17850389
Mean-Driven and Fluctuation-Driven Persistent Activity in Recurrent Networks
Spike trains from cortical neurons show a high degree of irregularity, with coefficients of variation (CV) of their interspike interval (ISI) distribution close to or higher than one. It has been suggested that this irregularity might be a reflection of a particular dynamical state of the local cortical circuit in which excitation and inhibition balance each other. In this balanced state, the mean current to the neurons is below threshold, and firing is driven by current fluctuations, resulting in irregular Poisson-like spike trains. Recent data show that the degree of irregularity in neuronal spike trains recorded during the delay period of working memory experiments is the same for both low-activity states of a few Hz and for elevated, persistent activity states of a few tens of Hz. Since the difference between these persistent activity states cannot be due to external factors coming from sensory inputs, this suggests that the underlying network dynamics might support coexisting balanced states at different firing rates. We use mean field techniques to study the possible existence of multiple balanced steady states in recurrent networks of current-based leaky integrate-and-fire (LIF) neurons. To assess the degree of balance of a steady state, we extend existing mean-field theories so that not only the firing rate, but also the coefficient of variation of the interspike interval distribution of the neurons, are determined self-consistently. Depending on the connectivity parameters of the network, we find bistable solutions of different types. If the local recurrent connectivity is mainly excitatory, the two stable steady states differ mainly in the mean current to the neurons. In this case, the mean drive in the elevated persistent activity state is suprathreshold and typically characterized by low spiking irregularity. If the local recurrent excitatory and inhibitory drives are both large and nearly balanced, or even dominated by inhibition, two stable states coexist, both with subthreshold current drive. In this case, the spiking variability in both the resting state and the mnemonic persistent state is large, but the balance condition implies parameter fine-tuning. Since the degree of required fine-tuning increases with network size and, on the other hand, the size of the fluctuations in the afferent current to the cells increases for small networks, overall we find that fluctuation-driven persistent activity in the very simplified type of models we analyze is not a robust phenomenon. Possible implications of considering more realistic models are discussed.
5101914
Wave Propagation Mediated by GABAB Synapse and Rebound Excitation in an Inhibitory Network: A Reduced Model Approach
A reduction method is used to analyze a spatially structured network model of inhibitory neurons. This network model displays wave propagation of postinhibitory rebound activity, which depends on GABAB synaptic interactions among the neurons. The reduced model allows explicit solutions for the wavefronts and their velocity as a function of various parameters, such as the synaptic coupling strength. These predictions are shown to agree well with the numerical simulations of the conductance-based biophysical model.
15978047
The dynamical stability of reverberatory neural circuits
Abstract. The concept of reverberation proposed by Lorente de Nó and Hebb is key to understanding strongly recurrent cortical networks. In particular, synaptic reverberation is now viewed as a likely mechanism for the active maintenance of working memory in the prefrontal cortex. Theoretically, this has spurred a debate as to how such a potentially explosive mechanism can provide stable working-memory function given the synaptic and cellular mechanisms at play in the cerebral cortex. We present here new evidence for the participation of NMDA receptors in the stabilization of persistent delay activity in a biophysical network model of conductance-based neurons. We show that the stability of working-memory function, and the required NMDA/AMPA ratio at recurrent excitatory synapses, depend on physiological properties of neurons and synaptic interactions, such as the time constants of excitation and inhibition, mutual inhibition between interneurons, differential NMDA receptor participation at excitatory projections to pyramidal neurons and interneurons, or the presence of slow intrinsic ion currents in pyramidal neurons. We review other mechanisms proposed to enhance the dynamical stability of synaptically generated attractor states of a reverberatory circuit. This recent work represents a necessary and significant step towards testing attractor network models by cortical electrophysiology.
11647652
Analysis of complex bursting in cortical pyramidal neuron models
Abstract Burst firing is a prominent feature of cortical pyramidal cells and is thought to have significant functional roles in reliable signaling and synaptic plasticity. Modeling studies have successfully elucidated possible biophysical mechanisms underlying complex bursting in pyramidal cells. Based on these results (Pinsky, Rinzel, J. Comput. Neurosci. 1 (1994) 39–60), we have built a simplified two-compartment burst model. Using the fast- and slow-variable analysis method, we show that complex bursting is an instance of square-wave bursting, where the dendritic slow potassium conductance is the single slow variable. The coupling parameters between the two compartments change the topological class of bursting thereby altering the firing patterns of the neuron. These results explain the diverse set of firing patterns seen with different dendritic morphologies (Mainen, Sejnowski, Nature 382 (1996) 363–366).
9104202
Rate-Adaptive Framing for Interfered Wireless Networks
The majority of existing wireless rate controls are based on the implicit assumption that frames are corrupted due to the random, arbitrary environmental and thermal noises. They generally reduce the channel rate on frame losses, trading lower efficiency in frequency band utilization for more robust modulation so that the current noise level may be tolerable. In highly interfered wireless networks where frames are lost mainly due to interference from other wireless transceivers, simply reducing the channel rate prolongs the frame transmission time and therefore aggravates frame loss ratio. This positive feedback in the rate control loop quickly diverges the interfered transceivers into a suboptimal channel rate and drives the network into a state with high interference. In the worst case, interfered transceivers can be starved. In this paper we present RAF, the rate-adaptive framing that jointly controls the channel rate and frame size according to the observed interference patterns and noise level at the receiver. Based on the inputs from physical layer carrier sense, the receiver derives the optimal channel rate and frame size that maximize throughput, and informs the transmitter of such optimal configuration in a few bits in the per-frame acknowledgement. Through intensive simulations we show that RAF consistently outperforms ARF, RBAR, and OAR in all simulated scenarios.
15536346
An Index Structure for Data Mining and Clustering
Abstract. In this paper we present an index structure, called MetricMap, that takes a set of objects and a distance metric and then maps those objects to a k-dimensional space in such a way that the distances among objects are approximately preserved. The index structure is a useful tool for clustering and visualization in data-intensive applications, because it replaces expensive distance calculations by sum-of-square calculations. This can make clustering in large databases with expensive distance metrics practical. We compare the index structure with another data mining index structure, FastMap, recently proposed by Faloutsos and Lin, according to two criteria: relative error and clustering accuracy. For relative error, we show that (i) FastMap gives a lower relative error than MetricMap for Euclidean distances, (ii) MetricMap gives a lower relative error than FastMap for non-Euclidean distances (i.e., general distance metrics), and (iii) combining the two reduces the error yet further. A similar result is obtained when comparing the accuracy of clustering. These results hold for different data sizes. The main qualitative conclusion is that these two index structures capture complementary information about distance metrics and therefore can be used together to great benefit. The net effect is that multi-day computations can be done in minutes.
6284709
Satisfiability of Quantitative Temporal Constraints with Multiple Granularities
Most work on temporal constraints has ignored the subtleties involved in dealing with multiple time granularities. This paper considers a constraint satisfaction problem (CSP) where binary quantitative constraints in terms of different time granularities can be specified on a set of variables, and unary constraints are allowed to limit the domain of variables. Such a CSP cannot be trivially reduced to one of the known CSP problems. The main result of the paper is a complete algorithm for checking consistency and finding a solution. The complexity of the algorithm is studied in the paper under different assumptions about the granularities involved in the CSP, and a second algorithm is proposed to improve the efficiency of the backtracking process needed to obtain all the solutions of the CSP. 
 
The work of Wang and Jajodia was partially supported by the National Science Foundation under the grant IRI-9633541.
3433910
Residual-Based Measurement of Peer and Link Lifetimes in Gnutella Networks
Existing methods of measuring lifetimes in P2P systems usually rely on the so-called create-based method (CBM), which divides a given observation window into two halves and samples users "created" in the first half every Delta time units until they die or the observation period ends. Despite its frequent use, this approach has no rigorous accuracy or overhead analysis in the literature. To shed more light on its performance, we flrst derive a model for CBM and show that small window size or large Delta may lead to highly inaccurate lifetime distributions. We then show that create-based sampling exhibits an inherent tradeoff between overhead and accuracy, which does not allow any fundamental improvement to the method. Instead, we propose a completely different approach for sampling user dynamics that keeps track of only residual lifetimes of peers and uses a simple renewal-process model to recover the actual lifetimes from the observed residuals. Our analysis indicates that for reasonably large systems, the proposed method can reduce bandwidth consumption by several orders of magnitude compared to prior approaches while simultaneously achieving higher accuracy. We finish the paper by implementing a two-tier Gnutella network crawler equipped with the proposed sampling method and obtain the distribution of ultrapeer lifetimes in a network of 6.4 million users and 60 million links. Our experimental results show that ultrapeer lifetimes are Pareto with shape a alpha ap 1.1; however, link lifetimes exhibit much lighter tails with alpha ap 1.9.
269391
Integrated coverage and connectivity configuration in wireless sensor networks
An effective approach for energy conservation in wireless sensor networks is scheduling sleep intervals for extraneous nodes, while the remaining nodes stay active to provide continuous service. For the sensor network to operate successfully, the active nodes must maintain both sensing coverage and network connectivity. Furthermore, the network must be able to configure itself to any feasible degrees of coverage and connectivity in order to support different applications and environments with diverse requirements. This paper presents the design and analysis of novel protocols that can dynamically configure a network to achieve guaranteed degrees of coverage and connectivity. This work differs from existing connectivity or coverage maintenance protocols in several key ways: 1) We present a Coverage Configuration Protocol (CCP) that can provide different degrees of coverage requested by applications. This flexibility allows the network to self-configure for a wide range of applications and (possibly dynamic) environments. 2) We provide a geometric analysis of the relationship between coverage and connectivity. This analysis yields key insights for treating coverage and connectivity in a unified framework: this is in sharp contrast to several existing approaches that address the two problems in isolation. 3) Finally, we integrate CCP with SPAN to provide both coverage and connectivity guarantees. We demonstrate the capability of our protocols to provide guaranteed coverage and connectivity configurations, through both geometric analysis and extensive simulations.