New nanomolar negative modulators of AMPA receptors

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Bursting synchronization in networks with long-range coupling mediated by a diffusing chemical substance

Many networks of physical and biological interest are characterized by a long-range coupling mediated by a chemical which diffuses through a medium in which oscillators are embedded. We considered a one-dimensional model for this effect for which the diffusion is fast enough so as to be implemented through a coupling whose intensity decays exponentially with the lattice distance. In particular, we analyzed the bursting synchronization of neurons described by two timescales (spiking and bursting activity), and coupled through such a long-range interaction network. One of the advantages of the model is that one can pass from a local (Laplacian) type of coupling to a global (all-to-all) one by varying a single parameter in the interaction term. We characterized bursting synchronization using an order parameter which undergoes a transition as the coupling parameters are changed through a critical value. We also investigated the role of an external time-periodic signal on the bursting synchronization properties of the network. We show potential applications in the control of pathological rhythms in biological neural networks.     

Electrophysiological Activity and Survival Rate of Rats Nervous Tissue Cells Depends on D/H Isotopic Composition of Medium

The deuterium content modification in an organism has a neuroprotective effect during the hypoxia model, affecting anxiety, memory and stress resistance. The aim of this work was to elucidate the possible mechanisms of the medium D/H composition modification on nerve cells. We studied the effect of an incubation medium with a 50 ppm deuterium content compared to a medium with 150 ppm on: (1) the activity of Wistar rats’ hippocampus CA1 field neurons, (2) the level of cultured cerebellar neuron death during glucose deprivation and temperature stress, (3) mitochondrial membrane potential (MMP) and the generation of reactive oxygen species in cultures of cerebellar neurons. The results of the analysis showed that the incubation of hippocampal sections in a medium with a 50 ppm deuterium reduced the amplitude of the pop-spike. The restoration of neuron activity was observed when sections were returned to the incubation medium with a 150 ppm deuterium content. An environment with a 50 ppm deuterium did not significantly affect the level of reactive oxygen species in neuron cultures, while MMP decreased by 16–20%. In experiments with glucose deprivation and temperature stress, the medium with 50 ppm increased the death of neurons. Thus, a short exposure of nerve cells in the medium with 50 ppm deuterium acts as an additional stressful factor, which is possibly associated with the violation of the cell energy balance. The decrease in the mitochondrial membrane potential, which is known to be associated with ATP synthesis, indicates that this effect may be associated with the cell energy imbalance. The decrease in the activity of the CA1 field hippocampal neurons may reflect reversible adaptive changes in the operation of fast-reacting ion channels.     

Neural spike renormalization. Part I — Universal number 1

For a class of circuit models for neurons, it has been shown that the transmembrane electrical potentials in spike bursts have an inverse correlation with the intra-cellular energy conversion: the fewer spikes per burst the more energetic each spike is. Here we demonstrate that as the per-spike energy goes down to zero, a universal constant to the bifurcation of spike-bursts emerges in a similar way as Feigenbaum's constant does to the period-doubling bifurcation to chaos generation, and the new universal constant is the first natural number 1.     

期货价格预测——反向传播神经网络模型

利用神经网络的 BP算法 ,对较难解决的期货价格问题做了一些研究 ,获得了一定的成功 .     

Neuronal RNA granule contains ApCPEB1, a novel cytoplasmic polyadenylation element binding protein, in Aplysia sensory neuron

The cytoplasmic polyadenylation element (CPE)-binding protein (CPEB) binds to CPE containing mRNAs on their 3'' untranslated regions (3''UTRs). This RNA binding protein comes out many important tasks, especially in learning and memory, by modifying the translational efficiency of target mRNAs via poly (A) tailing. Overexpressed CPEB has been reported to induce the formation of stress granules (SGs), a sort of RNA granule in mammalian cell lines. RNA granule is considered to be a potentially important factor in learning and memory. However, there is no study about RNA granule in Aplysia. To examine whether an Aplysia CPEB, ApCPEB1, forms RNA granules, we overexpressed ApCPEB1-EGFP in Aplysia sensory neurons. Consistent with the localization of mammalian CPEB, overexpressed ApCPEB1 formed granular structures, and was colocalized with RNAs and another RNA binding protein, ApCPEB, showing that ApCPEB1 positive granules are RNA-protein complexes. In addition, ApCPEB1 has a high turnover rate in RNA granules which were mobile structures. Thus, our results indicate that overexpressed ApCPEB1 is incorporated into RNA granule which is a dynamic structure in Aplysia sensory neuron. We propose that ApCPEB1 granule might modulate translation, as other RNA granules do, and furthermore, influence memory.     

Multiplicity of metastable retrieval phases in networks of multistate neurons

It is demonstrated that networks of multistate neurons storing an ensemble of multistate patterns exhibit a multiplicity of metastable retrieval phases. These phases are described by solutions of fixed-point equations with characteristic retrieval errors. They emerge if the gain of the neural input-output relation is varied. The number of these phases increases asQ ² with the numberQ of gray levels available to each neuron. Implications for the optimal gain function and basins of attraction are briefly discussed. Again, networks endowed with pseudoinverse couplings are found to perform better than networks with Hebbian couplings: at moderate loading levels phases with retrieval errors are destabilized, whereas the error-free phase remains stable up to the theoretically possible maximum, if the gain parameter is properly chosen.     

神经元的形态分类和识别

对神经元特性的认识,最基本问题是神经元的分类.神经元的形态复杂多样,神经元的识别分类问题有相当的困难.对提供的参考数据进行统计分析,将其转化成对应每类神经元的空间形态的指标参数,然后对每个指标进行定性分析和对比分析,确定对各类型神经元细胞识别起关键作用的指标,利用层次分析法、图形截面、几何相似性等分析方法建立神经元的分类模型.分类模型能够将附录提供的神经元进行准确的分类.数据分析采用开源科学计算软件Scilab进行处理,运行环境为Scilab5.2,部分代码采用C#基于VS2008开发.     

Parallel Dynamics of Fully Connected Q-Ising Neural Networks

Using a probabilistic approach, the parallel dynamics of fully connected Q-Ising neural networks is studied for arbitrary Q. A novel recursive scheme is set up to determine the time evolution of the order parameters through the evolution of the distribution of the local field, taking into account all feedback correlations. In contrast to extremely diluted and layered network architectures, the local field is no longer normally distributed but contains a discrete part. As an illustrative example, an explicit analysis is carried out for the first four time steps. For the case of the Q = 2 and Q = 3 model the results are compared with extensive numerical simulations and excellent agreement is found. Finally, equilibrium fixed-point equations are derived and compared with the thermodynamic approach based upon the replica-symmetric mean-field approximation.