Synaptic and nonsynaptic plasticity approximating probabilistic inference

Learning and memory operations in neural circuits are believed to involve molecular cascades of synaptic and nonsynaptic changes that lead to a diverse repertoire of dynamical phenomena at higher levels of processing. Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability all conspire to form and maintain memories. But it is still unclear how these seemingly redundant mechanisms could jointly orchestrate learning in a more unified system. To this end, a Hebbian learning rule for spiking neurons inspired by Bayesian statistics is proposed. In this model, synaptic weights and intrinsic currents are adapted on-line upon arrival of single spikes, which initiate a cascade of temporally interacting memory traces that locally estimate probabilities associated with relative neuronal activation levels. Trace dynamics enable synaptic learning to readily demonstrate a spike-timing dependence, stably return to a set-point over long time scales, and remain competitive despite this stability. Beyond unsupervised learning, linking the traces with an external plasticity-modulating signal enables spike-based reinforcement learning. At the postsynaptic neuron, the traces are represented by an activity-dependent ion channel that is shown to regulate the input received by a postsynaptic cell and generate intrinsic graded persistent firing levels. We show how spike-based Hebbian-Bayesian learning can be performed in a simulated inference task using integrate-and-fire (IAF) neurons that are Poisson-firing and background-driven, similar to the preferred regime of cortical neurons. Our results support the view that neurons can represent information in the form of probability distributions, and that probabilistic inference could be a functional by-product of coupled synaptic and nonsynaptic mechanisms operating over several timescales. The model provides a biophysical realization of Bayesian computation by reconciling several observed neural phenomena whose functional effects are only partially understood in concert.This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission.QC 20150430BrainScaleSErasmus Mundus EuroSPI

Continuum Halos in Nearby Galaxies -- an EVLA Survey (CHANG-ES) -- I: Introduction to the Survey

We introduce a new survey to map the radio continuum halos of a sample of 35 edge-on spiral galaxies at 1.5 GHz and 6 GHz in all polarization products. The survey is exploiting the new wide bandwidth capabilities of the Karl G. Jansky Very Large Array (i.e. the Expanded Very Large Array, or EVLA) in a variety of array configurations (B, C, and D) in order to compile the most comprehensive data set yet obtained for the study of radio halo properties. This is the first survey of radio halos to include all polarization products. In this first paper, we outline the scientific motivation of the survey, the specific science goals, and the expected improvements in noise levels and spatial coverage from the survey. Our goals include investigating the physical conditions and origin of halos, characterizing cosmic ray transport and wind speed, measuring Faraday rotation and mapping the magnetic field, probing the in-disk and extraplanar far-infrared - radio continuum relation, and reconciling non-thermal radio emission with high-energy gamma-ray models. The sample size allows us to search for correlations between radio halos and other properties, including environment, star formation rate, and the presence of AGNs. In a companion paper (Paper II) we outline the data reduction steps and present the first results of the survey for the galaxy, NGC 4631.Comment: 17 pages, 1 figure, accepted to the Astronomical Journal, Version 2 changes: added acknowledgement to NRA

US Cosmic Visions: New Ideas in Dark Matter 2017: Community Report

This white paper summarizes the workshop "U.S. Cosmic Visions: New Ideas in Dark Matter" held at University of Maryland on March 23-25, 2017.Comment: 102 pages + reference

Dark sectors 2016 Workshop: community report

This report, based on the Dark Sectors workshop at SLAC in April 2016, summarizes the scientific importance of searches for dark sector dark matter and forces at masses beneath the weak-scale, the status of this broad international field, the important milestones motivating future exploration, and promising experimental opportunities to reach these milestones over the next 5-10 years

Large-Scale simulations of plastic neural networks on neuromorphic hardware

SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 2.0 × 104 neurons and 5.1 × 107 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately 45× more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models

The Bioinformatics Virtual Coordination Network: An open-source and interactive learning environment

Lockdowns and “stay-at-home” orders, starting in March 2020, shuttered bench and field dependent research across the world as a consequence of the global COVID-19 pandemic. The pandemic continues to have an impact on research progress and career development, especially for graduate students and early career researchers, as strict social distance limitations stifle ongoing research and impede in-person educational programs. The goal of the Bioinformatics Virtual Coordination Network (BVCN) was to reduce some of these impacts by helping research biologists learn new skills and initiate computational projects as alternative ways to carry out their research. The BVCN was founded in April 2020, at the peak of initial shutdowns, by an international group of early-career microbiology researchers with expertise in bioinformatics and computational biology. The BVCN instructors identified several foundational bioinformatic topics and organized hands-on tutorials through cloud-based platforms that had minimal hardware requirements (in order to maximize accessibility) such as RStudio Cloud and MyBinder. The major topics included the Unix terminal interface, R and Python programming languages, amplicon analysis, metagenomics, functional protein annotation, transcriptome analysis, network science, and population genetics and comparative genomics. The BVCN was structured as an open-access resource with a central hub providing access to all lesson content and hands-on tutorials (https://biovcnet.github.io/). As laboratories reopened and participants returned to previous commitments, the BVCN evolved: while the platform continues to enable “a la carte” lessons for learning computational skills, new and ongoing collaborative projects were initiated among instructors and participants, including a virtual, open-access bioinformatics conference in June 2021. In this manuscript we discuss the history, successes, and challenges of the BVCN initiative, highlighting how the lessons learned and strategies implemented may be applicable to the development and planning of future courses, workshops, and training programs

The Receptor Tyrosine Kinase Alk Controls Neurofibromin Functions in Drosophila Growth and Learning

Anaplastic Lymphoma Kinase (Alk) is a Receptor Tyrosine Kinase (RTK) activated in several cancers, but with largely unknown physiological functions. We report two unexpected roles for the Drosophila ortholog dAlk, in body size determination and associative learning. Remarkably, reducing neuronal dAlk activity increased body size and enhanced associative learning, suggesting that its activation is inhibitory in both processes. Consistently, dAlk activation reduced body size and caused learning deficits resembling phenotypes of null mutations in dNf1, the Ras GTPase Activating Protein-encoding conserved ortholog of the Neurofibromatosis type 1 (NF1) disease gene. We show that dAlk and dNf1 co-localize extensively and interact functionally in the nervous system. Importantly, genetic or pharmacological inhibition of dAlk rescued the reduced body size, adult learning deficits, and Extracellular-Regulated-Kinase (ERK) overactivation dNf1 mutant phenotypes. These results identify dAlk as an upstream activator of dNf1-regulated Ras signaling responsible for several dNf1 defects, and they implicate human Alk as a potential therapeutic target in NF1

Galaxy and Mass Assembly (GAMA): Variation in Galaxy Structure Across the Green Valley

Using a sample of 472 local Universe (z < 0.06) galaxies in the stellar mass range 10.25 < log M*/MG < 10.75, we explore the variation in galaxy structure as a function of morphology and galaxy colour. Our sample of galaxies is sub-divided into red, green and blue colour groups and into elliptical and non-elliptical (disk-type) morphologies. Using KiDS and VIKING derived postage stamp images, a group of eight volunteers visually classified bars, rings, morphological lenses, tidal streams, shells and signs of merger activity for all systems. We find a significant surplus of rings (2.3σ) and lenses (2.9σ) in disk-type galaxies as they transition across the green valley. Combined, this implies a joint ring/lens green valley surplus significance of 3.3σ relative to equivalent disk-types within either the blue cloud or the red sequence. We recover a bar fraction of ∼ 44% which remains flat with colour, however, we find that the presence of a bar acts to modulate the incidence of rings and (to a lesser extent) lenses, with rings in barred disk-type galaxies more common by ∼ 20 − 30 percentage points relative to their unbarred counterparts, regardless of colour. Additionally, green valley disk-type galaxies with a bar exhibit a significant 3.0σ surplus of lenses relative to their blue/red analogues. The existence of such structures rules out violent transformative events as the primary end-of-life evolutionary mechanism, with a more passive scenario the favoured candidate for the majority of galaxies rapidly transitioning across the green valley. Key words: galaxies: elliptical and lenticular, cD – galaxies: spiral – galaxies: evo- lution – galaxies: star formation – galaxies: statistics – galaxies: structur

Observation of D⁰ Meson Decays to Π⁺π⁻μ⁺μ⁻ and K⁺K⁻μ⁺μ⁻ Final States

The first observation of the D⁰→π⁺π⁻μ⁺μ⁻ and D⁰→K⁺K⁻μ⁺μ⁻ decays is reported using a sample of proton-proton collisions collected by LHCb at a center-of-mass energy of 8 TeV, and corresponding to 2  fb⁻¹ of integrated luminosity. The corresponding branching fractions are measured using as normalization the decay D⁰→K⁻π⁺[μ⁺μ⁻][subscript ρ⁰/ω], where the two muons are consistent with coming from the decay of a ρ⁰ or ω meson. The results are B(D⁰→π⁺π⁻μ⁺μ⁻)=(9.64±0.48±0.51±0.97)×10⁻⁷ and B(D⁰→K⁺K⁻μ⁺μ⁻)=(1.54±0.27±0.09±0.16)×10⁻⁷, where the uncertainties are statistical, systematic, and due to the limited knowledge of the normalization branching fraction. The dependence of the branching fraction on the dimuon mass is also investigated