IPA型菲涅尔全息联想存贮器

本文提出了一种用菲涅尔全息片实现的IPA(Interpattern Association)型联想存贮器。其对不独立的存贮模式有较强的分辨能力,采用菲涅尔全息片,可以实现大神经元数目的光学联想存储,具有较大的存储能力。     

洗牌型图样间联想光学神经网络模型

本文将洗牌型神经网络结构和图样间联想神经网络算法相结合,提出了一种洗牌型图样间联想神经网络(PS-IPA)模型。该模型具有极其简单、稀疏的互连权矩阵,十分适于大规模神经网络的光学实现。计算机模拟结果表明洗牌型图样间联想神经网络的稳定性和抑制噪音的能力均优于图样间联想网络IPA.本文还给出了洗牌互连的一般性原则,使网络结构得到优化,增强了洗牌型神经网络的灵活性和适应性。并采用3-洗牌和2-洗牌结合的PS-IPA对汽车牌照的字符进行识别,得到了较好的结果。     

洗牌型光电混合神经网络实验系统

在研究洗牌网局部互连光学实现问题的基础上 ,采用洗牌型图样间联想模型建立了具有完整的加权互连、求和、非线性处理及反馈功能的光电混合神经网络实验系统 ,进行了 8× 8数字样本的光学联想识别。实验结果证实了洗牌网理论的可行性和洗牌型图样间联想模型光学实现的优越性     

双极双向联想存储器的单通道光学实现

以附加神经元引入附加背景的方式获得将线性离散比极神经元的神经网络在单通道光学矢量-矩阵乘法器内实现的方法,给出了相应的光学系统的修正和非负光学模板的编码形式.以双极神经元的双向联想存储器为例进行了计算机和光电实验模拟.     

多值神经网络改进模型及其光学实现

本文提出一种改进的光学神经网络模型,并利用空间光调制器PROM构成的光学系统实现了这种模型的联想记忆运算.计算机模拟和实验结果表明,改进模型提高了光学神经网络的识别能力,并在—定程度上提高了存贮容量.     

高容量的单极二值互连神经网络联想存储模型

由于光学固有的数值精度低,难以表示负值等弱点,用光学方法实现神经网络时存在着许多困难。针对光学的弱点,本文提出并建立了具有单极二值互连的适应截值模型,这一模型避开了光学实现时难以表示负值和互连精度差等弱点,计算机模拟及光学实现结果表明,这种单极互连神经网络模型同其他的单极模型相比具有高的存储容量及较强的寻址能力。     

自构建小波神经网络的内模控制研究与应用

考虑到小波神经网络隐含层神经元的数目决定了整个网络的规模和性能,根据小波基函数的激励强度和衰减程度可以添加或者删除小波神经网络隐含层神经元,优化了小波神经网络隐含层结构,采用自构建小波神经网络辨识内模控制系统的正模型和逆模型,该模型的神经网络结构可根据性能要求动态调整,从而改进了神经网络内模控制技术,实验结果表明,提出的控制方法比传统方法在鲁棒性和抗扰性方面具有更好的性能表现,各项指标均优于传统控制方法,实现氧化铝熟料烧结工艺优化。     

用于相似度并行测定的光学双极互连网络

本文提出一种实验系统，用非相干光互连网络并行测量输入模式与所有存储样本之间的相似度，并与电子ＷＴＡ网络级联以实现海明（Ｈａｍｍｉｎｇ）神经网络模型，文中给出了本系统的实验结果。     

用于光学神经网络硬件系统误差纠正的虚拟神经网络

提出了一种用于修正光学神经网络硬件系统误差的虚拟神经网络模型，光学实验结果表明该网络能够有效地消除硬件系统误差对实验结果的影响。     

适用于神经元状态非等概率分布的神经网络模型及其光学实现

针对Hopfield模型在存储模式的神经元状态不具备理想的等概率分布时性能下降，以及光学难以实现多灰度阶互连的弱点，提出了一种非对称截值点的截值模型，在易于光学或光电子技术实现的同时，与其他模型相比，存储容量和容噪声能力都有较大提高.同时，提出了光束方向编码方法，并用该方法实现了上述模型，给出了实验结果. 关键词：     

基于抑制性突触可塑性的反馈神经回路兴奋性与抑制性动态平衡

大脑皮层的兴奋性与抑制性平衡是维持正常脑功能的前提, 而其失衡会诱发癫痫、帕金森、抑郁症等多种神经疾病, 因此兴奋性与抑制性平衡的研究是脑科学领域的核心科学问题. 反馈神经回路是脑皮层网络的典型连接模式, 抑制性突触可塑性在兴奋性与抑制性平衡中扮演关键角色. 本文首先构建具有抑制性突触可塑性的反馈神经回路模型; 然后通过计算模拟研究揭示在抑制性突触可塑性的调控下反馈神经回路的兴奋性与抑制性可取得较高程度的动态平衡, 并且二者的平衡对输入扰动具有较强的鲁棒性; 其次给出了基于抑制性突触可塑性的反馈神经回路兴奋性与抑制性平衡机理的解释; 最后发现反馈回路神经元数目有利于提高兴奋性与抑制性平衡的程度, 这在一定程度上解释了为何神经元之间会存在较多的连接. 本文的研究对于理解脑皮层的兴奋性与抑制性动态平衡机理具有重要的参考价值.     

Intermittent synchronization in a network of bursting neurons

Synchronized oscillations in networks of inhibitory and excitatory coupled bursting neurons are common in a variety of neural systems from central pattern generators to human brain circuits. One example of the latter is the subcortical network of the basal ganglia, formed by excitatory and inhibitory bursters of the subthalamic nucleus and globus pallidus, involved in motor control and affected in Parkinson's disease. Recent experiments have demonstrated the intermittent nature of the phase-locking of neural activity in this network. Here, we explore one potential mechanism to explain the intermittent phase-locking in a network. We simplify the network to obtain a model of two inhibitory coupled elements and explore its dynamics. We used geometric analysis and singular perturbation methods for dynamical systems to reduce the full model to a simpler set of equations. Mathematical analysis was completed using three slow variables with two different time scales. Intermittently, synchronous oscillations are generated by overlapped spiking which crucially depends on the geometry of the slow phase plane and the interplay between slow variables as well as the strength of synapses. Two slow variables are responsible for the generation of activity patterns with overlapped spiking, and the other slower variable enhances the robustness of an irregular and intermittent activity pattern. While the analyzed network and the explored mechanism of intermittent synchrony appear to be quite generic, the results of this analysis can be used to trace particular values of biophysical parameters (synaptic strength and parameters of calcium dynamics), which are known to be impacted in Parkinson's disease.     

Synaptic inhibition and γ-aminobutyric acid in the mammalian central nervous system

Signal transmission through synapses connecting two neurons is mediated by release of neurotransmitter from the presynaptic axon terminals and activation of its receptor at the postsynaptic neurons. γ-Aminobutyric acid (GABA), non-protein amino acid formed by decarboxylation of glutamic acid, is a principal neurotransmitter at inhibitory synapses of vertebrate and invertebrate nervous system. On one hand glutamic acid serves as a principal excitatory neurotransmitter. This article reviews GABA researches on; (1) synaptic inhibition by membrane hyperpolarization, (2) exclusive localization in inhibitory neurons, (3) release from inhibitory neurons, (4) excitatory action at developmental stage, (5) phenotype of GABA-deficient mouse produced by gene-targeting, (6) developmental adjustment of neural network and (7) neurological/psychiatric disorder. In the end, GABA functions in simple nervous system and plants, and non-amino acid neurotransmitters were supplemented.     

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.     

On the Role of the Excitation/Inhibition Balance of Homeostatic Artificial Neural Networks

Homeostatic models of artificial neural networks have been developed to explain the self-organization of a stable dynamical connectivity between the neurons of the net. These models are typically two-population models, with excitatory and inhibitory cells. In these models, connectivity is a means to regulate cell activity, and in consequence, intracellular calcium levels towards a desired target level. The excitation/inhibition (E/I) balance is usually set to 80:20, a value characteristic for cortical cell distributions. We study the behavior of these homeostatic models outside of the physiological range of the E/I balance, and we find a pronounced bifurcation at about the physiological value of this balance. Lower inhibition values lead to sparsely connected networks. At a certain threshold value, the neurons develop a reasonably connected network that can fulfill the homeostasis criteria in a stable way. Beyond the threshold, the behavior of the artificial neural network changes drastically, with failing homeostasis and in consequence with an exploding number of connections. While the exact value of the balance at the bifurcation point is subject to the parameters of the model, the existence of this bifurcation might explain the stability of a certain E/I balance across a wide range of biological neural networks. Assuming that this class of models describes the self-organization of biological network connectivity reasonably realistically, the omnipresent physiological balance might represent a case of self-organized criticality in order to obtain a good connectivity while allowing for a stable intracellular calcium homeostasis.     

双耳幅值差确定声源方向的神经信息处理机理研究

神经信息系统实质上是定量系统, 应引起足够重视. 关于神经系统的定量研究方面的报道比较少见. 这一问题将会影响进一步的研究, 如双耳声音定向. 双耳定向是定量测量, 用定性分析的方法无法满足要求. 已有的生理实验发现声音输入信号强度与听觉神经的输出频率存在单调递增关系, 所以本文中声音强度的变化被简化成神经脉冲频率的变化. 本文基于圆映射和符号动力学原理, 建立了神经回路定量模型, 模型中对同侧输入回路采用兴奋性耦合, 对侧输入回路采用抑制性耦合, 并考虑神经元间突触连接的量子释放特征, 采用化学耦合模型实现连接, 用耦合系数表示神经元间的耦合程度. 采用Hodgkin-Huxley模型仿真研究听觉神经回路的输入/输出脉冲序列关系. 在已经仿真过的参数范围, 模型在输入信号变化与输出脉冲频率变化间存在单调递增/递减的关系. 对于单输入单输出的神经元, 采用符号动力学方法进行符号化; 对于多输入单输出的神经元, 采用分析各输出脉冲的产生时间, 判断其变化位置, 从神经脉冲序列中得到对应的两耳声音幅值差变化, 以此定位声源. 随着输出脉冲数的增加, 符号序列的长度增加, 符号序列对输入信号变化敏感, 能够得到较高的测量精度. 仿真结果表明这个模型是定量的, 神经脉冲序列能够区分信号的大小.     

Learning alters theta amplitude, theta-gamma coupling and neuronal synchronization in inferotemporal cortex

Background

How oscillatory brain rhythms alone, or in combination, influence cortical information processing to support learning has yet to be fully established. Local field potential and multi-unit neuronal activity recordings were made from 64-electrode arrays in the inferotemporal cortex of conscious sheep during and after visual discrimination learning of face or object pairs. A neural network model has been developed to simulate and aid functional interpretation of learning-evoked changes.

Results

Following learning the amplitude of theta (4-8 Hz), but not gamma (30-70 Hz) oscillations was increased, as was the ratio of theta to gamma. Over 75% of electrodes showed significant coupling between theta phase and gamma amplitude (theta-nested gamma). The strength of this coupling was also increased following learning and this was not simply a consequence of increased theta amplitude. Actual discrimination performance was significantly correlated with theta and theta-gamma coupling changes. Neuronal activity was phase-locked with theta but learning had no effect on firing rates or the magnitude or latencies of visual evoked potentials during stimuli. The neural network model developed showed that a combination of fast and slow inhibitory interneurons could generate theta-nested gamma. By increasing N-methyl-D-aspartate receptor sensitivity in the model similar changes were produced as in inferotemporal cortex after learning. The model showed that these changes could potentiate the firing of downstream neurons by a temporal desynchronization of excitatory neuron output without increasing the firing frequencies of the latter. This desynchronization effect was confirmed in IT neuronal activity following learning and its magnitude was correlated with discrimination performance.

Conclusions

Face discrimination learning produces significant increases in both theta amplitude and the strength of theta-gamma coupling in the inferotemporal cortex which are correlated with behavioral performance. A network model which can reproduce these changes suggests that a key function of such learning-evoked alterations in theta and theta-nested gamma activity may be increased temporal desynchronization in neuronal firing leading to optimal timing of inputs to downstream neural networks potentiating their responses. In this way learning can produce potentiation in neural networks simply through altering the temporal pattern of their inputs.     

Coherent periodic activity in excitatory Erdös-Renyi neural networks: the role of network connectivity

In this article, we investigate the role of connectivity in promoting coherent activity in excitatory neural networks. In particular, we would like to understand if the onset of collective oscillations can be related to a minimal average connectivity and how this critical connectivity depends on the number of neurons in the networks. For these purposes, we consider an excitatory random network of leaky integrate-and-fire pulse coupled neurons. The neurons are connected as in a directed Erdo?s-Renyi graph with average connectivity scaling as a power law with the number of neurons in the network. The scaling is controlled by a parameter γ, which allows to pass from massively connected to sparse networks and therefore to modify the topology of the system. At a macroscopic level, we observe two distinct dynamical phases: an asynchronous state corresponding to a desynchronized dynamics of the neurons and a regime of partial synchronization (PS) associated with a coherent periodic activity of the network. At low connectivity, the system is in an asynchronous state, while PS emerges above a certain critical average connectivity (c). For sufficiently large networks, (c) saturates to a constant value suggesting that a minimal average connectivity is sufficient to observe coherent activity in systems of any size irrespectively of the kind of considered network: sparse or massively connected. However, this value depends on the nature of the synapses: reliable or unreliable. For unreliable synapses, the critical value required to observe the onset of macroscopic behaviors is noticeably smaller than for reliable synaptic transmission. Due to the disorder present in the system, for finite number of neurons we have inhomogeneities in the neuronal behaviors, inducing a weak form of chaos, which vanishes in the thermodynamic limit. In such a limit, the disordered systems exhibit regular (non chaotic) dynamics and their properties correspond to that of a homogeneous fully connected network for any γ-value. Apart for the peculiar exception of sparse networks, which remain intrinsically inhomogeneous at any system size.