Autaptic modulation-induced neuronal electrical activities and wave propagation on network under electromagnetic induction

Based on an improved HR neuron model, the effects of electrical and chemical autapses on the firing activities of single neurons are studied, and the wave propagation in forward feedback neural network is also discussed by considering autapstic regulation under different intensities of electromagnetic induction. It is found that the electrical activities of single neuron can be changed by exerting excitatory or inhibitory of electrical and chemical autapses. With different feedback gains of electromagnetic induction current, membrane potential shows the oscillatory solutions and steady states. Under the condition of different autapse or electromagnetic induction, the propagation of electrical activities caused by the central neuron is transformed in the forward feedback network. Moreover, the spatial synchronization of the network will be changed by choosing different coupling intensities and feedback gains. It is proved that the electrical and chemical autapses play a significant role in firing modes of single neuron and the wave propagation of the forward feedback networks under the electromagnetic induction.     

The perception of entropy in rapidly moving sparse dot arrays: a nonlinear dynamic perspective

In visual fields composed of dots spatially randomly distributed but moving rigidly, the percept of coherent motion is lost once D_max is exceeded, resulting in an incoherent, random percept. We have investigated this transition both from a psychophysics perspective and in the development of a dynamic model of the visual system based on a spatially coupled array of nonlinear damped mass-springs cells. We present results of experiments using rigidly moving arrays of dots of different levels of sparseness and differing displacement magnitudes. Results show that the perception of randomness can be reliably judged and displays a transition from coherent to non-coherent motion as the motion amplitude is increased. Using standard psychophysical just noticeable difference (JND) judgements, we noted that the threshold JND was a function of displacement magnitude and sparseness and could not be explained by extant spatiotemporal filtering models. Our model qualitatively explains the important features of the data, reproducing the experimental D_max and entropy perception effects with increased stimuli motion amplitude at different spatial sparseness levels. We have then performed some numerical simulations of the model when the masses in the array are randomly distributed. Results show that sparseness plays different role if close or far from D_max in terms of motion coherence discrimination.     

Understanding the enhanced synchronization of delay-coupled networks with fluctuating topology

We study the dynamics of networks with coupling delay, from which the connectivity changes over time. The synchronization properties are shown to depend on the interplay of three time scales: the internal time scale of the dynamics, the coupling delay along the network links and time scale at which the topology changes. Concentrating on a linearized model, we develop an analytical theory for the stability of a synchronized solution. In two limit cases, the system can be reduced to an “effective” topology: in the fast switching approximation, when the network fluctuations are much faster than the internal time scale and the coupling delay, the effective network topology is the arithmetic mean over the different topologies. In the slow network limit, when the network fluctuation time scale is equal to the coupling delay, the effective adjacency matrix is the geometric mean over the adjacency matrices of the different topologies. In the intermediate regime, the system shows a sensitive dependence on the ratio of time scales, and on the specific topologies, reproduced as well by numerical simulations. Our results are shown to describe the synchronization properties of fluctuating networks of delay-coupled chaotic maps.     

Carpet oscillator: A new megastable nonlinear oscillator with infinite islands of self-excited and hidden attractors

Polarisation of the particle spin can be an important problem for different plasmas. In this article, the contribution of the electron spin on the growth rate of the temperature anisotropy of electromagnetic instabilities has been investigated. Results show that polarisation of the electron spin will restrict the instability growth rate while instability can survive due to the spin-depolarised electrons even when the requested temperature anisotropy is vanished. Instability can reach the damping state exponentially due to the spin-polarised electrons while it can grow linearly due to the spin-depolarised (the semi-classical) electrons.     

Investigating pedestrian evacuation using ant algorithms

We have presented non-linear analytical formula for fusion–fission cross-sections. This is achieved by analysing many fusion–fission experiments of the compound nuclei of atomic number range \(23 \le Z \le 146\) available in literature. Our parametrised formula can reproduce the fusion–fission cross-sections which agree well with the experiments. Our parametrisations depend on the charges and masses of the compound nuclei and fission fragments only. These results can be used as a guideline for estimating the fusion–fission cross-sections in those cases where measurements do not exist and also for studying new nuclei which are not yet explored.