Dissecting landscape art history with information theory.

Painting has played a major role in human expression, evolving subject to a complex interplay of representational conventions, social interactions, and a process of historization. From individual qualitative work of art historians emerges a metanarrative that remains difficult to evaluate in its validity regarding emergent macroscopic and underlying microscopic dynamics. The full scope of granular data, the summary statistics, and consequently, also their bias simply lie beyond the cognitive limit of individual qualitative human scholarship. Yet, a more quantitative understanding is still lacking, driven by a lack of data and a persistent dominance of qualitative scholarship in art history. Here, we show that quantitative analyses of creative processes in landscape painting can shed light, provide a systematic verification, and allow for questioning the emerging metanarrative. Using a quasicanonical benchmark dataset of 14,912 landscape paintings, covering a period from the Western renaissance to contemporary art, we systematically analyze the evolution of compositional proportion via a simple yet coherent information-theoretic dissection method that captures iterations of the dominant horizontal and vertical partition directions. Tracing frequency distributions of seemingly preferred compositions across several conceptual dimensions, we find that dominant dissection ratios can serve as a meaningful signature to capture the unique compositional characteristics and systematic evolution of individual artist bodies of work, creation date time spans, and conventional style periods, while concepts of artist nationality remain problematic. Network analyses of individual artists and style periods clarify their rhizomatic confusion while uncovering three distinguished yet nonintuitive supergroups that are meaningfully clustered in time.

Spontaneous Oscillatory Rhythm in Retinal Activities of Two Retinal Degeneration (rd1 and rd10) Mice.

Previously, we reported that besides retinal ganglion cell (RGC) spike, there is ~ 10 Hz oscillatory rhythmic activity in local field potential (LFP) in retinal degeneration model, rd1 mice. The more recently identified rd10 mice have a later onset and slower rate of photoreceptor degeneration than the rd1 mice, providing more therapeutic potential. In this study, before adapting rd10 mice as a new animal model for our electrical stimulation study, we investigated electrical characteristics of rd10 mice. From the raw waveform of recording using 8×8 microelectrode array (MEA) from in vitro-whole mount retina, RGC spikes and LFP were isolated by using different filter setting. Fourier transform was performed for detection of frequency of bursting RGC spikes and oscillatory field potential (OFP). In rd1 mice, ~10 Hz rhythmic burst of spontaneous RGC spikes is always phase-locked with the OFP and this phase-locking property is preserved regardless of postnatal ages. However, in rd10 mice, there is a strong phase-locking tendency between the spectral peak of bursting RGC spikes (~5 Hz) and the first peak of OFP (~5 Hz) across different age groups. But this phase-locking property is not robust as in rd1 retina, but maintains for a few seconds. Since rd1 and rd10 retina show phase-locking property at different frequency (~10 Hz vs. ~5 Hz), we expect different response patterns to electrical stimulus between rd1 and rd10 retina. Therefore, to extract optimal stimulation parameters in rd10 retina, first we might define selection criteria for responding rd10 ganglion cells to electrical stimulus.

Inferring the physical connectivity of complex networks from their functional dynamics.

BACKGROUND: Biological networks, such as protein-protein interactions, metabolic, signalling, transcription-regulatory networks and neural synapses, are representations of large-scale dynamic systems. The relationship between the network structure and functions remains one of the central problems in current multidisciplinary research. Significant progress has been made toward understanding the implication of topological features for the network dynamics and functions, especially in biological networks. Given observations of a network system's behaviours or measurements of its functional dynamics, what can we conclude of the details of physical connectivity of the underlying structure? RESULTS: We modelled the network system by employing a scale-free network of coupled phase oscillators. Pairwise phase coherence (PPC) was calculated for all the pairs of oscillators to present functional dynamics induced by the system. At the regime of global incoherence, we observed a Significant pairwise synchronization only between two nodes that are physically connected. Right after the onset of global synchronization, disconnected nodes begin to oscillate in a correlated fashion and the PPC of two nodes, either connected or disconnected, depends on their degrees.Based on the observation of PPCs, we built a weighted network of synchronization (WNS), an all-to-all functionally connected network where each link is weighted by the PPC of two oscillators at the ends of the link. In the regime of strong coupling, we observed a Significant similarity in the organization of WNSs induced by systems sharing the same substrate network but different configurations of initial phases and intrinsic frequencies of oscillators.We reconstruct physical network from the WNS by choosing the links whose weights are higher than a given threshold. We observed an optimal reconstruction just before the onset of global synchronization.Finally, we correlated the topology of the background network to the observed change of the functional activities in the system. CONCLUSIONS: The results presented in this study indicate a strong relationship between the structure and dynamics of complex network systems. As coupling strength increases, synchronization emerges among hub nodes and recruits small-degree nodes. The results show that the onset of global synchronization in the system hinders the reconstruction of an underlying complex structure. Our analysis helps to clarify how the synchronization is achieved in systems of different network topologies.

Emergence of chaotic itinerancy in simple ecological systems.

Chaotic itinerancy is a universal dynamical concept that describes itinerant motion among many different ordered states through chaotic transition in dynamical systems. Unlike the expectation of the prevalence of chaotic itinerancy in high-dimensional systems, we identify chaotic itinerant behavior from a relatively simple ecological system, which consists only of two coupled consumer-resource pairs. The system exhibits chaotic bursting activity, in which the explosion and the shrinkage of the population alternate indefinitely, while the explosion of one pair co-occurs with the shrinkage of the other pair. We analyze successfully the bursting activity in the framework of chaotic itinerancy, and find that large duration times of bursts tend to cluster in time, allowing the effective burst prognosis. We also investigate the control schemes on the bursting activity, and demonstrate that invoking the competitive rise of the consumer in one pair can even elongate the burst of the other pair rather than shorten it.

Phase-model analysis of coupled neuronal oscillators with multiple connections.

Synchronization of the coupled neuronal oscillators with multiple connections of different coupling nature is analyzed using the phase-model reduction method. Each coupling connection contributes to the dynamic behavior of the system in a complex nonlinear fashion. In the phase-model scheme, the contribution of the individual connections can be separated in terms of the effective coupling functions associated with each connection and a linear superposition of them provides the total effective coupling of the coupled system. The case of multiple connections with various conduction time delays is also examined, which is shown to be capable of promoting synchronization over an ensemble of spatially distributed neuronal oscillators in an efficient way.

Chaotic bursting as chaotic itinerancy in coupled neural oscillators.

We show that chaotic bursting activity observed in coupled neural oscillators is a kind of chaotic itinerancy. In neuronal systems with phase deformation along the trajectory, diffusive coupling induces a dephasing effect. Because of this effect, an antiphase synchronized solution is stable for weak coupling, while an in-phase solution is stable for very strong coupling. For intermediate coupling, a chaotic bursting activity is generated. It is a mixture of three different states: an antiphase firing state, an in-phase firing state, and a nonfiring resting state. As we construct numerically the deformed torus manifold underlying the chaotic bursting state, it is shown that the three unstable states are connected to give rise to a global chaotic itinerancy structure. Thus we claim that chaotic itinerancy provides an alternative route to chaos via torus breakdown.

The role of hydrogen peroxide produced by polychlorinated biphenyls in PMR1-deficient yeast cells.

Polychlorinated biphenyls (PCBs) are well-known recalcitrant environmental pollutants. Although the metabolism of the PCBs has been intensively studied, very little is known about their mechanism of toxicity in living organisms or how they are degraded. We have examined the effects of PCBs on two different yeast strains to determine their mechanism of action. One yeast strain (K601, wild type) is resistant to the growth-inhibitory effect of PCBs, whereas the other strain (AA542, PMR1 mutant) is susceptible. PCBs increased the level of intracellular hydrogen peroxide in AA542 cells but not in K601 cells. In the presence of alpha-tocopherol or ursolic acid the growth of AA542 cells was not inhibited by treatment with PCBs. These results suggest that PCBs block cell growth through production of hydrogen peroxide in the PMR1 mutant strain, AA542. We compared superoxide dismutase (SOD), glutathione peroxidase (GPx), and catalase activities in both strains. The catalase activity in K601 cells was 10 times higher than that in AA542 cells. In contrast, there was no difference in activities of SOD and GPx between the two strains. Collectively, these observations indicate that oxidative stress causes the inhibition of cell growth observed in catalase-deficient yeast cells exposed to PCBs.

Attack vulnerability of complex networks.

We study the response of complex networks subject to attacks on vertices and edges. Several existing complex network models as well as real-world networks of scientific collaborations and Internet traffic are numerically investigated, and the network performance is quantitatively measured by the average inverse geodesic length and the size of the largest connected subgraph. For each case of attacks on vertices and edges, four different attacking strategies are used: removals by the descending order of the degree and the betweenness centrality, calculated for either the initial network or the current network during the removal procedure. It is found that the removals by the recalculated degrees and betweenness centralities are often more harmful than the attack strategies based on the initial network, suggesting that the network structure changes as important vertices or edges are removed. Furthermore, the correlation between the betweenness centrality and the degree in complex networks is studied.

Independent component analysis of ictal EEG in medial temporal lobe epilepsy.

PURPOSE: Application of independent component analysis (ICA) to interictal EEGs and to event-related potentials has helped noise reduction and source localization. However, ICA has not been used for the analysis of ictal EEGs in partial seizures. In this study, we applied ICA to the ictal EEGs of patients with medial temporal lobe epilepsy (TLE) and investigated whether ictal components can be separated and whether they indicate correct lateralization. METHODS: Twenty-four EEGs from medial TLE patients were analyzed with the extended ICA algorithm. Among the resultant 20 components in each EEG, we selected components with an ictal nature and reviewed their corresponding topographic maps for the lateralization. We then applied quantitative methods for the verification of increased quality of the reconstructed EEGs. RESULTS: All ictal EEGs were successfully decomposed into one or more ictal components and nonictal components. After EEG reconstruction with exclusion of artifacts, the lateralizing power of the ictal EEG was increased from 75 to 96%. CONCLUSIONS: ICA can separate successfully the manifold components of ictal rhythms and can improve EEG quality.

Path finding strategies in scale-free networks.

We numerically investigate the scale-free network model of Barabási and Albert [A. L. Barabási and R. Albert, Science 286, 509 (1999)] through the use of various path finding strategies. In real networks, global network information is not accessible to each vertex, and the actual path connecting two vertices can sometimes be much longer than the shortest one. A generalized diameter depending on the actual path finding strategy is introduced, and a simple strategy, which utilizes only local information on the connectivity, is suggested and shown to yield small-world behavior: the diameter D of the network increases logarithmically with the network size N, the same as is found with global strategy. If paths are sought at random, D is equivalent to N(0.5) is found.