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).
1857380
Mining RNA Tertiary Motifs with Structure Graphs
We present a novel application of graph database mining to identify tertiary motifs in RNA structures. In our method, we abstract an RNA molecule as a labeled graph and use a frequent subgraph mining technique to derive tertiary motifs. By applying our technique to ribosome RNA and transfer RNA, we have identified known RNA tertiary motifs such as the ribose zipper and U-turn, plus candidates for novel tertiary motifs. Finally, we suggest an iterative multiple structure alignment algorithm to classify tertiary motifs and generate consensus motifs.
8744802
On static and dynamic partitioning behavior of large-scale networks
In this paper, we analyze the problem of network disconnection in the context of large-scale P2P networks and understand how both static and dynamic patterns of node failure affect the resilience of such graphs. We start by applying classical results from random graph theory to show that a large variety of deterministic and random P2P graphs almost surely (i.e., with probability 1-o(1)) remain connected under random failure if and only if they have no isolated nodes. This simple, yet powerful, result subsequently allows us to derive in closed-form the probability that a P2P network develops isolated nodes, and therefore partitions, under both types of node failure. We finish the paper by demonstrating that our models match simulations very well and that dynamic P2P systems are extremely resilient under node churn as long as the neighbor replacement delay is much smaller than the average user lifetime
149826
Generalized component analysis for text with heterogeneous attributes
We present a class of richly structured, undirected hidden variable models suitable for simultaneously modeling text along with other attributes encoded in different modalities. Our model generalizes techniques such as principal component analysis to heterogeneous data types. In contrast to other approaches, this framework allows modalities such as words, authors and timestamps to be captured in their natural, probabilistic encodings. A latent space representation for a previously unseen document can be obtained through a fast matrix multiplication using our method. We demonstrate the effectiveness of our framework on the task of author prediction from 13 years of the NIPS conference proceedings and for a recipient prediction task using a 10-month academic email archive of a researcher. Our approach should be more broadly applicable to many real-world applications where one wishes to efficiently make predictions for a large number of potential outputs using dimensionality reduction in a well defined probabilistic framework.
18230248
Impact of Sparse Pixel Vectorization Algorithm Parameters on Line Segmentation Performance
We present the impact of parameters in the Sparse Pixel Vectorization algorithm on the performance of the algorithm, which is evaluated using a pixel level performance evaluation protocol. Both theoretical and experimental analyses are presented, which show that the experimental results are well in accordance with the theoretical analyses. Both prove the robustness of the algorithm. Meanwhile the parameter values for the best performance of the algorithm are also found.
3413304
On Node Isolation Under Churn in Unstructured P2P Networks with Heavy-Tailed Lifetimes
Previous analytical studies [12], [18] of unstructured P2P resilience have assumed exponential user lifetimes and only considered age-independent neighbor replacement. In this paper, we overcome these limitations by introducing a general node-isolation model for heavy-tailed user lifetimes and arbitrary neighbor-selection algorithms. Using this model, we analyze two age-biased neighbor-selection strategies and show that they significantly improve the residual lifetimes of chosen users, which dramatically reduces the probability of user isolation and graph partitioning compared to uniform selection of neighbors. In fact, the second strategy based on random walks on age-weighted graphs demonstrates that for lifetimes with infinite variance, the system monotonically increases its resilience as its age and size grow. Specifically, we show that the probability of isolation converges to zero as these two metrics tend to infinity. We finish the paper with simulations in finite-size graphs that demonstrate the effect of this result in practice.