Evolutionary Subnetworks in Complex Systems

Links in a practical network may have different functions, which makes the original network a combination of some functional subnetworks. Here, by a model of coupled oscillators, we investigate how such functional subnetworks are evolved and developed according to the network structure and dynamics. In particular, we study the case of evolutionary clustered networks in which the function of each link (either attractive or repulsive coupling) is updated by the local dynamics. It is found that, during the process of system evolution, the network is gradually stabilized into a particular form in which the attractive (repulsive) subnetwork consists only the intralinks (interlinks). Based on the properties of subnetwork evolution, we also propose a new algorithm for network partition which is distinguished by the convenient operation and fast computing speed.Comment: 4 pages, 4 figure

Stable Hebbian learning from spike timing-dependent plasticity

We explore a synaptic plasticity model that incorporates recent findings that potentiation and depression can be induced by precisely timed pairs of synaptic events and postsynaptic spikes. In addition we include the observation that strong synapses undergo relatively less potentiation than weak synapses, whereas depression is independent of synaptic strength. After random stimulation, the synaptic weights reach an equilibrium distribution which is stable, unimodal, and has positive skew. This weight distribution compares favorably to the distributions of quantal amplitudes and of receptor number observed experimentally in central neurons and contrasts to the distribution found in plasticity models without size-dependent potentiation. Also in contrast to those models, which show strong competition Changes in the synaptic connections between neurons are widely believed to contribute to memory storage, and the activitydependen

Robustness and Enhancement of Neural Synchronization by Activity-Dependent Coupling

We study the synchronization of two model neurons coupled through a synapse having an activity-dependent strength. Our synapse follows the rules of Spike-Timing Dependent Plasticity (STDP). We show that this plasticity of the coupling between neurons produces enlarged frequency locking zones and results in synchronization that is more rapid and much more robust against noise than classical synchronization arising from connections with constant strength. We also present a simple discrete map model that demonstrates the generality of the phenomenon.Comment: 4 pages, accepted for publication in PR

Memristive operation mode of a site-controlled quantum dot floating gate transistor

The authors gratefully acknowledge financial support from the European Union (FPVII (2007-2013) under Grant Agreement No. 318287 Landauer) as well as the state of Bavaria.We have realized a floating gate transistor based on a GaAs/AlGaAs heterostructure with site-controlled InAs quantum dots. By short-circuiting the source contact with the lateral gates and performing closed voltage sweep cycles, we observe a memristive operation mode with pinched hysteresis loops and two clearly distinguishable conductive states. The conductance depends on the quantum dot charge which can be altered in a controllable manner by the voltage value and time interval spent in the charging region. The quantum dot memristor has the potential to realize artificial synapses in a state-of-the-art opto-electronic semiconductor platform by charge localization and Coulomb coupling.Publisher PDFPeer reviewe

Effects of rapamycin on cultured hepatocyte proliferation and gene expression

Rapamycin, a potent immunosuppressive drug that disrupts normal signal‐transduction processes, inhibited hepatocyte proliferation without evidence of inherent cytotoxicity in rat hepatocytes cultured in conventional medium or in a medium enriched with epidermal growth factor. The antiproliferative effect was dose dependent, uninfluenced by the concentration of epidermal growth factor in the medium and long lasting after a brief exposure. The effect of rapamycin was unaltered by the concomitant presence of FK 506 in the medium, suggesting that different binding affinities of these two drugs or even a separate rapamycin binding site may exist. Hepatocytes harvested 12 and 24 hr after partial hepatectomy were progressively less responsive to the antiproliferative effect of rapamycin. The gene expression of transforming growth factor‐β was reduced under in vivo rapamycin treatment, but at the same time the gene expression of albumin and glyceraldehyde‐3‐phosphate dehydrogenase was unchanged or increased. The experiments confirm that rapamycin has inherent growth‐control qualities, and they strengthen the hypothesis that the recently defined immunophilin network is central to many aspects of cellular growth control. (HEPATOLOGY 1992;15:871–877). Copyright © 1992 American Association for the Study of Liver Disease

Event-driven simulations of a plastic, spiking neural network

We consider a fully-connected network of leaky integrate-and-fire neurons with spike-timing-dependent plasticity. The plasticity is controlled by a parameter representing the expected weight of a synapse between neurons that are firing randomly with the same mean frequency. For low values of the plasticity parameter, the activities of the system are dominated by noise, while large values of the plasticity parameter lead to self-sustaining activity in the network. We perform event-driven simulations on finite-size networks with up to 128 neurons to find the stationary synaptic weight conformations for different values of the plasticity parameter. In both the low and high activity regimes, the synaptic weights are narrowly distributed around the plasticity parameter value consistent with the predictions of mean-field theory. However, the distribution broadens in the transition region between the two regimes, representing emergent network structures. Using a pseudophysical approach for visualization, we show that the emergent structures are of "path" or "hub" type, observed at different values of the plasticity parameter in the transition region.Comment: 9 pages, 6 figure

Structural network heterogeneities and network dynamics: a possible dynamical mechanism for hippocampal memory reactivation

The hippocampus has the capacity for reactivating recently acquired memories [1-3] and it is hypothesized that one of the functions of sleep reactivation is the facilitation of consolidation of novel memory traces [4-11]. The dynamic and network processes underlying such a reactivation remain, however, unknown. We show that such a reactivation characterized by local, self-sustained activity of a network region may be an inherent property of the recurrent excitatory-inhibitory network with a heterogeneous structure. The entry into the reactivation phase is mediated through a physiologically feasible regulation of global excitability and external input sources, while the reactivated component of the network is formed through induced network heterogeneities during learning. We show that structural changes needed for robust reactivation of a given network region are well within known physiological parameters [12,13].Comment: 16 pages, 5 figure

Storage capacity of phase-coded patterns in sparse neural networks

We study the storage of multiple phase-coded patterns as stable dynamical attractors in recurrent neural networks with sparse connectivity. To determine the synaptic strength of existent connections and store the phase-coded patterns, we introduce a learning rule inspired to the spike-timing dependent plasticity (STDP). We find that, after learning, the spontaneous dynamics of the network replay one of the stored dynamical patterns, depending on the network initialization. We study the network capacity as a function of topology, and find that a small- world-like topology may be optimal, as a compromise between the high wiring cost of long range connections and the capacity increase.Comment: Accepted for publication in Europhysics Letter

Nanobubbles at hydrophilic particle–water interfaces

The puzzling persistence of nanobubbles breaks Laplace’s law for bubbles, which is of great interest for promising applications in surface processing, H2 and CO2 storage, water treatment, and drug delivery. So far, nanobubbles are mostly reported on the hydrophobic planar substrates with atom flatness. It remains a challenge to quantify nanobubbles on rough and irregular surfaces due to the lack of characterization technique that can detect both the nanobubble morphology and chemical composition inside individual nanobubble-like objects. Here, by using synchrotron-based scanning transmission soft X-ray microscopy (STXM) with nanometer resolution, we discern nanoscopic gas bubbles > 25 nm with direct in-situ proof of O2 inside the nanobubbles at a hydrophilic particle-water interface under ambient conditions. We find a stable cloud of O2 nanobubbles at the diatomite particle-water interface hours after oxygen aeration and temperature variation. The in situ technique may be useful for many surface nanobubble related studies such as material preparation and property manipulation, phase equilibrium, nucleation kinetics and their relationships with chemical composition within the confined nanoscale space. The oxygen nanobubble clouds may be important in modifying particle-water interfaces and offering breakthrough technologies for oxygen delivery in sediment and/or deep water environment

Equilibrium Properties of Temporally Asymmetric Hebbian Plasticity

A theory of temporally asymmetric Hebb (TAH) rules which depress or potentiate synapses depending upon whether the postsynaptic cell fires before or after the presynaptic one is presented. Using the Fokker-Planck formalism, we show that the equilibrium synaptic distribution induced by such rules is highly sensitive to the manner in which bounds on the allowed range of synaptic values are imposed. In a biologically plausible multiplicative model, we find that the synapses in asynchronous networks reach a distribution that is invariant to the firing rates of either the pre- or post-synaptic cells. When these cells are temporally correlated, the synaptic strength varies smoothly with the degree and phase of synchrony between the cells.Comment: 3 figures, minor corrections of equations and tex