zinc is a substance that regulates neural excitability by binding whit sodium channel and potassium channel. the efficiency of free zinc ion, make down the neural survival rate, reduced the peak amplitude of na+ and make depolarization na channel, increased the peak amplitude of transition outward k+ currents and delayed rectifier. also it is an effective blocker of one subtype of tetrodoxine (...

in the bone marrow, there are certain populations of stem cell sources with the capacity to differentiate into several different types of cells. ideally, cell transplants would be readily obtainable, easy to expand and bank, and capable of surviving for sufficient periods of time. bone marrow mesenchymal stem cells (bm-mscs) possess all of these characteristics. one of the most important benefi...

Experiments showed that a neuron can fire when its membrane potential (an intrinsic quality related to electrical charge) reaches specific threshold. On theoretical studies, there are two crucial issues in exploring cortical neuronal dynamics: (i) what model describes spiking dynamics of each neuron, and (ii) how the neurons connected [E. M. Izhikevich, IEEE Trans. Neural Networks, 15 (2004)]. ...

In several neurological diseases, like essential tremor, the functions of the brain are severely impaired by synchronized processes, in which the neurons fire in a synchronized periodical manner at a frequency closely related to that of the tremor. Stimulation techniques have been developed to desynchronize these neuronal populations. One such technique is the electrical Deep Brain Stimulation ...

The present thesis introduces Clifford Algebra as a framework for neural computation. Clifford Algebra subsumes, for example, the reals, complex numbers and quaternions. Neural computation with Clifford algebras is model–based. This principle is established by constructing Clifford algebras from quadratic spaces. Then the subspace grading inherent to any Clifford algebra is introduced, which al...

Spiking neural network is a type of artificial neural network in which neurons communicate between each other with spikes. Spikes are identical Boolean events characterized by the time of their arrival. A spiking neuron has internal dynamics and responds to the history of inputs as opposed to the current inputs only. Because of such properties a spiking neural network has rich intrinsic capabil...

In this paper, the time for energy relaxation for LittleHopfield neural network using the new activation rule is shown to be better than the relaxation time using Hebbian learning. However, this should be so given the characteristics of the activation function and show through computer simulations that this is indeed so. In this paper, it has been proven that the new learning rule has a higher ...

In coupled learning rules for principal component analysis, eigenvectors and eigenvalues are simultaneously estimated in a coupled system of equations. Coupled single-neuron rules have favorable convergence properties. For the estimation of multiple eigenvectors, orthonormalization methods have to be applied, either full Gram-Schmidt orthonormalization, its first-order approximation as used in ...