Representational dissimilarity metric spaces for stochastic neural networks

Quantifying similarity between neural representations -- e.g. hidden layer activation vectors -- is a perennial problem in deep learning and neuroscience research. Existing methods compare deterministic responses (e.g. artificial networks that lack stochastic layers) or averaged responses (e.g., trial-averaged firing rates in biological data). However, these measures of deterministic representational similarity ignore the scale and geometric structure of noise, both of which play important roles in neural computation. To rectify this, we generalize previously proposed shape metrics (Williams et al. 2021) to quantify differences in stochastic representations. These new distances satisfy the triangle inequality, and thus can be used as a rigorous basis for many supervised and unsupervised analyses. Leveraging this novel framework, we find that the stochastic geometries of neurobiological representations of oriented visual gratings and naturalistic scenes respectively resemble untrained and trained deep network representations. Further, we are able to more accurately predict certain network attributes (e.g. training hyperparameters) from its position in stochastic (versus deterministic) shape space

A microRNA program regulates the balance between cardiomyocyte hyperplasia and hypertrophy and stimulates cardiac regeneration

Myocardial regeneration is restricted to early postnatal life, when mammalian cardiomyocytes still retain the ability to proliferate. The molecular cues that induce cell cycle arrest of neonatal cardiomyocytes towards terminally differentiated adult heart muscle cells remain obscure. Here we report that the miR-106b~25 cluster is higher expressed in the early postnatal myocardium and decreases in expression towards adulthood, especially under conditions of overload, and orchestrates the transition of cardiomyocyte hyperplasia towards cell cycle arrest and hypertrophy by virtue of its targetome. In line, gene delivery of miR-106b~25 to the mouse heart provokes cardiomyocyte proliferation by targeting a network of negative cell cycle regulators including E2f5, Cdkn1c, Ccne1 and Wee1. Conversely, gene-targeted miR-106b~25 null mice display spontaneous hypertrophic remodeling and exaggerated remodeling to overload by derepression of the prohypertrophic transcription factors Hand2 and Mef2d. Taking advantage of the regulatory function of miR-106b~25 on cardiomyocyte hyperplasia and hypertrophy, viral gene delivery of miR-106b~25 provokes nearly complete regeneration of the adult myocardium after ischemic injury. Our data demonstrate that exploitation of conserved molecular programs can enhance the regenerative capacity of the injured heart.E.D. is supported by a VENI award 916-150-16 from the Netherlands Organization for Health Research and Development (ZonMW), an EMBO Long-term Fellowship (EMBO ALTF 848-2013) and a FP7 Marie Curie Intra-European Fellowship (Project number 627539). V.S.P. was funded by a fellowship from the FCT/ Ministério da Ciência, Tec-nologia e Inovação SFRH/BD/111799/2015. P.D.C.M. is an Established Investigator of the Dutch Heart Foundation. L.D.W. acknowledges support from the Dutch CardioVascular Alliance (ARENA-PRIME). L.D.W. was further supported by grant 311549 from the European Research Council (ERC), a VICI award 918-156-47 from the Dutch Research Council and Marie Sklodowska-Curie grant agreement no. 813716 (TRAIN-HEART)

Simulation of the fluid structure interaction for an aerostatic bearing and a flexible substrate

The fluid structure interaction for an aerostatic bearing and a substrate is solved numerically by a semi-analytical model, programmed in the software package MATLAB. This semi-analytical model uses a fluidic network of resistances and capacities to solve the pressure field in the bearing channel. These pressures are sent to a mechanical module, which computes the substrate deformations by the direct stiffness method. This semi-analytical model is verified by a second model, built into the commercial software package ANSYS. The ANSYS model includes a two-way coupled FEM and FVM solver. Position and time-dependent bearing height variations are computed by means of a dynamic mesh. The implementation of the semi-analytical model is done and verified by three cases. The first case verifies the pressure profile inside a parallel plate configuration with a moving top wall. The last two cases verify the time-dependent position of a rigid and flexible substrate supported by an aerostatic bearing. The semianalytical model is proved to be an effective tool for aerostatic bearing design, since it is able to solve the FSI within a couple of minutes instead of days for a coupled FEM and FVM solver

The use of a stochastic LGD in a credit default economic capital framework

status: publishe