanglement of formation is found and its dependence on tensor product decompositions of the Hilbert space is discussed. It is shown that the set of separable states is narrower than in two-qubit case. Moreover, there exist states which are separable with respect to all tensor product decompositions of the Hilbert space.Comment: 8pp, published versio

Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators—trained using model simulations—to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin–Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics

Measurements of π±\pi^\pm, K±^\pm, p and pˉ\bar{\textrm{p}} spectra in proton-proton interactions at 20, 31, 40, 80 and 158 GeV/c with the NA61/SHINE spectrometer at the CERN SPS

Measurements of inclusive spectra and mean multiplicities of $\pi^\pm$, K$^\pm$, p and $\bar{\textrm{p}}$ produced in inelastic p+p interactions at incident projectile momenta of 20, 31, 40, 80 and 158 GeV/c ($\sqrt{s} =$ 6.3, 7.7, 8.8, 12.3 and 17.3 GeV, respectively) were performed at the CERN Super Proton Synchrotron using the large acceptance NA61/SHINE hadron spectrometer. Spectra are presented as function of rapidity and transverse momentum and are compared to predictions of current models. The measurements serve as the baseline in the NA61/SHINE study of the properties of the onset of deconfinement and search for the critical point of strongly interacting matter

Measurements of ππ±^{±} , KK±^{±}, pp and pˉ\bar{p} spectra in proton-proton interactions at 20, 31, 40, 80 and 158 GeV/cc with the NA61/SHINE spectrometer at the CERN SPS

Measurements of inclusive spectra and mean multiplicities of π ± π± , K ± ± , p and p ¯ p¯ produced in inelastic p + p interactions at incident projectile momenta of 20, 31, 40, 80 and 158 GeV /c GeV /c (s √ = s= 6.3, 7.7, 8.8, 12.3 and 17.3 GeV GeV , respectively) were performed at the CERN Super Proton Synchrotron using the large acceptance NA61/SHINE hadron spectrometer. Spectra are presented as function of rapidity and transverse momentum and are compared to predictions of current models. The measurements serve as the baseline in the NA61/SHINE study of the properties of the onset of deconfinement and search for the critical point of strongly interacting matter

Measurements of π±\pi^\pm, K±K^\pm, pp and pˉ\bar{p} spectra in 40^{40}Ar+45^{45}Sc collisions at 13AA to 150AA GeV/cc

The NA61/SHINE experiment at the CERN Super Proton Synchrotron studies the onset of deconfinement in strongly interacting matter through a beam energy scan of particle production in collisions of nuclei of varied sizes. This paper presents results on inclusive double-differential spectra, transverse momentum and rapidity distributions and mean multiplicities of $\pi^\pm$, $K^\pm$, $p$ and $\bar{p}$ produced in $^{40}$Ar+$^{45}$Sc collisions at beam momenta of 13$A$, 19$A$, 30$A$, 40$A$, 75$A$ and 150$A$ GeV/$c$. The analysis uses the 10% most central collisions, where the observed forward energy defines centrality. The energy dependence of the $K^\pm$/$\pi^\pm$ ratios as well as of inverse slope parameters of the $K^\pm$ transverse mass distributions are placed in between those found in inelastic $p$+$p$ and central Pb+Pb collisions. The results obtained here establish a system-size dependence of hadron production properties that so far cannot be explained either within statistical (SMES, HRG) or dynamical (EPOS, UrQMD, PHSD, SMASH) models

Measurements of π+\pi^+, π−\pi^-, pp, pˉ\bar{p}, K+K^+ and K−K^- production in 120 GeV/cc p + C interactions

This paper presents multiplicity measurements of charged hadrons produced in 120 GeV/$c$ proton-carbon interactions. The measurements were made using data collected at the NA61/SHINE experiment during two different data-taking periods, with increased phase space coverage in the second configuration due to the addition of new subdetectors. Particle identification via $dE/dx$ was employed to obtain double-differential production multiplicities of $\pi^+$, $\pi^-$, $p$, $\bar{p}$, $K^+$ and $K^-$. These measurements are presented as a function of laboratory momentum in intervals of laboratory polar angle covering the range from 0 to 450 mrad. They provide crucial inputs for current and future long-baseline neutrino experiments, where they are used to estimate the initial neutrino flux

Search for the critical point of strongly-interacting matter in 40^{40}Ar + 45^{45}Sc collisions at 150A Ge V /c using scaled factorial moments of protons

The critical point of dense, strongly interacting matter is searched for at the CERN SPS in $^{40}$Ar + $^{45}$Sc collisions at 150A Ge V /c. The dependence of second-order scaled factorial moments of proton multiplicity distribution on the number of subdivisions of transverse momentum space is measured. The intermittency analysis is performed using both transverse momentum and cumulative transverse momentum. For the first time, statistically independent data sets are used for each subdivision number. The obtained results do not indicate any statistically significant intermittency pattern. An upper limit on the fraction of correlated proton pairs and the power of the correlation function is obtained based on a comparison with the Power-law Model developed for this purpose