Accelerated physical emulation of Bayesian inference in spiking neural networks

The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices. In particular, it may hold the key to overcoming the von Neumann bottleneck that limits contemporary computer architectures. Physical-model neuromorphic devices seek to replicate not only this inherent parallelism, but also aspects of its microscopic dynamics in analog circuits emulating neurons and synapses. However, these machines require network models that are not only adept at solving particular tasks, but that can also cope with the inherent imperfections of analog substrates. We present a spiking network model that performs Bayesian inference through sampling on the BrainScaleS neuromorphic platform, where we use it for generative and discriminative computations on visual data. By illustrating its functionality on this platform, we implicitly demonstrate its robustness to various substrate-specific distortive effects, as well as its accelerated capability for computation. These results showcase the advantages of brain-inspired physical computation and provide important building blocks for large-scale neuromorphic applications.Comment: This preprint has been published 2019 November 14. Please cite as: Kungl A. F. et al. (2019) Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks. Front. Neurosci. 13:1201. doi: 10.3389/fnins.2019.0120

Coherent spin dynamics of rare-earth doped crystals in the high-cooperativity regime

Rare-earth doped crystals have long coherence times and the potential to provide quantum interfaces between microwave and optical photons. Such applications benefit from a high cooperativity between the spin ensemble and a microwave cavity -- this motivates an increase in the rare earth ion concentration which in turn impacts the spin coherence lifetime. We measure spin dynamics of two rare-earth spin species, $^{145}$Nd and Yb doped into Y$_{2}$SiO$_{5}$, coupled to a planar microwave resonator in the high cooperativity regime, in the temperature range 1.2 K to 14 mK. We identify relevant decoherence mechanisms including instantaneous diffusion arising from resonant spins and temperature-dependent spectral diffusion from impurity electron and nuclear spins in the environment. We explore two methods to mitigate the effects of spectral diffusion in the Yb system in the low-temperature limit, first, using magnetic fields of up to 1 T to suppress impurity spin dynamics and, second, using transitions with low effective g-factors to reduce sensitivity to such dynamics. Finally, we demonstrate how the `clock transition' present in the $^{171}$Yb system at zero field can be used to increase coherence times up to $T_{2} = 6(1)$ ms.Comment: 8 pages, 5 figure

Competition and moral behavior: A meta-analysis of forty-five crowd-sourced experimental designs

Does competition affect moral behavior? This fundamental question has been debated among leading scholars for centuries, and more recently, it has been tested in experimental studies yielding a body of rather inconclusive empirical evidence. A potential source of ambivalent empirical results on the same hypothesis is design heterogeneity—variation in true effect sizes across various reasonable experimental research protocols. To provide further evidence on whether competition affects moral behavior and to examine whether the generalizability of a single experimental study is jeopardized by design heterogeneity, we invited independent research teams to contribute experimental designs to a crowd-sourced project. In a large-scale online data collection, 18,123 experimental participants were randomly allocated to 45 randomly selected experimental designs out of 95 submitted designs. We find a small adverse effect of competition on moral behavior in a meta-analysis of the pooled data. The crowd-sourced design of our study allows for a clean identification and estimation of the variation in effect sizes above and beyond what could be expected due to sampling variance. We find substantial design heterogeneity—estimated to be about 1.6 times as large as the average standard error of effect size estimates of the 45 research designs—indicating that the informativeness and generalizability of results based on a single experimental design are limited. Drawing strong conclusions about the underlying hypotheses in the presence of substantive design heterogeneity requires moving toward much larger data collections on various experimental designs testing the same hypothesis

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

EXTENDED GAUSSIAN IMAGES FOR THE REGISTRATION OF TERRESTRIAL SCAN DATA

Terrestrial laser scanning instruments are coming more and more into operation. Up to now vendors of laser scanners mainly use manual or semi-automated registration techniques combined with artificial targets to register single scans. The automatic matching of multiple scans without additional targets is still a research topic in the field of terrestrial laser scanning. Many different proposals and algorithms have already been presented to solve this task. Nevertheless, a strong demand remains for fast and robust algorithms for the registration of multiple scans. The problem is difficult to solve and the process is often divided into two stages. First a coarse matching is done in order to determine a pre-alignment of the scanned surfaces. Then a fine matching algorithm is used to achieve more accurate results. Robust algorithms like the well known iterative closest point (ICP) algorithm and lots of variants already exist for the fine matching, whereas the existing methods for the pre-alignment of the scan data are often rudimental and limited. In this paper a proposal for automatic registration of terrestrial laser scanning data using extended Gaussian images is introduced. The method is placed in the coarse matching stage of the registration process and can be used to determine the rotation component between different scan positions. Therefore normal vectors of local or segmented planes of the input data are used.

ANALYSIS OF SCORE FUNCTIONS FOR THE AUTOMATIC REGISTRATION OF TERRESTRIAL LASER SCANS

e.g. for surveying purposes, cultural heritage, city modelling or architectural applications. Usually different points of view are necessary to acquire an object completely. The task of finding the relative orientation between several scan positions is termed as registration. Research work in data driven methods for the registration of point clouds was frequently published the last decade. A distinction between coarse registration and fine registration has been drawn. We use a featurebased registration to search for a coarse orientation. The transformation parameters are calculated by establishing correspondences between extracted planes. An efficient search strategy delivers possible candidates. The focus of this paper lies in the verification phase that is responsible for picking the correct solution. A score function is used to evaluate the candidates of probabilistic solutions for the searched transformation. The goal of a score function is to get a reliable indication, which solution is best. Ideally, the score function attains its maximum for the correct solution and is significantly larger than the score of any wrong solution.