1,215 research outputs found

    Eigenvalue Spectra of Functional Networks in fMRI Data and Artificial Models

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    Full paper available at Springerlink: http://link.springer.com/chapter/10.1007%2F978-3-642-38658-9_19In this work we provide a spectral comparison of functional networks in fMRI data of brain activity and artificial energy-based neural model. The spectra (set of eigenvalues of the graph adjacency matrix) of both networks turn out to obey similar decay rate and characteristic power-law scaling in their middle parts. This extends the set of statistics, which are already confirmed to be similar for both neural models and medical data, by the graph spectrum

    Diameter of the spike-flow graphs of geometrical neural networks

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    Full article is available at Springerlink: http://link.springer.com/chapter/10.1007%2F978-3-642-31464-3_52 DOI: 10.1007/978-3-642-31464-3_52Average path length is recognised as one of the vital characteristics of random graphs and complex networks. Despite a rather sparse structure, some cases were reported to have a relatively short lengths between every pair of nodes, making the whole network available in just several hops. This small-worldliness was reported in metabolic, social or linguistic networks and recently in the Internet. In this paper we present results concerning path length distribution and the diameter of the spike-flow graph obtained from dynamics of geometrically embedded neural networks. Numerical results confirm both short diameter and average path length of resulting activity graph. In addition to numerical results, we also discuss means of running simulations in a concurrent environment

    Spectra of the Spike-Flow Graphs in Geometrically Embedded Neural Networks

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    Full article available at Springerlink: http://link.springer.com/chapter/10.1007%2F978-3-642-29347-4_17 DOI: 10.1007/978-3-642-29347-4_17In this work we study a simplified model of a neural activity flow in networks, whose connectivity is based on geometrical embedding, rather than being lattices or fully connected graphs. We present numerical results showing that as the spectrum (set of eigenvalues of adjacency matrix) of the resulting activity-based network develops a scale-free dependency. Moreover it strengthens and becomes valid for a wider segment along with the simulation progress, which implies a highly organised structure of the analysed graph

    Scale-freeness and small-world phenomenon in information-flow graphs of geometrical neural networks

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    In this dissertation we set out to study a simplified model of activation flow in artificial neural networks with geometrical embedding. The model provides a mathematical description of abstract neural activation transfer in terms, which bear resemblances to multi-value Boltzmann-like evolution. The activation-preserving constraint mimics a critical regime of the dynamics and, along with accounting for geometrical location of the neurons, makes the system more feasible for modelling of real-world networks. We focus on scale invariance or scale-freeness and small-world phenomena in the said networks. Our results clearly confirm presence of both features at the functional level of the activity-flow graph. We show that the degree distribution preserves a power-law shape with the exponent value approximately equal to -2. In addition, we present our results concerning characteristic path length in the said graphs, which grows roughly logarithmically with the size of the network, while the clustering coefficient turns out to be relatively high. Taken together, the clustering and path length ratios are surprisingly high, and thus confirm large both local and global efficiency of the network. Finally, we compare the properties of activation-flow model to those reported in neurobiological analyses of brain networks recorded with functional magnetic resonance imagining (fMRI). There is a strong agreement between the shape and exponent value of degree distribution also the clustering and characteristic path lengths are comparable in both the model and medical data.Celem niniejszej rozprawy jest analiza uproszczonego modelu przepływu aktywności w sztucznych sieciach neuronowych zanurzonych w przestrzeni geometrycznej. Przedstawiony model dostarcza matematycznego opisu transferu aktywności w terminach zbliżonych do wielowartościowych maszyn Boltzmanna. Wymóg zachowania stałej sumarycznej aktywności odzwierciedla krytyczność dynamiki i wraz z uwzględnieniem wpływu lokalizacji geometrycznej neuronów sprawia, że system jest bardziej adekwatny do modelowania rzeczywistych sieci. Badania koncentrują się na bezskalowości oraz fenomenie małego świata w wyżej wymienionych sieciach. Uzyskane rezultaty potwierdzają obecność obu własności w omawianych grafach. Pokażemy, że rozkład stopni wejściowych wierzchołków zachowuje się jak funkcja potęgowa z wykładnikiem równym -2. Ponadto prezentujemy wyniki dotyczące charakterystycznej długości ścieżki, który rośnie logarytmicznie wraz z wielkością systemu, podczas gdy współczynnik klasteryzacji okazuje się dość duży. W konsekwencji stosunek klasteryzacji do długości ścieżek jest zaskakująco wysoki, co jest dystynktywną własnością sieci małego świata. Wreszcie, dokonujemy porównania cech omawianego modelu przepływu aktywności z neuro-biologicznymi rezultatami, przedstawionymi w badaniach grafów mózgowych z danych uzyskanych z funkcjonalnego obrazowania z wykorzystaniem rezonansu magnetycznego (fMRI). Wskazujemy silną odpowiedniość pomiędzy kształtem i wartością wykładnika rozkładu stopni, zaś klasteryzacja i charakterystyczna długość ścieżki są porównywalne w modelu i danych medycznych

    Actor-Transformers for Group Activity Recognition

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    This paper strives to recognize individual actions and group activities from videos. While existing solutions for this challenging problem explicitly model spatial and temporal relationships based on location of individual actors, we propose an actor-transformer model able to learn and selectively extract information relevant for group activity recognition. We feed the transformer with rich actor-specific static and dynamic representations expressed by features from a 2D pose network and 3D CNN, respectively. We empirically study different ways to combine these representations and show their complementary benefits. Experiments show what is important to transform and how it should be transformed. What is more, actor-transformers achieve state-of-the-art results on two publicly available benchmarks for group activity recognition, outperforming the previous best published results by a considerable margin.Comment: CVPR 202

    Correlations Decrease with Propagation of Spiking Activity in the Mouse Barrel Cortex

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    Propagation of suprathreshold spiking activity through neuronal populations is important for the function of the central nervous system. Neural correlations have an impact on cortical function particularly on the signaling of information and propagation of spiking activity. Therefore we measured the change in correlations as suprathreshold spiking activity propagated between recurrent neuronal networks of the mammalian cerebral cortex. Using optical methods we recorded spiking activity from large samples of neurons from two neural populations simultaneously. The results indicate that correlations decreased as spiking activity propagated from layer 4 to layer 2/3 in the rodent barrel cortex
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