Inhibitory neurons promote robust critical firing dynamics in networks of integrate-and-fire neurons.

We study the firing dynamics of a discrete-state and discrete-time version of an integrate-and-fire neuronal network model with both excitatory and inhibitory neurons. When the integer-valued state of a neuron exceeds a threshold value, the neuron fires, sends out state-changing signals to its connected neurons, and returns to the resting state. In this model, a continuous phase transition from non-ceaseless firing to ceaseless firing is observed. At criticality, power-law distributions of avalanche size and duration with the previously derived exponents, -3/2 and -2, respectively, are observed. Using a mean-field approach, we show analytically how the critical point depends on model parameters. Our main result is that the combined presence of both inhibitory neurons and integrate-and-fire dynamics greatly enhances the robustness of critical power-law behavior (i.e., there is an increased range of parameters, including both sub- and supercritical values, for which several decades of power-law behavior occurs).

Impact of imperfect information on network attack.

This paper explores the effectiveness of network attack when the attacker has imperfect information about the network. For Erdos-Rényi networks, we observe that dynamical importance and betweenness centrality-based attacks are surprisingly robust to the presence of a moderate amount of imperfect information and are more effective compared with simpler degree-based attacks even at moderate levels of network information error. In contrast, for scale-free networks the effectiveness of attack is much less degraded by a moderate level of information error. Furthermore, in the Erdos-Rényi case the effectiveness of network attack is much more degraded by missing links as compared with the same number of false links.

Spatially embedded growing small-world networks.

Networks in nature are often formed within a spatial domain in a dynamical manner, gaining links and nodes as they develop over time. Motivated by the growth and development of neuronal networks, we propose a class of spatially-based growing network models and investigate the resulting statistical network properties as a function of the dimension and topology of the space in which the networks are embedded. In particular, we consider two models in which nodes are placed one by one in random locations in space, with each such placement followed by configuration relaxation toward uniform node density, and connection of the new node with spatially nearby nodes. We find that such growth processes naturally result in networks with small-world features, including a short characteristic path length and nonzero clustering. We find no qualitative differences in these properties for two different topologies, and we suggest that results for these properties may not depend strongly on the topology of the embedding space. The results do depend strongly on dimension, and higher-dimensional spaces result in shorter path lengths but less clustering.

Stability of Boolean networks: the joint effects of topology and update rules.

We study the stability of orbits in large Boolean networks. We treat the case in which the network has a given complex topology, and we do not assume a specific form for the update rules, which may be correlated with local topological properties of the network. While recent past work has addressed the separate effects of complex network topology and certain classes of update rules on stability, only crude results exist about how these effects interact. We present a widely applicable solution to this problem. Numerical simulations confirm our theory and show that local correlations between topology and update rules can have profound effects on the qualitative behavior of these systems.

Weakly explosive percolation in directed networks.

Percolation, the formation of a macroscopic connected component, is a key feature in the description of complex networks. The dynamical properties of a variety of systems can be understood in terms of percolation, including the robustness of power grids and information networks, the spreading of epidemics and forest fires, and the stability of gene regulatory networks. Recent studies have shown that if network edges are added "competitively" in undirected networks, the onset of percolation is abrupt or "explosive." The unusual qualitative features of this phase transition have been the subject of much recent attention. Here we generalize this previously studied network growth process from undirected networks to directed networks and use finite-size scaling theory to find several scaling exponents. We find that this process is also characterized by a very rapid growth in the giant component, but that this growth is not as sudden as in undirected networks.

Dynamical instability in Boolean networks as a percolation problem.

Boolean networks, widely used to model gene regulation, exhibit a phase transition between regimes in which small perturbations either die out or grow exponentially. We show and numerically verify that this phase transition in the dynamics can be mapped onto a static percolation problem which predicts the long-time average Hamming distance between perturbed and unperturbed orbits.

Symmetric double proton tunneling in formic acid dimer: a diabatic basis approach.

A model of double proton tunneling in formic acid dimer is developed using a reaction surface Hamiltonian. The surface includes the symmetric OH stretch plus the in-plane stretch and bend interdimer vibrations. The surface Hamiltonian is coupled to a bath of five A1g and B3g normal modes obtained at the D2h transition state structure. Eigenstates are calculated using Davidson and block-Davidson iterative methods. Strong mode specific effects are found in the tunneling splittings for the reaction surface, where splittings are enhanced upon excitation of the interdimer bend motion. The results are interpreted within the framework of a diabatic representation of reaction surface modes. The splitting patterns observed for the reaction surface eigenstates are only slightly modified upon coupling to the bath states. Splitting patterns for the bath states are also determined. It is found that predicting these splittings is greatly complicated by subtle mixings with the inter-dimer bend states.

Human-observer LROC study of lesion detection in Ga-67 SPECT images reconstructed using MAP with anatomical priors.

We compare the image quality of SPECT reconstruction with and without an anatomical prior. Area under the localization-response operating characteristic (LROC) curve is our figure of merit. Simulated Ga-67 citrate images, a SPECT lymph-nodule imaging agent, were generated using the MCAT digital phantom. Reconstructed images were read by human observers.Several reconstruction strategies are compared, including rescaled block iterative (RBI) and maximum-a-posteriori (MAP) with various priors. We find that MAP reconstruction using prior knowledge of organ and lesion boundaries significantly improves lesion-detection performance (p < 0.05). Pseudo-lesion boundaries, regions without increased uptake which are incorrectly treated as prior knowledge of lesion boundaries, do not decrease performance.