Distinct dynamical behavior in Erd\H{o}s-R\'enyi networks, regular random networks, ring lattices, and all-to-all neuronal networks

Neuronal network dynamics depends on network structure. In this paper we study how network topology underpins the emergence of different dynamical behaviors in neuronal networks. In particular, we consider neuronal network dynamics on Erd\H{o}s-R\'enyi (ER) networks, regular random (RR) networks, ring lattices, and all-to-all networks. We solve analytically a neuronal network model with stochastic binary-state neurons in all the network topologies, except ring lattices. Given that apart from network structure, all four models are equivalent, this allows us to understand the role of network structure in neuronal network dynamics. Whilst ER and RR networks are characterized by similar phase diagrams, we find strikingly different phase diagrams in the all-to-all network. Neuronal network dynamics is not only different within certain parameter ranges, but it also undergoes different bifurcations (with a richer repertoire of bifurcations in ER and RR compared to all-to-all networks). This suggests that local heterogeneity in the ratio between excitation and inhibition plays a crucial role on emergent dynamics. Furthermore, we also observe one subtle discrepancy between ER and RR networks, namely ER networks undergo a neuronal activity jump at lower noise levels compared to RR networks, presumably due to the degree heterogeneity in ER networks that is absent in RR networks. Finally, a comparison between network oscillations in RR networks and ring lattices shows the importance of small-world properties in sustaining stable network oscillations.Comment: 9 pages, 4 figure

Potts model on complex networks

We consider the general p-state Potts model on random networks with a given degree distribution (random Bethe lattices). We find the effect of the suppression of a first order phase transition in this model when the degree distribution of the network is fat-tailed, that is, in more precise terms, when the second moment of the distribution diverges. In this situation the transition is continuous and of infinite order, and size effect is anomalously strong. In particular, in the case of $p=1$, we arrive at the exact solution, which coincides with the known solution of the percolation problem on these networks.Comment: 6 pages, 1 figur

Estimating the Brittle Strength of Nuclear Fuel Material

The development of nuclear energy involves use of promising nitride nuclear fuel in reactors of the 4th generation. This will require improving the fuel production technology as well as its test methods. For estimation of the strength of the nuclear fuel material as well and for further refinement of nuclear fuel test technology we propose to use small discoid samples, similar in shape to the elements of nuclear fuel in the context of ”Brazilian test” (compression applied to disk specimen in the median plane). We present here the results of testing small discoid specimens made of brittle materials such as cast iron and graphite (both being considered as possible model materials for the nuclear fuel). We compared these materials to nuclear fuelitself (as represented by uranium dioxide). In addition the effect of the specimen size on resistance to destruction was investigated. The type of deformation and fracture found in samples made from cast iron suggests that this material cannot be used as a model for the nuclear fuel. At the same time the results obtained in tests on samples composed of graphite ARV-1 were in good agreement with the results oftests on uranium dioxide. Using the data obtained in this study, a calculation formula for determining the strength of the nuclear fuel material based on the “Brazilian test” results is proposed

Peltier effect in normal metal-insulator-heavy fermion metal junctions

A theoretical study has been undertaken of the Peltier effect in normal metal - insulator - heavy fermion metal junctions. The results indicate that, at temperatures below the Kondo temperature, such junctions can be used as electronic microrefrigerators to cool the normal metal electrode and are several times more efficient in cooling than the normal metal - heavy fermion metal junctions.Comment: 3 pages in REVTeX, 2 figures, to be published in Appl. Phys. Lett., April 7, 200

Neural networks with dynamical synapses: from mixed-mode oscillations and spindles to chaos

Understanding of short-term synaptic depression (STSD) and other forms of synaptic plasticity is a topical problem in neuroscience. Here we study the role of STSD in the formation of complex patterns of brain rhythms. We use a cortical circuit model of neural networks composed of irregular spiking excitatory and inhibitory neurons having type 1 and 2 excitability and stochastic dynamics. In the model, neurons form a sparsely connected network and their spontaneous activity is driven by random spikes representing synaptic noise. Using simulations and analytical calculations, we found that if the STSD is absent, the neural network shows either asynchronous behavior or regular network oscillations depending on the noise level. In networks with STSD, changing parameters of synaptic plasticity and the noise level, we observed transitions to complex patters of collective activity: mixed-mode and spindle oscillations, bursts of collective activity, and chaotic behaviour. Interestingly, these patterns are stable in a certain range of the parameters and separated by critical boundaries. Thus, the parameters of synaptic plasticity can play a role of control parameters or switchers between different network states. However, changes of the parameters caused by a disease may lead to dramatic impairment of ongoing neural activity. We analyze the chaotic neural activity by use of the 0-1 test for chaos (Gottwald, G. & Melbourne, I., 2004) and show that it has a collective nature.Comment: 7 pages, Proceedings of 12th Granada Seminar, September 17-21, 201