A Bayesian alternative to mutual information for the hierarchical clustering of dependent random variables

The use of mutual information as a similarity measure in agglomerative hierarchical clustering (AHC) raises an important issue: some correction needs to be applied for the dimensionality of variables. In this work, we formulate the decision of merging dependent multivariate normal variables in an AHC procedure as a Bayesian model comparison. We found that the Bayesian formulation naturally shrinks the empirical covariance matrix towards a matrix set a priori (e.g., the identity), provides an automated stopping rule, and corrects for dimensionality using a term that scales up the measure as a function of the dimensionality of the variables. Also, the resulting log Bayes factor is asymptotically proportional to the plug-in estimate of mutual information, with an additive correction for dimensionality in agreement with the Bayesian information criterion. We investigated the behavior of these Bayesian alternatives (in exact and asymptotic forms) to mutual information on simulated and real data. An encouraging result was first derived on simulations: the hierarchical clustering based on the log Bayes factor outperformed off-the-shelf clustering techniques as well as raw and normalized mutual information in terms of classification accuracy. On a toy example, we found that the Bayesian approaches led to results that were similar to those of mutual information clustering techniques, with the advantage of an automated thresholding. On real functional magnetic resonance imaging (fMRI) datasets measuring brain activity, it identified clusters consistent with the established outcome of standard procedures. On this application, normalized mutual information had a highly atypical behavior, in the sense that it systematically favored very large clusters. These initial experiments suggest that the proposed Bayesian alternatives to mutual information are a useful new tool for hierarchical clustering

Mesoscopic modeling of hidden spiking neurons

Can we use spiking neural networks (SNN) as generative models of multi-neuronal recordings, while taking into account that most neurons are unobserved? Modeling the unobserved neurons with large pools of hidden spiking neurons leads to severely underconstrained problems that are hard to tackle with maximum likelihood estimation. In this work, we use coarse-graining and mean-field approximations to derive a bottom-up, neuronally-grounded latent variable model (neuLVM), where the activity of the unobserved neurons is reduced to a low-dimensional mesoscopic description. In contrast to previous latent variable models, neuLVM can be explicitly mapped to a recurrent, multi-population SNN, giving it a transparent biological interpretation. We show, on synthetic spike trains, that a few observed neurons are sufficient for neuLVM to perform efficient model inversion of large SNNs, in the sense that it can recover connectivity parameters, infer single-trial latent population activity, reproduce ongoing metastable dynamics, and generalize when subjected to perturbations mimicking optogenetic stimulation

Trial matching: capturing variability with data-constrained spiking neural networks

Simultaneous behavioral and electrophysiological recordings call for new methods to reveal the interactions between neural activity and behavior. A milestone would be an interpretable model of the co-variability of spiking activity and behavior across trials. Here, we model a cortical sensory-motor pathway in a tactile detection task with a large recurrent spiking neural network (RSNN), fitted to the recordings via gradient-based optimization. We focus specifically on the difficulty to match the trial-to-trial variability in the data. Our solution relies on optimal transport to define a distance between the distributions of generated and recorded trials. The technique is applied to artificial data and neural recordings covering six cortical areas. We find that the resulting RSNN can generate realistic cortical activity and predict jaw movements across the main modes of trial-to-trial variability. Our analysis also identifies an unexpected mode of variability in the data corresponding to task-irrelevant movements of the mouse.Comment: 11 pages of main text, 4 figures in main, 5 pages of appendix, 4 figures in appendi

Epidemiology of physician interventions in maritime environment by the Marseille Fire Brigade (BMPM) from 2005 to 2017

Background: Marseille is the second largest city in France. The Marseille Fire Brigade (BMPM) is the largest unity of the French Navy. This organization is in charge of rescue operations and medical intervention in the Marseille area. The aim of the study was to describe the epidemiology of interventions that required a physician to be present that were performed by the BMPM between the years of 2005 to 2017.  Materials and methods: The statistical office database of the BMPM and the medical interventions forms (FIM) acquired from the BMPM medical ambulances (SMUR) archives were analysed from the years 2005 to 2017.  Results: The BMPM performed a total of 2,375 interventions in the maritime environment between 2005 and 2017. A physician was necessary for intervention a total of 186 times. The extraction and analysis reports of 107 medical intervention forms found the BMPM archives revealed a significant number of interventions (67%) in the southern bay of Marseille and Frioul, specifically from the If and Planier islands. The majority of interventions (77%) took place within the 300m band. The most common cause of medical intervention was due to an accidental fall into the water, followed by boating (sailing and motor), and swimming. Drowning was the most common cause of mortality, consisting of 34% of all interventions. Diving accidents represented 14% of interventions. Trauma affected 22% of the study population and 83% of trauma patients were transported to the hospital under the supervision of a physician.  Conclusions: Potential areas for improvement in the management of drowning victims are the use of Szpilman’s classification, sonography, and non-invasive ventilation. A recertification course for medical education training of BMPM doctors on the management of diving accidents could help to optimize the information recorded on FIM. Accident prevention training should be continued and reinforced when it comes to maritime activities.