Spike-timing-dependent plasticity in balanced random networks

The balanced random network model attracts considerable interest because it explains the irregular spiking activity at low rates and large membrane potential fluctuations exhibited by cortical neurons in vivo. In this article, we investigate to what extent this model is also compatible with the experimentally observed phenomenon of spike-timing-dependent plasticity (STDP). Confronted with the plethora of theoretical models for STDP available, we reexamine the experimental data. On this basis, we propose a novel STDP update rule, with a multiplicative dependence on the synaptic weight for depression, and a power law dependence for potentiation. We show that this rule, when implemented in large, balanced networks of realistic connectivity and sparseness, is compatible with the asynchronous irregular activity regime. The resultant equilibrium weight distribution is unimodal with fluctuating individual weight trajectories and does not exhibit development of structure. We investigate the robustness of our results with respect to the relative strength of depression. We introduce synchronous stimulation to a group of neurons and demonstrate that the decoupling of this group from the rest of the network is so severe that it cannot effectively control the spiking of other neurons, even those with the highest convergence from this group.     

Response time variability

Response time variability is a new optimization problem with a broad range of applications and a distinctive number of theoretic flavour. The problem occurs whenever events, jobs, clients or products need to be sequenced so as to minimize the variability of time for which they wait for the next turn in obtaining the resources necessary for their advance. The problem has numerous real-life applications. We study its computational complexity, present efficiency, polynomial time algorithms for some cases, and the NP-hardness proof for a general problem. We propose a position exchange heuristic and apply it to improve the total response time variability of an initial sequence. The latter is the optimum bottleneck sequence, Webster or Jefferson sequence of the apportionment, or a random sequence. We report on computational experiments with the heuristic.     

能量均衡的无线传感器网络分簇方法

在对节点通信模式和簇群划分过程分析的基础上,提出一种在节点分布不均匀的条件下,构建能量均衡簇群的方法.该算法兼顾了簇群成员节点与簇头通信的能量消耗和簇群能耗负载,实现各簇群间能耗的平衡.仿真表明,该方法在网络生命期、节点平均生命期和网络扩展性方面比基于最短距离的分簇算法具有更好的性能.     

Computing and stability in cortical networks

Cortical neurons are predominantly excitatory and highly interconnected. In spite of this, the cortex is remarkably stable: normal brains do not exhibit the kind of runaway excitation one might expect of such a system. How does the cortex maintain stability in the face of this massive excitatory feedback? More importantly, how does it do so during computations, which necessarily involve elevated firing rates? Here we address these questions in the context of attractor networks-networks that exhibit multiple stable states, or memories. We find that such networks can be stabilized at the relatively low firing rates observed in vivo if two conditions are met: (1) the background state, where all neurons are firing at low rates, is inhibition dominated, and (2) the fraction of neurons involved in a memory is above some threshold, so that there is sufficient coupling between the memory neurons and the background. This allows "dynamical stabilization" of the attractors, meaning feedback from the pool of background neurons stabilizes what would otherwise be an unstable state. We suggest that dynamical stabilization may be a strategy used for a broad range of computations, not just those involving attractors.     

电力通信网络中负载均衡的路由协议

在电力通信网络中,负载均衡能够减少瓶颈节点的过载情况,有助于提升电力通信系统的可靠性和网络资源利用率。针对电力通信网络独特的结构与流量特征,提出一种确定性路由与机会路由相结合的负载均衡的路由协议。每个节点从以自己为中心的区域中选出候选节点集合负责转发数据包,候选节点依据局部的准确代价与远处的估计代价划分优先级并决定转发概率。与负载均衡优先的开放最短路径优先（LBA-OSPF）协议相比,节点平均负载降低了32.3%,端到端时延减少了50.3%。     

Correlations and population dynamics in cortical networks

The function of cortical networks depends on the collective interplay between neurons and neuronal populations, which is reflected in the correlation of signals that can be recorded at different levels. To correctly interpret these observations it is important to understand the origin of neuronal correlations. Here we study how cells in large recurrent networks of excitatory and inhibitory neurons interact and how the associated correlations affect stationary states of idle network activity. We demonstrate that the structure of the connectivity matrix of such networks induces considerable correlations between synaptic currents as well as between subthreshold membrane potentials, provided Dale's principle is respected. If, in contrast, synaptic weights are randomly distributed, input correlations can vanish, even for densely connected networks. Although correlations are strongly attenuated when proceeding from membrane potentials to action potentials (spikes), the resulting weak correlations in the spike output can cause substantial fluctuations in the population activity, even in highly diluted networks. We show that simple mean-field models that take the structure of the coupling matrix into account can adequately describe the power spectra of the population activity. The consequences of Dale's principle on correlations and rate fluctuations are discussed in the light of recent experimental findings.     

无线传感器网络中能耗平衡的传输策略

分簇无线传感器网络中存在着能量空洞的问题,而能量均匀消耗被认为是解决该问题的有效方法。将圆形无线传感器网络划分成不同宽度的同心圆,提出混合单跳传输和多跳传输的策略,以实现能量均匀消耗。与其他已经存在的研究不同,提出的策略针对的是传感器平均能量消耗的平衡,并能用线性方程组计算出多跳转发和单跳转发的概率。数值计算证实本方案能保证不同位置传感器平均能量消耗平衡,且能保持总体能量消耗在某个较低的水平。     

Exact and approximate balanced data gathering in energy-constrained sensor networks

We consider the problem of gathering data from a wireless multi-hop network of energy-constrained sensor nodes to a common base station. Specifically, we aim to balance the total amount of data received from the sensor network during its lifetime against a requirement of sufficient coverage for all the sensor locations surveyed. Our main contribution lies in formulating this balanced data gathering task, studying the effects of balancing, and proposing an approximation algorithm for the problem. Based on an LP network flow formulation, we present experimental results on both optimal and approximate data routing designs, in open transmission ranges and with impenetrable obstacles between the nodes.     

Optimal topology control for balanced energy consumption in wireless networks

Hosts in wireless networks are usually powered by batteries, thus the lifetime of a network depends on the battery life of each individual host. One major solution to improve energy-efficiency is to minimize the total energy consumption. However, battery energy is a local resource, to save energy at each host and balance the energy consumption among all the hosts in the network appear to be more practical. In this paper, we prove that minimum weight incremental arborescence (MWIA) is the optimal solution for minimizing the maximum transmission power among a set of wireless nodes. We propose an algorithm that utilizes MWIA to construct a connected topology, called MWIA-based Topology Control(MWIA-TC) algorithm. We further apply MWIA-TC to the accumulated energy consumption. The theoretical analysis and experimental results show that MWIA-TC outperforms a well-known algorithm, Minimum Incremental Power (MIP), in both energy saving and network lifetime extension.     

Approximation schemes for load balanced clustering in wireless sensor networks

Clustering sensor nodes is an efficient technique to improve scalability and life time of a wireless sensor network (WSN). However, in a cluster based WSN, the leaders (cluster heads) consume more energy due to some extra load for various activities such as data collection, data aggregation, and communication of the aggregated data to the base station. Therefore, balancing the load of the cluster heads is a crucial issue for the long run operation of the WSNs. In this paper, we first present a load balanced clustering scheme for wireless sensor networks. We show that the algorithm runs in O(nlogn) time for n sensor nodes. We prove that the algorithm is optimal for the case in which the sensor nodes have equal load. We also show that it is a polynomial time 2-approximation algorithm for the general case, i.e., when the sensor nodes have variable load. We finally improve this algorithm and propose a 1.5-approximation algorithm for the general case. The experimental results show the efficiency of the proposed algorithm in terms of the load balancing of the cluster heads, execution time, and the network life.     

The high-conductance state of cortical networks

We studied the dynamics of large networks of spiking neurons with conductance-based (nonlinear) synapses and compared them to networks with current-based (linear) synapses. For systems with sparse and inhibition-dominated recurrent connectivity, weak external inputs induced asynchronous irregular firing at low rates. Membrane potentials fluctuated a few millivolts below threshold, and membrane conductances were increased by a factor 2 to 5 with respect to the resting state. This combination of parameters characterizes the ongoing spiking activity typically recorded in the cortex in vivo. Many aspects of the asynchronous irregular state in conductance-based networks could be sufficiently well characterized with a simple numerical mean field approach. In particular, it correctly predicted an intriguing property of conductance-based networks that does not appear to be shared by current-based models: they exhibit states of low-rate asynchronous irregular activity that persist for some period of time even in the absence of external inputs and without cortical pacemakers. Simulations of larger networks (up to 350,000 neurons) demonstrated that the survival time of self-sustained activity increases exponentially with network size.     

能耗均衡的自供能无线传感器网络分簇路由算法

当前的采能技术已经能够让传感器节点自动从环境中获得适量的能量补给,针对现有自供能无线传感器网络分簇路由算法中未考虑位于不同地理区域的节点所获补给能量大小的不同,而导致能量补给少区域的簇头数过少、簇规模过大、全网能耗不均衡等问题,本文提出了一种能耗均衡的自供能无线传感器网络分簇路由算法-EBCS（energy balanced clustering with self-energized）,该算法结合实际能量补给场景对簇头选举机制进行了改进,并采用了一种自适应式簇间通信机制,充分保存与利用补给能量。理论和仿真实验表明：EBCS算法能够较好维持预设的簇头比例,在网络平均剩余能量、当前可用节点数量等性能方面优于另外两种现有算法。     

高效节能的WSN非均匀分簇节点调度算法研究

针对目前无线传感器网络分簇算法中存在的节点能量消耗不均衡,大量节点工作导致信息冗余和能量浪费等问题,提出一种高效节能的WSN非均匀分簇节点调度算法EEBUC（Energy-Efficient and Balanced Unequal Clustering Nodes Scheduling）。该算法在簇的形成阶段,考虑候选簇首离汇聚点的距离、所在区域的节点密度和节点能量形成非均匀的竞争范围,构造大小不等的簇,平衡簇内和簇间的通信能耗;同时结合调度簇内冗余节点方法,减少网络中每轮工作节点数量,提高网络能量利用率。利用OMNET++仿真软件进行仿真,实验结果表明,EEBUC算法能有效节约网络能量,均衡节点能耗,比LEACH 协议和EEUC协议分别延长网络寿命203%和50%。     

Swarm optimisation algorithms applied to large balanced communication networks

In the last years, several combinatorial optimisation problems have arisen in the computer communications networking field. In many cases, for solving these problems it is necessary the use of meta-heuristics. An important problem in communication networks is the Terminal Assignment Problem (TAP). Our goal is to minimise the link cost of large balanced communication networks. TAP is a NP-Hard problem. The intractability of this problem is the motivation for the pursuits of Swarm Intelligence (SI) algorithms that produce approximate, rather than exact, solutions. This paper makes a comparison among the effectiveness of three SI algorithms: Ant Colony Optimisation, Discrete Particle Swarm Optimisation and Artificial Bee Colony. We also compare the SI algorithms with several algorithms from literature. Simulation results verify the effectiveness of the proposed algorithms. The results show that SI algorithms provide good solutions in a better running time.     

Simulating cortical networks on heterogeneous multi-GPU systems

Recent advances in neuroscientific understanding have highlighted the highly parallel computation power of the mammalian neocortex. In this paper we describe a GPGPU-accelerated implementation of an intelligent learning model inspired by the structural and functional properties of the neocortex. Furthermore, we consider two inefficiencies inherent to our initial implementation and propose software optimizations to mitigate such problems. Analysis of our application’s behavior and performance provides important insights into the GPGPU architecture, including the number of cores, the memory system, atomic operations, and the global thread scheduler. Additionally, we create a runtime profiling tool for the cortical network that proportionally distributes work across the host CPU as well as multiple GPGPUs available to the system. Using the profiling tool with these optimizations on Nvidia’s CUDA framework, we achieve up to 60× speedup over a single-threaded CPU implementation of the model.     

Rate models for conductance-based cortical neuronal networks

Population rate models provide powerful tools for investigating the principles that underlie the cooperative function of large neuronal systems. However, biophysical interpretations of these models have been ambiguous. Hence, their applicability to real neuronal systems and their experimental validation have been severely limited. In this work, we show that conductance-based models of large cortical neuronal networks can be described by simplified rate models, provided that the network state does not possess a high degree of synchrony. We first derive a precise mapping between the parameters of the rate equations and those of the conductance-based network models for time-independent inputs. This mapping is based on the assumption that the effect of increasing the cell's input conductance on its f-I curve is mainly subtractive. This assumption is confirmed by a single compartment Hodgkin-Huxley type model with a transient potassium A-current. This approach is applied to the study of a network model of a hypercolumn in primary visual cortex. We also explore extensions of the rate model to the dynamic domain by studying the firing-rate response of our conductance-based neuron to time-dependent noisy inputs. We show that the dynamics of this response can be approximated by a time-dependent second-order differential equation. This phenomenological single-cell rate model is used to calculate the response of a conductance-based network to time-dependent inputs.     

无线网络负载均衡的干扰感知路由度量

无线信道干扰和负载分布不均匀严重影响无线网络的网络吞吐量、端到端延时等。在已有的路由度量的基础上,充分继承其通过邻居节点负载描述干扰强度的优势,进一步分析节点负载的影响,提出负载均衡的干扰感知路由度量,将干扰邻居节点的数量、负载和距离综合作用结果作为流间干扰强度,使用节点处的平均队列长度捕捉节点负载,并改进期望传输时间消除链路的不对称性,实现负载均衡和干扰感知,避开热点区域。同时将LBIA合并入路由协议。仿真结果表明:该路由度量可以有效地实现网络负载均衡,提升网络整体性能。     

On the differential and linear efficiency of balanced Feistel networks

Balanced Feistel networks (BFN) have been widely used for constructing efficient block ciphers. They are known to provide high efficiency with respect to differential and linear cryptanalysis, when instantiated with SL-type round functions (BFN-SL). This work suggests that BFNs attain higher efficiency when the round function is defined as a composition of two substitution layers connected by a linear diffusion layer (SLS-type round function). The resulting structure is called BFN-SLS.Tight upper bounds on the differential and linear trail probabilities are proven for such constructions. When compared to BFN-SL with single-round diffusion, BFN-SLS exhibits an increase by almost 1/3 in the proportion of active S-boxes. When compared to BFN-SL with multiple-round diffusion, BFN-SLS provides the same proportion of active S-boxes, requiring, however, twice less linear operations and a single diffusion matrix for all rounds.It is argued that the cost of linear operations cannot be ignored when dealing with efficiency. Different BFNs are compared under consideration of the relative complexity of linear and nonlinear finite field operations. As a result, since BFN-SLS minimizes the number of necessary linear operations, its efficiency is higher than that of the known BFN-SL constructions.     

The complexity of latching transitions in large scale cortical networks

We study latching dynamics, i.e. the ability of a network to hop spontaneously from one discrete attractor state to another, which has been proposed as a model of an infinitely recursive process in large scale cortical networks, perhaps associated with higher cortical functions, such as language. We show that latching dynamics can span the range from deterministic to random under the control of a threshold parameter U. In particular, the interesting intermediate case is characterized by an asymmetric and complex set of transitions. We also indicate how finite latching sequences can become infinite, depending on the properties of the transition probability matrix and of its eigenvalues.