Connectivity reflects coding: A model of voltage-based spike-timing-dependent-plasticity with homeostasis

Electrophysiological connectivity patterns in cortex often show a few strong connections in a sea of weak connections. In some brain areas a large fraction of strong connections are bidirectional, in others they are mainly unidirectional. In order to explain these connectivity patterns, we use a model of Spike-Timing-Dependent Plasticity where synaptic changes depend on presynaptic spike arrival and the postsynaptic membrane potential. The model describes several nonlinear effects in STDP experiments, as well as the voltage dependence of plasticity under voltage clamp and classical paradigms of LTP/LTD induction. We show that in a simulated recurrent network of spiking neurons our plasticity rule leads not only to receptive field development, but also to connectivity patterns that reflect the neural code: for temporal coding paradigms strong connections are predominantly unidirectional, whereas they are bidirectional under rate coding. Thus variable connectivity patterns in the brain could reflect different coding principles across brain areas

Using the River Ecosystem Service Index to evaluate “Free Moving Rivers” restoration measures: A case study on the Ammer river (Bavaria)

Restoring natural fluvial dynamics is fundamental for sustaining biodiversity and functional integrity of river and floodplain ecosystems. In Central Europe, however, pervasive river regulation and bank protection have greatly impaired ecosystem functioning and many water bodies fail to achieve a good ecological status within the European Water Framework Directive. The “Free Moving Rivers” approach seeks to restore the ecological integrity of rivers and floodplains by creating appropriate conditions for natural fluvial dynamics. Principal goals of the approach include removing artificial constraints on river processes and expanding the river corridor to restore natural river habitats and structures. Lacking, however, are complementary tools that evaluate and predict changes to ecosystem services (ESSs) after implementation. Here, we describe a case study of the Ammer river in Bavaria, Germany, to (i) calculate the extent of the “Free Moving Rivers” corridor, and (ii) assess changes to ESSs of a proposed river restoration measure under two alternative land-use scenarios. To do this, we apply the River Ecosystem Service Index (RESI), whereby individual ESSs are assessed in a spatially explicit way. We show how a proposed implementation of the “Free Moving Rivers” approach enhances three investigated ESSs: flood retention, sediment balance and habitat provision. We conclude that RESI is a potentially useful tool with wide applicability for restoration planning that synthesises floodplain complexity in such a way that facilitates decision making

First-in-Human Phase I Study to Evaluate the Brain-Penetrant PI3K/mTOR Inhibitor GDC-0084 in Patients with Progressive or Recurrent High-Grade Glioma.

PurposeGDC-0084 is an oral, brain-penetrant small-molecule inhibitor of PI3K and mTOR. A first-in-human, phase I study was conducted in patients with recurrent high-grade glioma.Patients and methodsGDC-0084 was administered orally, once daily, to evaluate safety, pharmacokinetics (PK), and activity. Fluorodeoxyglucose-PET (FDG-PET) was performed to measure metabolic responses.ResultsForty-seven heavily pretreated patients enrolled in eight cohorts (2-65 mg). Dose-limiting toxicities included 1 case of grade 2 bradycardia and grade 3 myocardial ischemia (15 mg), grade 3 stomatitis (45 mg), and 2 cases of grade 3 mucosal inflammation (65 mg); the MTD was 45 mg/day. GDC-0084 demonstrated linear and dose-proportional PK, with a half-life (∼19 hours) supportive of once-daily dosing. At 45 mg/day, steady-state concentrations exceeded preclinical target concentrations producing antitumor activity in xenograft models. FDG-PET in 7 of 27 patients (26%) showed metabolic partial response. At doses ≥45 mg/day, a trend toward decreased median standardized uptake value in normal brain was observed, suggesting central nervous system penetration of drug. In two resection specimens, GDC-0084 was detected at similar levels in tumor and brain tissue, with a brain tissue/tumor-to-plasma ratio of >1 and >0.5 for total and free drug, respectively. Best overall response was stable disease in 19 patients (40%) and progressive disease in 26 patients (55%); 2 patients (4%) were nonevaluable.ConclusionsGDC-0084 demonstrated classic PI3K/mTOR-inhibitor related toxicities. FDG-PET and concentration data from brain tumor tissue suggest that GDC-0084 crossed the blood-brain barrier

Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression

Changes in synaptic efficacies need to be long-lasting in order to serve as a substrate for memory. Experimentally, synaptic plasticity exhibits phases covering the induction of long-term potentiation and depression (LTP/LTD) during the early phase of synaptic plasticity, the setting of synaptic tags, a trigger process for protein synthesis, and a slow transition leading to synaptic consolidation during the late phase of synaptic plasticity. We present a mathematical model that describes these different phases of synaptic plasticity. The model explains a large body of experimental data on synaptic tagging and capture, cross-tagging, and the late phases of LTP and LTD. Moreover, the model accounts for the dependence of LTP and LTD induction on voltage and presynaptic stimulation frequency. The stabilization of potentiated synapses during the transition from early to late LTP occurs by protein synthesis dynamics that are shared by groups of synapses. The functional consequence of this shared process is that previously stabilized patterns of strong or weak synapses onto the same postsynaptic neuron are well protected against later changes induced by LTP/LTD protocols at individual synapses

Democratic population decisions result in robust policy-gradient learning: A parametric study with GPU simulations

High performance computing on the Graphics Processing Unit (GPU) is an emerging field driven by the promise of high computational power at a low cost. However, GPU programming is a non-trivial task and moreover architectural limitations raise the question of whether investing effort in this direction may be worthwhile. In this work, we use GPU programming to simulate a two-layer network of Integrate-and-Fire neurons with varying degrees of recurrent connectivity and investigate its ability to learn a simplified navigation task using a policy-gradient learning rule stemming from Reinforcement Learning. The purpose of this paper is twofold. First, we want to support the use of GPUs in the field of Computational Neuroscience. Second, using GPU computing power, we investigate the conditions under which the said architecture and learning rule demonstrate best performance. Our work indicates that networks featuring strong Mexican-Hat-shaped recurrent connections in the top layer, where decision making is governed by the formation of a stable activity bump in the neural population (a "non-democratic" mechanism), achieve mediocre learning results at best. In absence of recurrent connections, where all neurons "vote" independently ("democratic") for a decision via population vector readout, the task is generally learned better and more robustly. Our study would have been extremely difficult on a desktop computer without the use of GPU programming. We present the routines developed for this purpose and show that a speed improvement of 5x up to 42x is provided versus optimised Python code. The higher speed is achieved when we exploit the parallelism of the GPU in the search of learning parameters. This suggests that efficient GPU programming can significantly reduce the time needed for simulating networks of spiking neurons, particularly when multiple parameter configurations are investigated. © 2011 Richmond et al

Assessing land use and flood management impacts on ecosystem services in a river landscape (Upper Danube, Germany)

Rivers and floodplains provide many regulating, provisioning and cultural ecosystem services (ES) such as flood risk regulation, crop production or recreation. Intensive use of resources such as hydropower production, construction of detention basins and intensive agriculture substantially change ecosystems and may affect their capacity to provide ES. Legal frameworks such as the European Water Framework Directive, Bird and Habitats Directive and Floods Directive already address various uses and interests. However, management is still sectoral and often potential synergies or trade‐offs between sectors are not considered. The ES concept could support a joint and holistic evaluation of impacts and proactively suggest advantageous options. The river ecosystem service index (RESI) method evaluates the capacity of floodplains to provide ES by using a standardized five‐point scale for 1 km‐floodplain segments based on available spatial data. This scaling allows consistent scoring of all ES and their integration into a single index. The aim of this article is to assess ES impacts of different flood prevention scenarios on a 75 km section of the Danube river corridor in Germany. The RESI method was applied to evaluate scenario effects on 13 ES with the standardized five‐point scale. Synergies and trade‐offs were identified as well as ES bundles and dependencies on land use and connectivity. The ratio of actual and former floodplain has the strongest influence on the total ES provision: the higher the percentage and area of an active floodplain, the higher the sum of ES. The RESI method proved useful to support decision‐making in regional planning.BMBF, 033W024A, ReWaM - Verbundprojekt RESI: River Ecosystem Service Index, Teilprojekt

Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons

Genome-wide association meta-analyses and fine-mapping elucidate pathways influencing albuminuria

Abstract: Increased levels of the urinary albumin-to-creatinine ratio (UACR) are associated with higher risk of kidney disease progression and cardiovascular events, but underlying mechanisms are incompletely understood. Here, we conduct trans-ethnic (n = 564,257) and European-ancestry specific meta-analyses of genome-wide association studies of UACR, including ancestry- and diabetes-specific analyses, and identify 68 UACR-associated loci. Genetic correlation analyses and risk score associations in an independent electronic medical records database (n = 192,868) reveal connections with proteinuria, hyperlipidemia, gout, and hypertension. Fine-mapping and trans-Omics analyses with gene expression in 47 tissues and plasma protein levels implicate genes potentially operating through differential expression in kidney (including TGFB1, MUC1, PRKCI, and OAF), and allow coupling of UACR associations to altered plasma OAF concentrations. Knockdown of OAF and PRKCI orthologs in Drosophila nephrocytes reduces albumin endocytosis. Silencing fly PRKCI further impairs slit diaphragm formation. These results generate a priority list of genes and pathways for translational research to reduce albuminuria

Genome-wide association meta-analyses and fine-mapping elucidate pathways influencing albuminuria

Publisher Copyright: © 2019, The Author(s).Increased levels of the urinary albumin-to-creatinine ratio (UACR) are associated with higher risk of kidney disease progression and cardiovascular events, but underlying mechanisms are incompletely understood. Here, we conduct trans-ethnic (n = 564,257) and European-ancestry specific meta-analyses of genome-wide association studies of UACR, including ancestry- and diabetes-specific analyses, and identify 68 UACR-associated loci. Genetic correlation analyses and risk score associations in an independent electronic medical records database (n = 192,868) reveal connections with proteinuria, hyperlipidemia, gout, and hypertension. Fine-mapping and trans-Omics analyses with gene expression in 47 tissues and plasma protein levels implicate genes potentially operating through differential expression in kidney (including TGFB1, MUC1, PRKCI, and OAF), and allow coupling of UACR associations to altered plasma OAF concentrations. Knockdown of OAF and PRKCI orthologs in Drosophila nephrocytes reduces albumin endocytosis. Silencing fly PRKCI further impairs slit diaphragm formation. These results generate a priority list of genes and pathways for translational research to reduce albuminuria.Peer reviewe