单神经元分辨水平的小鼠全脑网络可视化

脑科学是当今国际科技研究的前沿领域。脑是最复杂的器官,其中尚有诸多重大的基础科学问题有待解决。开展脑科学研究需多学科人员协同攻关,对于建立新学科将有极大的促进作用,对于人类健康和社会发展具有巨大的推动作用。简述了神经科学在结构成像方面的基础性需求,介绍了小鼠全脑可视化的发展历程以及近几年的代表性研究,并展望了全脑可视化研究的发展趋势,对可能存在的难点予以说明。     

Sequential behavior and stability properties of enzymatic neuron networks

The cycle structure of enzymatic neural networks may be characterized in terms of number of cycles exhibited, size of cycle state sets and cycle lengths. Simulation experiments show that the stability properties of these networks have some unusual features which are not exhibited by networks of two-state switching elements or by randomly constructed ecosystem models. The behavioral and structural stability of these systems decreases with their structural complexity, as measured by the number of components. The behavioral and structural stability of enzymatic neural networks also decreases with structural complexity, as measured by the number of excitase types, but only up to the middle level of excitases per neuron. This is the point of highest potential responsiveness of the system to environmental stimuli. Beyond this point the behavioral and structural stability increase. This is due to the fact that the number of possible states increases up to this point and decreases beyond it. The number of possible states, not the number of components, serves as the useful measure of complexity in these types of systems. The selection circuits learning algorithm has been used to evolve networks whose cycle structures have desired features.     

Biochemical switching device realizing McCulloch-Pitts type equation

We analyzed cyclic enzyme systems, one of the best candidates for biochemical switching devices, especially focusing on their control mode against external perturbations. Since these systems have the reliability of ON-OFF types of operation (McCulloch-Pitts' neuronic equation), we shall present here the mechanical difference between these systems and electronic switching circuit, especially on the mnemonic mechanism of biochemical switching devices.     

On the ability of neural networks to perform generalization by induction

The ability of neural networks to perform generalization by induction is the ability to learn an algorithm without the benefit of complete information about it. We consider the properties of networks and algorithms that determine the efficiency of generalization. These properties are described in quantitative terms. The most effective generalization is shown to be achieved by networks with the least admissible capacity. General conclusions are illustrated by computer simulations for a three-layered neural network. We draw a quantitative comparison between the general equations and specific results reported here and elsewhere.     

The linear theory of neuron networks: The static problem

The construction of a theory of activity in neuron networks of arbitrary topological structure is commenced under the linear excitation hypothesis: we consider conditions for possible steady-state equilibria, deferring a dynamical treatment to the sequel.     

The linear theory of neuron networks: The dynamic problem

The development of a general theory of neuron-networks is here extended to cases of non-steady state activity. Conditions for stability and neutrality of an equilibrium point are set up, and the possible functions representing the variation of excitation over time are enumerated. The inverse network problem is considered—which is, given a preassigned pattern of activity over time, to construct when possible a neuron-network having this pattern. Finally, a canonical form for neuron networks is derived, in the sense of a network of a certain special topological structure which is equivalent in activity characteristics to any given network.     

The relative importance of color signaling for plant generalization in pollination networks

Plant strategies frequently vary from opportunistic pollination to specialization to single pollinators within the same community. Unraveling the proximate mechanisms that determine the degree of plant generalization to pollinators has become a primary goal of pollination ecology. Color signaling is a potentially important mechanism because it is well established that many pollinators use color stimuli to locate food items. Until now, studies on the importance of color signaling in structuring pollination networks have not considered floral coloration as it is perceived by pollinators. Here, we use a framework recently developed for network analyses to compare the relative importance of color matching (i.e. the degree of phenotypic matching between flower coloration and pollinators’ visual system) and other variables (phylogeny, co‐abundance and spatiotemporal overlap between plants and pollinators) for plant generalization. We analyzed 25 000 visits in three temperate regions. We show that color matching in combination with spatiotemporal overlap or co‐abundance significantly influences plant generalization in one of the three regions. We suggest that intense human activities in two regions have decreased the mean level of color matching, potentially disrupting the communication between plants and pollinators. This study illuminates how the sensory ecology of pollinators contributes to structure a highly diversified pollination network.     

人工神经网络模型在水稻群体分蘖动态模拟中外推能力的测试

研究利用人工神经网络模型 ,以水稻群体分蘖动态为例 ,采用交互验证和独立验证的方式 ,对水稻生长 BP网络模型进行了训练与模拟 ,其结果与水稻群体分蘖的积温统计模型、基本动力学模型和复合分蘖模型进行了比较。研究结果表明 ,神经网络模型具有一定的外推能力 ,但其外推能力依赖于大量的训练样本。神经网络模型具有较好的拟合能力 ,是因为有较多的模型参数 ,因此对神经网络模型的训练需要大量的参数来保证其参数不致过度吻合。具有外推能力神经网络模型的最少训练样本数应大于 6 .75倍于神经网络参数数目 ,小于 13.5倍于神经网络参数数目。因此在应用神经网络模型时 ,如果神经网络模型包括较多的输入变量时 ,可考虑采用主成分分析、对应分析等技术对输入变量进行信息综合 ,相应地减少网络模型的参数。另一方面 ,当训练样本不足时 ,最好只用神经网络模型对同一系统的情况进行模拟 ,应谨慎使用神经网络模型进行外推。神经网络模型给作物模拟研究的科学工作者提供了一个“傻瓜”式工具 ,对数学建模不熟悉的农业研究人员 ,人工神经网络可以替代数学建模进行仿真实验 ;对于精通数学建模的研究人员来说 ,它至少是一种补充和可作为比较的非线性数据处理方法     

A selective learning method to improve the generalization of multilayer feedforward neural networks

Multilayer feedforward neural networks with backpropagation algorithm have been used successfully in many applications. However, the level of generalization is heavily dependent on the quality of the training data. That is, some of the training patterns can be redundant or irrelevant. It has been shown that with careful dynamic selection of training patterns, better generalization performance may be obtained. Nevertheless, generalization is carried out independently of the novel patterns to be approximated. In this paper, we present a learning method that automatically selects the training patterns more appropriate to the new sample to be predicted. This training method follows a lazy learning strategy, in the sense that it builds approximations centered around the novel sample. The proposed method has been applied to three different domains: two artificial approximation problems and a real time series prediction problem. Results have been compared to standard backpropagation using the complete training data set and the new method shows better generalization abilities.     

A quantitative method for analysing neuron networks marked by acetylcholinesterase histochemistry

Enzyme histochemistry is frequently used in classical morphological studies for the qualitative analysis of neuronal networks. However, this procedure does not readily provide quantitative results. Two new alternative approaches based on digital image processing techniques were explored and the data quality compared. The preliminary results explored the feasibility of these approaches in the applied setting.     

A note on the McCulloch-Pitts neural net for heat-cold discrimination

An error is pointed out in the neural net model which had been previously proposed to explain the heat-cold responses that depended on the duration of the stimulus. A corrected net for the effect is given. Contribution no. 3 of the Department of Biophysics of the University of Pittsburgh.     

How training and testing histories affect generalization: a test of simple neural networks

We show that a simple network model of associative learning can reproduce three findings that arise from particular training and testing procedures in generalization experiments: the effect of (i) 'errorless learning', (ii) extinction testing on peak shift, and (iii) the central tendency effect. These findings provide a true test of the network model which was developed to account for other phenomena, and highlight the potential of neural networks to study the phenomena that depend on sequences of experiences with many stimuli. Our results suggest that at least some such phenomena, e.g. stimulus range effects, may derive from basic mechanisms of associative memory rather than from more complex memory processes.     

A neuron model as an universal element of self-learning networks for pattern recognition

A mathematical model of the neuron, socalled D-neuron, is proposed on the basis of some new conceptions concerning the molecular mechanism of the synaptical memory. According to these conceptions, the receptors of the neuron reception surface are divided into functional independent fields of receptors. The receptors of any field belong to corresponding membrane protein complex which contains moreover Na⁺-channels, K⁺-channels and eventually other protein subunits. Three processes are supposed to take place in any complex by its interaction with chemical transmitters: i cooperative transitions of the subunits, ii time-controlled transport of ions and iii changes of concentrations of the protein complex subunits. These processes correspond to the following information processings: i recording in the memory, ii discrimination and iii accomodation. In this paper they all are described by an idealized system of algebraic and differential equations. The proposed neuron model can account for the short- and long-term memory mechanism on the level of a single neuron as well as for the control of the neuron networks by the hormones. finally, the neuron model is presented as a universal unit of self-learning networks.     

Discussion: McCulloch-Pitts and related neural nets from 1943 to 1989

The McCulloch-Pitts paper “A Logical Calculus of the Ideas Immanent in Nervous Activity” was published in theBulletin of Mathematical Biophysics in 1943, a decade before the work of Hodgkin, Huxley, Katz and Eccles. The McCulloch-Pitts neuron is an extremely simplified representation of neural properties, based simply on the existence of a threshold for the activation of an action potential. This work has been supported in part by Grants from the University of Chicago Brain Research Foundation, and the U.S. Department of the Navy, Office of Naval Research (Grant No. N 00014-89J-1099).     

Dynamical patterns of calcium signaling in a functional model of neuron–astrocyte networks

We propose a functional mathematical model for neuron-astrocyte networks. The model incorporates elements of the tripartite synapse and the spatial branching structure of coupled astrocytes. We consider glutamate-induced calcium signaling as a specific mode of excitability and transmission in astrocytic–neuronal networks. We reproduce local and global dynamical patterns observed experimentally.     

Dynamics of spontaneous activity in random networks with multiple neuron subtypes and synaptic noise

During slow-wave sleep, brain electrical activity is dominated by the slow (< 1 Hz) electroencephalogram (EEG) oscillations characterized by the periodic transitions between active (or Up) and silent (or Down) states in the membrane voltage of the cortical and thalamic neurons. Sleep slow oscillation is believed to play critical role in consolidation of recent memories. Past computational studies, based on the Hodgkin-Huxley type neuronal models, revealed possible intracellular and network mechanisms of the neuronal activity during sleep, however, they failed to explore the large-scale cortical network dynamics depending on collective behavior in the large populations of neurons. In this new study, we developed a novel class of reduced discrete time spiking neuron models for large-scale network simulations of wake and sleep dynamics. In addition to the spiking mechanism, the new model implemented nonlinearities capturing effects of the leak current, the Ca²⁺ dependent K⁺ current and the persistent Na⁺ current that were found to be critical for transitions between Up and Down states of the slow oscillation. We applied the new model to study large-scale two-dimensional cortical network activity during slow-wave sleep. Our study explained traveling wave dynamics and characteristic synchronization properties of transitions between Up and Down states of the slow oscillation as observed in vivo in recordings from cats. We further predict a critical role of synaptic noise and slow adaptive currents for spike sequence replay as found during sleep related memory consolidation.     

A hidden threshold in motor neuron gene networks revealed by modulation of miR-218 dose

1. Download : Download high-res image (258KB)
2. Download : Download full-size image

     

Influence of time delay and channel blocking on multiple coherence resonance in Hodgkin-Huxley neuron networks

Toxins such as tetraethylammonium (TEA) and tetrodotoxin (TTX) may reduce the number of working potassium and sodium ion channels by poisoning and making them blocked, respectively. In this paper, we study how channel blocking (CB) affects the time delay-induced multiple coherence resonance (MCR), i.e., a phenomenon that the spiking of neuronal networks intermittently reaches the most ordered state, in stochastic Hodgkin-Huxley neuron networks. It is found that potassium and sodium CB have distinct effects. For potassium CB, the MCR occurs more frequently as the CB develops, but for sodium CB the MCR is badly impaired and only the first coherence resonance (CR) holds and, consequently, the MCR evolves into a single CR as sodium CB develops. We found for sodium CB the spiking becomes disordered at larger delay lengths, which may be the reason for the destruction of the MCR. The underlying mechanism is briefly discussed in terms of distinct effects of potassium and sodium CB on the spiking activity. These results show that potassium CB can increase the frequency of MCR with time delay, but sodium CB may suppress and even destroy the delay-induced MCR. These findings may help to understand the joint effects of CB and time delay on the spiking coherence of neuronal networks.