Robust emergence of small-world structure in networks of spiking neurons

Spontaneous activity in biological neural networks shows patterns of dynamic synchronization. We propose that these patterns support the formation␣of a small-world structure—network connectivity␣optimal for distributed information processing. We␣present numerical simulations with connected Hindmarsh–Rose neurons in which, starting from random connection distributions, small-world networks evolve as a result of applying an adaptive rewiring rule. The rule connects pairs of neurons that tend fire in synchrony, and disconnects ones that fail to synchronize. Repeated application of the rule leads to small-world structures. This mechanism is robustly observed for bursting and irregular firing regimes.     

A case for spiking neural network simulation based on configurable multiple-FPGA systems

Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.     

The selectivity of noise and coupling for coherence biresonance and array-enhanced coherence biresonance in coupled neural systems

The selectivity of noise and coupling for coherence biresonance (CBR) and array-enhanced coherence biresonance (AECBR) in coupled neural systems has been investigated. It is shown that, depending on the coupling strength and noise intensity, various coherence behaviors and phenomena are exhibited, including CBR, coherence resonance without tuning, AECBR and undamped signal transmission. There exist optimal coupling and noise regions for the occurrence of CBR and AECBR in the transmission of noise-induced oscillations (NIOs).     

Spike-time reliability of layered neural oscillator networks

We study the reliability of layered networks of coupled “type I” neural oscillators in response to fluctuating input signals. Reliability means that a signal elicits essentially identical responses upon repeated presentations, regardless of the network’s initial condition. We study reliability on two distinct scales: neuronal reliability, which concerns the repeatability of spike times of individual neurons embedded within a network, and pooled-response reliability, which concerns the repeatability of total synaptic outputs from a subpopulation of the neurons in a network. We find that neuronal reliability depends strongly both on the overall architecture of a network, such as whether it is arranged into one or two layers, and on the strengths of the synaptic connections. Specifically, for the type of single-neuron dynamics and coupling considered, single-layer networks are found to be very reliable, while two-layer networks lose their reliability with the introduction of even a small amount of feedback. As expected, pooled responses for large enough populations become more reliable, even when individual neurons are not. We also study the effects of noise on reliability, and find that noise that affects all neurons similarly has much greater impact on reliability than noise that affects each neuron differently. Qualitative explanations are proposed for the phenomena observed.

A stochastic population approach to the problem of stable recruitment hierarchies in spiking neural networks

Synchrony-driven recruitment learning addresses the question of how arbitrary concepts, represented by synchronously active ensembles, may be acquired within a randomly connected static graph of neuron-like elements. Recruitment learning in hierarchies is an inherently unstable process. This paper presents conditions on parameters for a feedforward network to ensure stable recruitment hierarchies. The parameter analysis is conducted by using a stochastic population approach to model a spiking neural network. The resulting network converges to activate a desired number of units at each stage of the hierarchy. The original recruitment method is modified first by increasing feedforward connection density for ensuring sufficient activation, then by incorporating temporally distributed feedforward delays for separating inputs temporally, and finally by limiting excess activation via lateral inhibition. The task of activating a desired number of units from a population is performed similarly to a temporal k-winners-take-all network.     

Efficiency of neural transmission as a function of synaptic noise, threshold, and source characteristics

There has been a growing interest in the estimation of information carried by a single neuron and multiple single units or population of neurons to specific stimuli. In this paper we analyze, inspired by article of Levy and Baxter (2002), the efficiency of a neuronal communication by considering dendrosomatic summation as a Shannon-type channel (1948) and by considering such uncertain synaptic transmission as part of the dendrosomatic computation. Specifically, we study Mutual Information between input and output signals for different types of neuronal network architectures by applying efficient entropy estimators. We analyze the influence of the following quantities affecting transmission abilities of neurons: synaptic failure, activation threshold, firing rate and type of the input source. We observed a number of surprising non-intuitive effects. It turns out that, especially for lower activation thresholds, significant synaptic noise can lead even to twofold increase of the transmission efficiency. Moreover, the efficiency turns out to be a non-monotonic function of the activation threshold. We find a universal value of threshold for which a local maximum of Mutual Information is achieved for most of the neuronal architectures, regardless of the type of the source (correlated and non-correlated). Additionally, to reach the global maximum the optimal firing rates must increase with the threshold. This effect is particularly visible for lower firing rates. For higher firing rates the influence of synaptic noise on the transmission efficiency is more advantageous. Noise is an inherent component of communication in biological systems, hence, based on our analysis, we conjecture that the neuronal architecture was adjusted to make more effective use of this attribute.     

Temporal correlation based learning in neuron models

We study a learning rule based upon the temporal correlation (weighted by a learning kernel) between incoming spikes and the internal state of the postsynaptic neuron, building upon previous studies of spike timing dependent synaptic plasticity (Kempter, R., Gerstner, W., van Hemmen, J.L., Wagner, H., 1998. Extracting Oscillations: Neuronal coincidence detection with noisy periodic spike input. Neural computation 10, 1987–2017; Kempter, R., Gerstner, W., van Hemmen, J.L., 1999. Hebbian learning and spiking neurons. Physical Reviewm E59, 4498–4514; van Hemmen, J.L., 2001. Theory of synaptic plasticity. In: Moss, F., Gielen, S. (Eds.), Handbook of biological physics. vol. 4, Neuro Informatics, neural modelling, Elsevier, Amsterdam, pp. 771–823. Our learning rule for the synaptic weight w _ij is where the t _j,μ are the arrival times of spikes from the presynaptic neuron j and the function u(t) describes the state of the postsynaptic neuron i. Thus, the spike-triggered average contained in the inner integral is weighted by a kernel Γ(s), the learning window, positive for negative, negative for positive values of the time difference s between post- and presynaptic activity. An antisymmetry assumption for the learning window enables us to derive analytical expressions for a general class of neuron models and to study the changes in input-output relationships following from synaptic weight changes. This is a genuinely non-linear effect (Song, S., Miller, K., Abbott, L., 2000. Competitive Hebbian learning through spike timing dependent synaptic plasticity. Nature Neuroscience 3, 919–926).     

Effect of steady-state response versus excitatory/inhibitory balance on spiking synchronization in neural networks with log-normal synaptic weight distribution

Synchronization of neural activity, especially at the gamma band, contributes to perceptual functions. In several psychiatric disorders, deficits of perceptual functions are reflected in synchronization abnormalities. Plausible cause of this impairment is an alteration in the balance between excitation and inhibition (E/I balance); a disruption in the E/I balance leads to abnormal neural interactions reminiscent of pathological states. Moreover, the local lateral excitatory-excitatory synaptic connections in the cortex exhibit excitatory postsynaptic potentials (EPSPs) that follow a log-normal amplitude distribution. This long-tailed distribution is considered an important factor for the emergence of spatiotemporal neural activity. In this context, we hypothesized that manipulating the EPSP distribution under abnormal E/I balance conditions would provide insights into psychiatric disorders characterized by deficits in perceptual functions, potentially revealing the mechanisms underlying pathological neural behaviors. In this study, we evaluated the synchronization of neural activity with external periodic stimuli in spiking neural networks in cases of both E/I balance and imbalance with or without a long-tailed EPSP amplitude distribution. The results showed that external stimuli of a high frequency lead to a decrease in the degree of synchronization with an increasing ratio of excitatory to inhibitory neurons in the presence, but not in the absence, of high-amplitude EPSPs. This monotonic reduction can be interpreted as an autonomous, strong-EPSP-dependent spiking activity selectively interfering with the responses to external stimuli. This observation is consistent with pathological findings. Thus, our modeling approach has potential to improve the understanding of the steady-state response in both healthy and pathological states.     

Using interspike intervals to quantify noise effects on spike trains in temperature encoding neurons

This paper examines how noise interacts with the non-linear dynamical mechanisms of neuronal stimulus. We study the spike trains generated by a minimal Hodgkin-Huxley type model of a cold receptor neuron. The distributions of interspike intervals(ISIs) of purely deterministic simulations exhibit considerable differences compared to the noisy ones. We quantify the effect of noise using ISI return plots and the ISI-distance recently proposed by Kreuz et al. (J Neurosci Meth, 165:151–161, 2007). It is shown that the spike trains of a cold receptor neuron are more strongly affected by noise for low temperatures than for high temperatures. This trend is also observed in both regimes of cold receptors: tonic firing(which occurs for low and high temperatures) and bursting (which occurs for intermediate temperatures).     

Improving noise resistance of intrinsic rhythms in a square-wave burster model

The square-wave burster (Wang and Rinzel, 2003) is a class of autonomous bursting cells that share a bifurcation structure. It is known that this class of cells is involved in the generation of various life-supporting rhythms. In our research to realize an electronic circuit that mimics the rhythm generating mechanism in the square-wave burster, our circuit experimentally exhibited severe fluctuations in its rhythmic activity. We have found a noise-sensitive region in the phase portrait of the ideal model and have proposed modifications of the model that can reduce this fluctuation. A possible modification to ionic-conductance neuron models (Kohno and Aihara, 2011) was inspired by them. This modification, however, cannot be applied to a group of square-wave bursters, including the Butera–Rinzel–Smith model (0010 and 0050), which is a model of the pre-Bötzinger complex bursting neuron that plays a crucial role in the generation of respiration rhythms, because this modification premises that the slow dynamics originates from an activation gate variable of a hyperpolarizing ionic current. However, in some square-wave bursters, they are controlled by an inactivation gate variable of a depolarizing ionic current. In this study, we proposed a similar modification with a completely different mechanism that can be applied to this group of square-wave bursters. In the presence of noises, the modified Butera–Rinzel–Smith model can generate rhythmic activity that is more stable and similar to biological observations than the original model. The mechanisms underlying this modification are explained with noisy bifurcation diagrams.     

磷脂酶D1信号对神经干细胞向神经分化的重要影响

磷脂酶D1(PLD1)在细胞生长、存活、分化、膜转运和细胞骨架组织等多种功能的调控中发挥重要作用。近年来研究发现,PLD1在神经干细胞（NSCs）向神经元的分化中也起关键作用。PLD1参与多种信号通路如Rho家族GTP酶和Ca²⁺信号通路的调节,影响轴突生长、突触发育及其可塑性。因此,PLD1作为神经系统中一种重要的信号分子引起了广泛的关注。本文综述了PLD1的结构、功能、作用机制及其在NSCs向神经分化中的调控作用,对深入研究NSCs的分化和神经元的再生有重要的指导意义。     

Signaling pathways of the early differentiation of neural stem cells by neurotrophin-3

Neurotrophin-3 (NT-3) is well known to play an important role in facilitating neuronal survival and differentiation during development. However, the mechanisms by which neurotrophin-3 promotes prolonged Akt/MAPK signaling at an early stage are not well understood. Here, we report that NT-3 works at an early stage of neuronal differentiation in mouse neural stem cells (NSCs). After treatment with NT-3 for 12h, more NSCs differentiated into neurons than did untreated cells. These findings demonstrated that stimulation with NT-3 causes NSCs to differentiate into neurons through a phosphatidylinositol 3-kinase (PI3K)/Akt signaling pathway and the phosphorylated extracellular signal-regulated kinase (ERK) pathway. In addition, treatment with NT-3 induced neurite outgrowth by specific phosphorylation of p38 MAPK, which was accompanied by neuronal differentiation. Taken together, these results suggest that NT-3, along with the Trk C receptors in NSCs, might lead to the survival and neuronal differentiation of NSCs via two distinct downstream signaling pathways at an early stage of neuronal differentiation.     

Multiple pulse interactions and averaging in systems of coupled neural oscillators

Oscillators coupled strongly are capable of complicated behavior which may be pathological for biological control systems. Nevertheless, strong coupling may be needed to prevent asynchrony. We discuss how some neural networks may be designed to achieve only simple locking behavior when the coupling is strong. The design is based on the fact that the method of averaging produces equations that are capable only of locking or drift, not pathological complexity. Furthermore, it is shown that oscillators that interact by means of multiple pulses per cycle, dispersed around the cycle, behave like averaged equations, even if the number of pulses is small. We discuss the biological intuition behind this scheme, and show numerically that it works when the oscillators are taken to be composites, each unit of which is governed by a well-known model of a neural oscillator. Finally, we describe numerical methods for computing from equations for coupled limit cycle oscillators the averaged coupling functions of our theory.Research partially supported by the National Science Foundation under grants DMS 8796235 and DMS 8701405 and the Air Force Office of Scientific Research under University Research Contract F 49620-C-0131 to Northeastern University     

Role of phospholipase D1 in neurite outgrowth of neural stem cells

Employing neural stem cells from the brain cortex of E12 rat embryos, we investigated the possible role of phospholipase D (PLD) in the synaptogenesis and neurite formation of neural cells during differentiation. Expression level of PLD1 increased during neuronal differentiation of the neural stem cells, resulting in increased PLD activity. Expression level of synapsin I, a marker of synaptogenesis, also increased as the differentiation of neural stem cells progressed. To figure out the effect of PLD on synapsin I expression, we treated the neural stem cells with phorbol myristate acetate (PMA) to stimulate PLD activity. Increased PLD activity induced by PMA treatment resulted in elevated synapsin I expression and neurite outgrowth during neuronal differentiation. To further confirm the role of PLD in neurite outgrowth, we transfected the dominant-negative form of rat PLD1 cDNA (DN-rPLD1) into neural stem cells to downregulate PLD activity. Overexpression of DN-rPLD1 showed the complete inhibition of neurite outgrowth of neural stem cells under differentiation condition. While transfection of DN-rPLD1 did not affect the synapsin I expression, overexpression of rPLD1 resulted in increased synapsin I expression of the neural cells. These results suggest that PLD1 plays a critical role in neurite outgrowth during differentiation of the neural stem cells. In conclusion, this is the first evidence to show that PLD1 acts as an important regulator of neurite outgrowth in neural stem cell by promoting neuronal differentiation via increase of synapsin I expression.     

Control of continuous fed-batch fermentation process using neural network based model predictive controller

Cell growth and metabolite production greatly depend on the feeding of the nutrients in fed-batch fermentations. A strategy for controlling the glucose feed rate in fed-batch baker’s yeast fermentation and a novel controller was studied. The difference between the specific carbon dioxide evolution rate and oxygen uptake rate (Q _c − Q _o) was used as controller variable. The controller evaluated was neural network based model predictive controller and optimizer. The performance of the controller was evaluated by the set point tracking. Results showed good performance of the controller.     

微粒群优化神经网络及其在环境评价中的运用

农业项目环境影响综合评价是目前新的研究领域,随着农业项目的增加,其环境影响的研究愈来愈重要.以某农业项目为例,运用PSO-BP进行农业项目环境评价;仿真和实验表明:微粒群优化神经网络,能够克服神经网络收敛速度慢,陷入局部最小的缺点;微粒群优化算法涉及的参数不多,但是微粒群优化结果是比较理想的.     

Reformation of the severed septohippocampal cholinergic pathway in the adult rat by transplanted septal neurons

Summary Transplants containing developing cholinergic neurons were obtained from the septum-diagonal band area of rat fetuses and were implanted into a lesion of the septohippocampal cholinergic pathway or into a cavity of the occipital cortex in adult recipient rats. The growth of new cholinergic fibres from the implant into the hippocampal formation was followed with choline acetyltransferase (ChAT) determinations and acetylcholine esterase (AChE) histochemistry. A fimbrial lesion alone, transecting the septohippocampal pathway, caused an almost complete cholinergic denervation of the hippocampal formation that persisted throughout the five month experimental period. A septal transplant implanted into the cavity of the fimbrial lesion restored a new AChE-positive innervation pattern in the hippocampus and the dentate gyrus that closely mimicked the original innervation removed by the lesion. In parallel, there was a progressive recovery in the ChAT levels, starting in the septal end, and progressing in a temporal direction. A new cholinergic fibre supply could be established in the hippocampal formation also along an abnormal route, i.e. from the transplants implanted into a cavity in the occipital cortex (involving also the dorsal part of the entorhinal cortex). Provided the hippocampus previously had been denervated of its normal cholinergic innervation, a partly normal AChE-positive terminal pattern was thus re-established also from this abnormal position. If, on the other hand, the cholinergic afferents were left intact, the ingrowing fibres were restricted mainly to the outer portion of the dentate molecular layer, i.e. the terminal zone of the lesioned entorhinal perforant path fibres. This suggests that the growth of the sprouting AChE-positive fibres into the normal cholinergic terminal fields was blocked by the presence of an intact cholinergic innervation. It is concluded that regrowing cholinergic axons can be guided over large distances within the hippocampal formation, and that their patterning within the terminal fields is very precisely regulated by mechanisms released by deafferentation.     

Fasciculation and elongation protein zeta-1 (FEZ1) participates in the polarization of hippocampal neuron by controlling the mitochondrial motility

The fasciculation and elongation protein zeta-1 (FEZ1), a mammalian orthologue of Caenorhabditis elegans UNC-76 protein, is a 45-kDa protein with four coiled-coiled domains and efficiently promotes the neurite elongation in the rat phaeochromocytoma PC12 cells. UNC-76 proteins of C. elegans and Drosophila have been genetically demonstrated to be involved in the axonal guidance. We here show that FEZ1 RNA interference (RNAi) represses the formation of axon in rat embryo hippocampal neurons. An anterograde mitochondrial movement is also retarded in neurites of the RNAi-treated hippocampal neurons. Moreover, the size of mitochondria is considerably elongated by the RNAi treatment. The transport of mitochondria from soma to axon or dendrites is essential for the neuronal differentiation. Therefore, our results strongly suggest that FEZ1 participates in the establishment of neuronal polarity by controlling the mitochondrial motility along axon.     

A neural network model of attention-modulated neurodynamics

Visual attention appears to modulate cortical neurodynamics and synchronization through various cholinergic mechanisms. In order to study these mechanisms, we have developed a neural network model of visual cortex area V4, based on psychophysical, anatomical and physiological data. With this model, we want to link selective visual information processing to neural circuits within V4, bottom-up sensory input pathways, top-down attention input pathways, and to cholinergic modulation from the prefrontal lobe. We investigate cellular and network mechanisms underlying some recent analytical results from visual attention experimental data. Our model can reproduce the experimental findings that attention to a stimulus causes increased gamma-frequency synchronization in the superficial layers. Computer simulations and STA power analysis also demonstrate different effects of the different cholinergic attention modulation action mechanisms.