Browsing by Author "Perc, Matjaž"

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    Disordered configurations of the Glauber model in two-dimensional networks
    Bačić, Iva; Franović, Igor; Perc, Matjaž
    We analyze the ordering efficiency and the structure of disordered configurations for the zero-temperature Glauber model on Watts-Strogatz networks obtained by rewiring 2D regular square lattices. In the small-world regime, the dynamics fails to reach the ordered state in the thermodynamic limit. Due to the interplay of the perturbed regular topology and the energy neutral stochastic state transitions, the stationary state consists of two intertwined domains, manifested as multiclustered states on the original lattice. Moreover, for intermediate rewiring probabilities, one finds an additional source of disorder due to the low connectivity degree, which gives rise to small isolated droplets of spins. We also examine the ordering process in paradigmatic two-layer networks with heterogeneous rewiring probabilities. Comparing the cases of a multiplex network and the corresponding network with random inter-layer connectivity, we demonstrate that the character of the final state qualitatively depends on the type of inter-layer connections.
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    Extending Dynamic Memory of Spiking Neuron Networks
    Klinshov, Vladimir; Kovalchuk, Andrey; Soloviev, Igor; Maslennikov, Oleg; Franović, Igor; Perc, Matjaž
    Explaining the mechanisms of dynamic memory, that allows for a temporary storage of information at the timescale of seconds despite the neuronal firing at the millisecond scale, is an important challenge not only for neuroscience, but also for computation in neuromorphic artificial networks. We demonstrate the potential origin of such longer timescales by comparing the spontaneous activity in excitatory neural networks with sparse random, regular and small-world connection topologies. We derive a mean-field model based on a self-consistent approach and white noise approximation to analyze the transient and long-term collective network dynamics. While the long-term dynamics is typically irregular and weakly correlated independent of the network architecture, especially long timescales are revealed for the transient activity comprised of switching fronts in regular and small-world networks with a small rewiring probability. Analyzing the dynamic memory of networks in performing a simple computational delay task within the framework of reservoir computing, we show that an optimal performance on average is reached for a regular connection topology if the input is appropriately structured, but certain instances of small-world networks may strongly deviate from configuration averages and outperform all the other considered network architectures.
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    Inverse stochastic resonance in a system of excitable active rotators with adaptive coupling
    Bačić, Iva; Klinshov, Vladimir; Nekorkin, Vladimir; Perc, Matjaž; Franović, Igor
    Inverse stochastic resonance is a phenomenon where an oscillating system influenced by noise exhibits a minimal oscillation frequency at an intermediate noise level. We demonstrate a novel generic scenario for such an effect in a multi-timescale system, considering an example of emergent oscillations in two adaptively coupled active rotators with excitable local dynamics. The impact of plasticity turns out to be twofold. First, at the level of multiscale dynamics, one finds a range of intermediate adaptivity rates that give rise to multistability between the limit cycle attractors and the stable equilibria, a condition necessary for the onset of the effect. Second, applying the fast-slow analysis, we show that the plasticity also plays a facilitatory role on a more subtle level, guiding the fast flow dynamics to parameter domains where the stable equilibria become focuses rather than nodes, which effectively enhances the influence of noise. The described scenario persists for different plasticity rules, underlying its robustness in the light of potential applications to neuroscience and other types of cell dynamics.
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    Rate chaos and memory lifetime in spiking neural networks
    Klinshov, Vladimir V.; Kovalchuk, Andrey V.; Franović, Igor; Perc, Matjaž; Svetec, Milan
    Rate chaos is a collective state of a neural network characterized by slow irregular fluctuations of firing rates of individual neurons. We study a sparsely connected network of spiking neurons which demonstrates three different scenarios for the emergence of rate chaos, based either on increasing the synaptic strength, increasing the synaptic integration time, or clustering of the excitatory synaptic connections. Although all the scenarios lead to collective dynamics with similar statistical features, it turns out that the implications for the computational capability of the network in performing a simple delay task are strongly dependent on the particular scenario. Namely, only the scenario involving slow dynamics of synapses results in an appreciable extension of the network's dynamic memory. In other cases, the dynamic memory remains short despite the emergence of long timescales in the neuronal spike trains. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).