700 research outputs found

    A reliable order-statistics-based approximate nearest neighbor search algorithm

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    We propose a new algorithm for fast approximate nearest neighbor search based on the properties of ordered vectors. Data vectors are classified based on the index and sign of their largest components, thereby partitioning the space in a number of cones centered in the origin. The query is itself classified, and the search starts from the selected cone and proceeds to neighboring ones. Overall, the proposed algorithm corresponds to locality sensitive hashing in the space of directions, with hashing based on the order of components. Thanks to the statistical features emerging through ordering, it deals very well with the challenging case of unstructured data, and is a valuable building block for more complex techniques dealing with structured data. Experiments on both simulated and real-world data prove the proposed algorithm to provide a state-of-the-art performance

    Localist representation can improve efficiency for detection and counting

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    Almost all representations have both distributed and localist aspects, depending upon what properties of the data are being considered. With noisy data, features represented in a localist way can be detected very efficiently, and in binary representations they can be counted more efficiently than those represented in a distributed way. Brains operate in noisy environments, so the localist representation of behaviourally important events is advantageous, and fits what has been found experimentally. Distributed representations require more neurons to perform as efficiently, but they do have greater versatility

    Simulation of networks of spiking neurons: A review of tools and strategies

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    We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of Computational Neuroscience, in press (2007

    Semi-Supervised Learning of Cartesian Factors

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    The existence of place cells (PCs), grid cells (GCs), border cells (BCs), and head direction cells (HCs) as well as the dependencies between them have been enigmatic. We make an effort to explain their nature by introducing the concept of Cartesian Factors. These factors have specific properties: (i) they assume and complement each other, like direction and position and (ii) they have localized discrete representations with predictive attractors enabling implicit metric-like computations. In our model, HCs make the distributed and local representation of direction. Predictive attractor dynamics on that network forms the Cartesian Factor "direction." We embed these HCs and idiothetic visual information into a semi-supervised sparse autoencoding comparator structure that compresses its inputs and learns PCs, the distributed local and direction independent (allothetic) representation of the Cartesian Factor of global space. We use a supervised, information compressing predictive algorithm and form direction sensitive (oriented) GCs from the learned PCs by means of an attractor-like algorithm. Since the algorithm can continue the grid structure beyond the region of the PCs, i.e.,beyond its learning domain, thus the GCs and the PCs together form our metric-like Cartesian Factors of space. We also stipulate that the same algorithm can produce BCs. Our algorithm applies (a) a bag representation that models the "what system" and (b) magnitude ordered place cell activities that model either the integrate-and-fire mechanism, or theta phase precession, or both. We relate the components of the algorithm to the entorhinal-hippocampal complex and to its working. The algorithm requires both spatial and lifetime sparsification that may gain support from the two-stage memory formation of this complex

    Global Priorities for Conserving the Evolutionary History of Sharks, Rays, and Chimaeras

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    In an era of accelerated biodiversity loss and limited conservation resources, systematic prioritization of species and places is essential. In terrestrial vertebrates, evolutionary distinctness has been used to identify species and locations that embody the greatest share of evolutionary history. We estimate evolutionary distinctness for a large marine vertebrate radiation on a dated taxon-complete tree for all 1,192 chondrichthyan fishes (sharks, rays and chimaeras) by augmenting a new 610-species molecular phylogeny using taxonomic constraints. Chondrichthyans are by far the most evolutionarily distinct of all major radiations of jawed vertebrates—the average species embodies 26 million years of unique evolutionary history. With this metric, we identify 21 countries with the highest richness, endemism and evolutionary distinctness of threatened species as targets for conservation prioritization. On average, threatened chondrichthyans are more evolutionarily distinct—further motivating improved conservation, fisheries management and trade regulation to avoid significant pruning of the chondrichthyan tree of life

    Intelligent Reflecting Surfaces in Wireless Communication Systems

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    Efficient Algorithms for Coastal Geographic Problems

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    The increasing performance of computers has made it possible to solve algorithmically problems for which manual and possibly inaccurate methods have been previously used. Nevertheless, one must still pay attention to the performance of an algorithm if huge datasets are used or if the problem iscomputationally difficult. Two geographic problems are studied in the articles included in this thesis. In the first problem the goal is to determine distances from points, called study points, to shorelines in predefined directions. Together with other in-formation, mainly related to wind, these distances can be used to estimate wave exposure at different areas. In the second problem the input consists of a set of sites where water quality observations have been made and of the results of the measurements at the different sites. The goal is to select a subset of the observational sites in such a manner that water quality is still measured in a sufficient accuracy when monitoring at the other sites is stopped to reduce economic cost. Most of the thesis concentrates on the first problem, known as the fetch length problem. The main challenge is that the two-dimensional map is represented as a set of polygons with millions of vertices in total and the distances may also be computed for millions of study points in several directions. Efficient algorithms are developed for the problem, one of them approximate and the others exact except for rounding errors. The solutions also differ in that three of them are targeted for serial operation or for a small number of CPU cores whereas one, together with its further developments, is suitable also for parallel machines such as GPUs.Tietokoneiden suorituskyvyn kasvaminen on tehnyt mahdolliseksi ratkaista algoritmisesti ongelmia, joita on aiemmin tarkasteltu paljon ihmistyötä vaativilla, mahdollisesti epätarkoilla, menetelmillä. Algoritmien suorituskykyyn on kuitenkin toisinaan edelleen kiinnitettävä huomiota lähtömateriaalin suuren määrän tai ongelman laskennallisen vaikeuden takia. Väitöskirjaansisältyvissäartikkeleissatarkastellaankahtamaantieteellistä ongelmaa. Ensimmäisessä näistä on määritettävä etäisyyksiä merellä olevista pisteistä lähimpään rantaviivaan ennalta määrätyissä suunnissa. Etäisyyksiä ja tuulen voimakkuutta koskevien tietojen avulla on mahdollista arvioida esimerkiksi aallokon voimakkuutta. Toisessa ongelmista annettuna on joukko tarkkailuasemia ja niiltä aiemmin kerättyä tietoa erilaisista vedenlaatua kuvaavista parametreista kuten sameudesta ja ravinteiden määristä. Tehtävänä on valita asemajoukosta sellainen osa joukko, että vedenlaatua voidaan edelleen tarkkailla riittävällä tarkkuudella, kun mittausten tekeminen muilla havaintopaikoilla lopetetaan kustannusten säästämiseksi. Väitöskirja keskittyy pääosin ensimmäisen ongelman, suunnattujen etäisyyksien, ratkaisemiseen. Haasteena on se, että tarkasteltava kaksiulotteinen kartta kuvaa rantaviivan tyypillisesti miljoonista kärkipisteistä koostuvana joukkonapolygonejajaetäisyyksiäonlaskettavamiljoonilletarkastelupisteille kymmenissä eri suunnissa. Ongelmalle kehitetään tehokkaita ratkaisutapoja, joista yksi on likimääräinen, muut pyöristysvirheitä lukuun ottamatta tarkkoja. Ratkaisut eroavat toisistaan myös siinä, että kolme menetelmistä on suunniteltu ajettavaksi sarjamuotoisesti tai pienellä määrällä suoritinytimiä, kun taas yksi menetelmistä ja siihen tehdyt parannukset soveltuvat myös voimakkaasti rinnakkaisille laitteille kuten GPU:lle. Vedenlaatuongelmassa annetulla asemajoukolla on suuri määrä mahdollisia osajoukkoja. Lisäksi tehtävässä käytetään aikaa vaativia operaatioita kuten lineaarista regressiota, mikä entisestään rajoittaa sitä, kuinka monta osajoukkoa voidaan tutkia. Ratkaisussa käytetäänkin heuristiikkoja, jotkaeivät välttämättä tuota optimaalista lopputulosta.Siirretty Doriast

    Contribution of Cerebellar Sensorimotor Adaptation to Hippocampal Spatial Memory

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    Complementing its primary role in motor control, cerebellar learning has also a bottom-up influence on cognitive functions, where high-level representations build up from elementary sensorimotor memories. In this paper we examine the cerebellar contribution to both procedural and declarative components of spatial cognition. To do so, we model a functional interplay between the cerebellum and the hippocampal formation during goal-oriented navigation. We reinterpret and complete existing genetic behavioural observations by means of quantitative accounts that cross-link synaptic plasticity mechanisms, single cell and population coding properties, and behavioural responses. In contrast to earlier hypotheses positing only a purely procedural impact of cerebellar adaptation deficits, our results suggest a cerebellar involvement in high-level aspects of behaviour. In particular, we propose that cerebellar learning mechanisms may influence hippocampal place fields, by contributing to the path integration process. Our simulations predict differences in place-cell discharge properties between normal mice and L7-PKCI mutant mice lacking long-term depression at cerebellar parallel fibre-Purkinje cell synapses. On the behavioural level, these results suggest that, by influencing the accuracy of hippocampal spatial codes, cerebellar deficits may impact the exploration-exploitation balance during spatial navigation
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