The electrical behavior of neurons can be more complex in the presence of autapse. In the presence of an autaptic connection, the Izhikevich neuron model can show a variety of dynamic behaviors, such as chaotic behavior. This paper presents a novel, high speed and robust pseudo random number generator based on the autaptic Izhikevich neuron oscillator and its FPGA implementation. The autaptic Izhikevich neuron model is simulated and dynamically analyzed. Then, the proposed pseudo-random number generator is modeled and simulated using the Xilinx system generator platform, synthesized using Xilinx Synthesis Tool, and implemented on the XILINX SPARTAN-6 XC6SLX9 FPGA evaluation board. As a post processing operation, the XOR function is used to increase the randomness of the output bit sequences. The FPGA implementation results show that the implementation cost of the proposed pseudo-random number generator is lower than similar works, and the proposed generator achieves a maximum frequency of 63.2 MHz. The NIST test suite is used for testing the quality of the generated bit sequences. The NIST test results indicates the high quality of the generated random bit sequence.

This paper is concerned with stochastic stability and stochastic bifurcation of the Fitzhug-Nagumo model with multiplicative white noise. We employ largest Lyapunov exponent and singular boundary theory to investigate local and global stochastic stability at the equilibrium point. In the rest, the solution of averaging the Ito diffusion equation and extreme point of steady-state probability density function provide sufficient conditions that the stochastic system undergoes pitchfork and phenomenological bifurcations. These theoretical results of the stochastic neuroscience model are confirmed by some numerical simulations and stochastic trajectories. Finally, we compare this approach with Rulkov approach and explain how pitchfork and phenomenological bifurcations describe spiking limit cycles and stability of neuron's resting state.

Objective(s): Cell therapy has provided clinical applications to the treatment of motor neuron diseases. The current obstacle in stem cell therapy is to direct differentiation of stem cells into neurons in the neurodegenerative disorders. Biomaterial scaffolds can improve cell differentiation and are widely used in translational medicine and tissue engineering. The aim of this study was to compare the efficiency of two-dimensional with a three-dimensional culture system in their ability to generate functional motor neuron-like cells from adipose-derived stem cells. Materials and Methods: We compared motor neuron-like cells derived from rat adipose tissue in differentiation, adhesion, proliferation, and functional properties on two-dimensional with threedimensional culture systems. Neural differentiation was analyzed by immunocytochemistry for immature (Islet1) and mature (HB9, ChAT, and synaptophysin) motor neuron markers. Results: Our results indicated that the three-dimensional environment exhibited an increase in the number of Islet1. In contrast, two-dimensional culture system resulted in more homeobox gene (HB9), Choline Acetyltransferase (ChAT), and synaptophysin positive cells. The results of this investigation showed that proliferation and adhesion of motor neuron-like cells significantly increased in threedimensional compared with two-dimensional environments. Conclusion: The findings of this study suggested that three-dimension may create a proliferative niche for motor neuron-like cells. Overall, this study strengthens the idea that three-dimensional culture may mimic neural stem cell environment for neural tissue regeneration.

Analysis and design of space structures are normally time consuming since a large number of members is involved. The problem becomes more involved when optimization is performed, where numerous analyses should be carried out. In this paper, neural network and genetic algorithm are applied for optimization of double steel layer grids. 180 models with three topologies, spans between 10 and 75 m and height between I and 2.5 m are analyzed and designed for optimum weight. The results have been used for training and testing of proposed neural networks system to predict the weight of the structures. The genetic algorithm based on neural networks is used for design optimization. Thus, for a site plan with arbitrary length and width, the output of the neurongenetic system is the selection of a double steel grid with optimized shape based on three available topologies with height, distance between columns and length of horizontal members.

Background: When two eyes are faced with two completely different sights, corresponding areas of the two eyes retina triggered with entirely two different stimulus and instead of receiving a fixed and stable image of two stimulus, brain perceive the alternation of the two images. This phenomenon is known as an intelligent tool to study cognitive processes.Methods: Taking a network of excitatory and inhibitory Hodgkin-Huxley type neuron in network architecture of visual cortex, the behavior of coupled spiking neuron in cortical brain that have a direct effect on the emergence of this phenomenon was modeled. To evaluate the performance of this model, direction lines as the two stimuli were applied to the eyes.Findings: Rebuilding the predominance time of each eye, and independence between the two successive dominance times were reported by this model. In addition, the effect of changes in the stimulus applied to each of the two eyes was studied and it was figured out that applied changing stimulus strength to one eye was affected only on dominance time of the other eye. Besides, increasing stimulus strength of both eyes decreased mean dominance time.Conclusion: Obtained results of this model were consistent with known experimental result for this phenomenon and many of dynamics features of this phenomenon reconstructed. Discussed theories in this field were also confirmed.