Comparison and Regulation of Neuronal Synchronization for Various STDP Rules

We discuss effects of various experimentally supported STDP learning rules on frequency synchronization of two unidirectional coupled neurons systematically. First, we show that synchronization windows for all STDP rules cannot be enhanced compared to constant connection under the same model. Then, we explore the influence of learning parameters on synchronization window and find optimal parameters that lead to the widest window. Our findings indicate that synchronization strongly depends on the specific shape and the parameters of the STDP update rules. Thus, we give some explanations by analyzing the synchronization mechanisms for various STDP rules finally

Direct Signal Detection Without Data‐Aided: A MIMO Functional Network Approach

Functional network (FN) has been successfully applied in many fields, but so far no methods of direct signal detection (DSD) using FN have been published. In this chapter, a novel DSD approach using FN, which can be applied to cases with a plural source signal sequence, with short sequence, and even with the absence of a training sequence, is presented. Firstly, a multiple‐input multiple‐output FN (MIMOFN), in which the initial input vector is devised via QR decomposition of the receiving signal matrix, is constructed to solve the special issues of DSD. In the meantime, the design method for the neural function of this special MIMOFN is proposed. Then the learning rule for the parameters of neural functions is trained and updated by back‐propagation (BP) algorithm. The correctness and effectiveness of the new approach are verified by simulation results, together with some special simulation phenomena of the algorithm. The proposed method can detect the source sequence directly from the observed output data by utilizing MIMOFN without a training sequence and estimating the channel impulse response

Some statistical models and approaches to target tracking and data association

Target tracking involves estimating the state of a moving object from noisy observations of uncertain origin and is a problem of significant importance to surveillance applications. In a tracking scenario the thorniest problem is of data-association; that is, how to determine which measurements come from which targets. This topic has been studied extensively and a number of solutions have been proposed. Among them, the Probabilistic Multi-Hypothesis Tracker (PMHT) developed by Luginbuhl & Streit is a relatively new one. By making a modification on the measurement model, specifically, positing the measurement/target association process as independent across measurements, the PMHT is able to render a fully-optimal (under the modified assumption) tracker. The PMHT exhibits an elegant structure of easy extensibility and flexibility; and, at the same time, it suffers from some intrinsic problems. ^ The first topic of this dissertation is to explore the PMHT and seek its improvement in practical applications: we analyze its underlying principles, study its problems and suggest some solutions; we exploit its structural flexibility and extend it to various forms to pursue the best performance, and to function as a natural overlay to a hidden Markov “maneuver” process; we compare it to some popular tracking algorithms such as the Probabilistic Data Association Filter (PDAF), Multi-Hypothesis Tracker (MHT) and S-D assignment; we investigate its consistence, scrutinize; its model and derive the performance bound. ^ Fusion is another important topic in tracking, particularly multiple sensor tracking. To date, however, relatively little literature addresses the issue of communication, which appears to be a limited or expensive resource in many systems. The main challenge, also a second topic of this dissertation, is how to reduce the required bandwidth without, or with little, degradation of tracking accuracy. We introduce intelligent quantization schemes in measurement fusion and discuss some practical issues in target tracking and suggest solutions by marrying particle filtering with techniques to work with out-of-sequence-measurements (OOSMs) and quantizers. Simulation results show that via intelligent quantization, 3 to 4 bits per dimension per measurement per transmission is enough for fairly accurate tracking.

A Novel Hybrid Approach to Selecting Marker Genes for Cancer Classification Using Gene Expression Data

Abstract—Selecting a subset of marker genes from thousands of genes is an important topic in microarray experiments for diseases classification and prediction. In this paper, we proposed a novel hybrid approach that combines gene ranking, heuristic clustering analysis and wrapper method to select marker genes for tumor classification. In our method, we firstly employed gene filtering to select the informative genes; secondly, we extracted a set of prototype genes as the representative of the informative genes by heuristic K-means clustering; finally, employed SVM-RFE to find marker genes from the representative genes based on recursive feature elimination. The performance of our method was evaluated by AML/ALL microarray dataset. The experimental results revealed that our method could find very small subset of marker genes with minimum redundancy but got better classification accuracy. Keywords-gene expression profiles, feature selection, heuristic clustering, prototype gene, SVM-RFE, cancer classification. I

Polymorphisms of chicken TLR3 and 7 in different breeds.

Toll-like receptors (TLRs) mediate immune responses via the recognition of pathogen-associated molecular patterns (PAMPs), thus playing important roles in host defense. Among the chicken (Ch) TLR family, ChTLR3 and 7 have been shown to recognize viral RNA. In our earlier studies, we have reported polymorphisms of TLR1, 2, 4, 5, 15 and 21. In the present study, we amplified TLR3 and 7 genes from different chicken breeds and analyzed their sequences. We identified 7 amino acid polymorphism sites in ChTLR3 with 6 outer part sites and 1 inner part site, and 4 amino acid polymorphism sites in ChTLR7 with 3 outer part sites and 1 inner part site. These results demonstrate that ChTLR genes are polymorphic among different chicken breeds, suggesting a varied resistance across numerous chicken breeds. This information might help improve chicken health by breeding and vaccination

A network approach to migrants’ transnational biographies

This paper reviews social network analysis (SNA) as a method to be utilized in biographical research which is a novel contribution. We argue that applying SNA in the context of biography research through standardized data collection as well as visualization of networks can open up participants’ interpretations of relations throughout their lives, and allow a creative and innovative way of data collection that is responsive to participants’ own meanings and associations while allowing the researchers to conduct systematical data analysis. The paper discusses the analytical potential of SNA in biographical research, where the efficacy of this method is critically discussed, together with its limitations, and its potential within the context of biographical research

Chicken TLR7 polymorphic sites in different breeds.

<p>¹Aa = Amino acid</p><p>² BF = Beijing Fatty chicken, BW = Beijing White 939 chicken, HL = Hy-Linevarietybrown chicken, LB = Laiwu Black chicken, LH = Luhua chicken, NN3 = Nongda No.3 chicken, RY = Royal chicken, WL = White Leghorn chicken, WS = White-Feather Silky chicken, referred sequence = Gallus gallus TLR7 NM 001011688.</p><p>³ Synonymous (n = 1) and nonsynonymous (n = 4) substitution.</p><p>Chicken TLR7 polymorphic sites in different breeds.</p

Chicken TLR3 polymorphic sites in different breeds.

<p>¹Aa = Amino acid</p><p>² BF = Beijing Fatty chicken, BW = Beijing White 939 chicken, HL = Hy-Linevarietybrown chicken, LB = Laiwu Black chicken, LH = Luhua chicken, NN3 = Nongda No.3 chicken, RY = Royal chicken, WL = White Leghorn chicken, WS = White-Feather Silky chicken, referred sequence = Gallus gallus TLR3 NM_001011691.</p><p>³ Synonymous (n = 6) and nonsynonymous (n = 7) substitution.</p><p>Chicken TLR3 polymorphic sites in different breeds.</p