@article{612, abstract = {Metabotropic GABAB receptors mediate slow inhibitory effects presynaptically and postsynaptically through the modulation of different effector signalling pathways. Here, we analysed the distribution of GABAB receptors using highly sensitive SDS-digested freeze-fracture replica labelling in mouse cerebellar Purkinje cells. Immunoreactivity for GABAB1 was observed on presynaptic and, more abundantly, on postsynaptic compartments, showing both scattered and clustered distribution patterns. Quantitative analysis of immunoparticles revealed a somato-dendritic gradient, with the density of immunoparticles increasing 26-fold from somata to dendritic spines. To understand the spatial relationship of GABAB receptors with two key effector ion channels, the G protein-gated inwardly rectifying K+ (GIRK/Kir3) channel and the voltage-dependent Ca2+ channel, biochemical and immunohistochemical approaches were performed. Co-immunoprecipitation analysis demonstrated that GABAB receptors co-assembled with GIRK and CaV2.1 channels in the cerebellum. Using double-labelling immunoelectron microscopic techniques, co-clustering between GABAB1 and GIRK2 was detected in dendritic spines, whereas they were mainly segregated in the dendritic shafts. In contrast, co-clustering of GABAB1 and CaV2.1 was detected in dendritic shafts but not spines. Presynaptically, although no significant co-clustering of GABAB1 and GIRK2 or CaV2.1 channels was detected, inter-cluster distance for GABAB1 and GIRK2 was significantly smaller in the active zone than in the dendritic shafts, and that for GABAB1 and CaV2.1 was significantly smaller in the active zone than in the dendritic shafts and spines. Thus, GABAB receptors are associated with GIRK and CaV2.1 channels in different subcellular compartments. These data provide a better framework for understanding the different roles played by GABAB receptors and their effector ion channels in the cerebellar network.}, author = {Luján, Rafael and Aguado, Carolina and Ciruela, Francisco and Cózar, Javier and Kleindienst, David and De La Ossa, Luis and Bettler, Bernhard and Wickman, Kevin and Watanabe, Masahiko and Shigemoto, Ryuichi and Fukazawa, Yugo}, journal = {Brain Structure and Function}, number = {3}, pages = {1565 -- 1587}, publisher = {Springer}, title = {{Differential association of GABAB receptors with their effector ion channels in Purkinje cells}}, doi = {10.1007/s00429-017-1568-y}, volume = {223}, year = {2018}, } @article{21, abstract = {Parvalbumin-positive (PV+) GABAergic interneurons in hippocampal microcircuits are thought to play a key role in several higher network functions, such as feedforward and feedback inhibition, network oscillations, and pattern separation. Fast lateral inhibition mediated by GABAergic interneurons may implement a winner-takes-all mechanism in the hippocampal input layer. However, it is not clear whether the functional connectivity rules of granule cells (GCs) and interneurons in the dentate gyrus are consistent with such a mechanism. Using simultaneous patch-clamp recordings from up to seven GCs and up to four PV+ interneurons in the dentate gyrus, we find that connectivity is structured in space, synapse-specific, and enriched in specific disynaptic motifs. In contrast to the neocortex, lateral inhibition in the dentate gyrus (in which a GC inhibits neighboring GCs via a PV+ interneuron) is ~ 10-times more abundant than recurrent inhibition (in which a GC inhibits itself). Thus, unique connectivity rules may enable the dentate gyrus to perform specific higher-order computations}, author = {Espinoza Martinez, Claudia and Guzmán, José and Zhang, Xiaomin and Jonas, Peter M}, journal = {Nature Communications}, number = {1}, publisher = {Nature Publishing Group}, title = {{Parvalbumin+ interneurons obey unique connectivity rules and establish a powerful lateral-inhibition microcircuit in dentate gyrus}}, doi = {10.1038/s41467-018-06899-3}, volume = {9}, year = {2018}, } @inproceedings{66, abstract = {Crypto-currencies are digital assets designed to work as a medium of exchange, e.g., Bitcoin, but they are susceptible to attacks (dishonest behavior of participants). A framework for the analysis of attacks in crypto-currencies requires (a) modeling of game-theoretic aspects to analyze incentives for deviation from honest behavior; (b) concurrent interactions between participants; and (c) analysis of long-term monetary gains. Traditional game-theoretic approaches for the analysis of security protocols consider either qualitative temporal properties such as safety and termination, or the very special class of one-shot (stateless) games. However, to analyze general attacks on protocols for crypto-currencies, both stateful analysis and quantitative objectives are necessary. In this work our main contributions are as follows: (a) we show how a class of concurrent mean-payo games, namely ergodic games, can model various attacks that arise naturally in crypto-currencies; (b) we present the first practical implementation of algorithms for ergodic games that scales to model realistic problems for crypto-currencies; and (c) we present experimental results showing that our framework can handle games with thousands of states and millions of transitions.}, author = {Chatterjee, Krishnendu and Goharshady, Amir and Ibsen-Jensen, Rasmus and Velner, Yaron}, isbn = {978-3-95977-087-3}, location = {Beijing, China}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik}, title = {{Ergodic mean-payoff games for the analysis of attacks in crypto-currencies}}, doi = {10.4230/LIPIcs.CONCUR.2018.11}, volume = {118}, year = {2018}, } @inproceedings{311, abstract = {Smart contracts are computer programs that are executed by a network of mutually distrusting agents, without the need of an external trusted authority. Smart contracts handle and transfer assets of considerable value (in the form of crypto-currency like Bitcoin). Hence, it is crucial that their implementation is bug-free. We identify the utility (or expected payoff) of interacting with such smart contracts as the basic and canonical quantitative property for such contracts. We present a framework for such quantitative analysis of smart contracts. Such a formal framework poses new and novel research challenges in programming languages, as it requires modeling of game-theoretic aspects to analyze incentives for deviation from honest behavior and modeling utilities which are not specified as standard temporal properties such as safety and termination. While game-theoretic incentives have been analyzed in the security community, their analysis has been restricted to the very special case of stateless games. However, to analyze smart contracts, stateful analysis is required as it must account for the different program states of the protocol. Our main contributions are as follows: we present (i)~a simplified programming language for smart contracts; (ii)~an automatic translation of the programs to state-based games; (iii)~an abstraction-refinement approach to solve such games; and (iv)~experimental results on real-world-inspired smart contracts.}, author = {Chatterjee, Krishnendu and Goharshady, Amir and Velner, Yaron}, location = {Thessaloniki, Greece}, pages = {739 -- 767}, publisher = {Springer}, title = {{Quantitative analysis of smart contracts}}, doi = {10.1007/978-3-319-89884-1_26}, volume = {10801}, year = {2018}, } @inproceedings{6340, abstract = {We present a secure approach for maintaining andreporting credit history records on the Blockchain. Our ap-proach removes third-parties such as credit reporting agen-cies from the lending process and replaces them with smartcontracts. This allows customers to interact directly with thelenders or banks while ensuring the integrity, unmalleabilityand privacy of their credit data. Additionally, each customerhas full control over complete or selective disclosure of hercredit records, eliminating the risk of privacy violations or databreaches. Moreover, our approach provides strong guaranteesfor the lenders as well. A lender can check both correctness andcompleteness of the credit data disclosed to her. This is the firstapproach that can perform all credit reporting tasks withouta central authority or changing the financial mechanisms*.}, author = {Goharshady, Amir Kafshdar and Behrouz, Ali and Chatterjee, Krishnendu}, booktitle = {Proceedings of the IEEE International Conference on Blockchain}, isbn = {978-1-5386-7975-3 }, location = {Halifax, Canada}, pages = {1343--1348}, publisher = {IEEE}, title = {{Secure Credit Reporting on the Blockchain}}, doi = {10.1109/Cybermatics_2018.2018.00231}, year = {2018}, }