Sat, 26 Jul 2008 08:04:06 BST CiteULike: AbnerCYH neuro http://www.citeulike.org/user/AbnerCYH/tag/neuro CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/AbnerCYH/article/2622105 <i>(1981)</i> Pattern Recognition with Fuzzy Objective Function Algorithms James Bezdek (1981) 2008-04-02T05:09:59-00:00 1981 Kluwer Academic Publishers information kdd neuro http://www.citeulike.org/user/AbnerCYH/article/1967617 <i>Brief Bioinform, Vol. 8, No. 6. (1 November 2007), pp. 446-456.</i><br /><br />The application of informatics to neuroscience goes far beyond traditional' bioinformatics modalities such as DNA sequences. In this review, we describe how informatics is being used to study the nervous system at multiple levels, spanning scales from molecules to behavior. The continuing development of standards for data exchange and interoperability, together with increasing awareness and acceptance of the importance of data sharing, are among the key efforts required to advance the field. 10.1093/bib/bbm047 Informatics in neuroscience Leon French Paul Pavlidis doi:10.1093/bib/bbm047 Brief Bioinform, Vol. 8, No. 6. (1 November 2007), pp. 446-456. 2007-11-23T17:24:07-00:00 2007 Brief Bioinform 8 6 446 456 biology complex graph neuro http://www.citeulike.org/user/AbnerCYH/article/158613 <i>Neuroinformatics, Vol. 2, No. 2. (2004), pp. 145-162.</i><br /><br />While much information is available on the structural connectivity of the cerebral cortex, especially in the primate, the main organizational principles of the connection patterns linking brain areas, columns and individual cells have remained elusive. We attempt to characterize a wide variety of cortical connectivity data sets using a specific set of graph theory methods. We measure global aspects of cortical graphs including the abundance of small structural motifs such as cycles, the degree of local clustering of connections and the average path length. We examine large-scale cortical connection matrices obtained from neuroanatomical data bases, as well as probabilistic connection matrices at the level of small cortical neuronal populations linked by intra-areal and inter-areal connections. All cortical connection matrices examined in this study exhibit "small-world" attributes, characterized by the presence of abundant clustering of connections combined with short average distances between neuronal elements. We discuss the significance of these universal organizational features of cortex in light of functional brain anatomy. Supplementary materials are at www.indiana.edu/~cortex/lab.htm. The small world of the cerebral cortex. O Sporns JD Zwi doi:10.1385/NI:2:2:145 Neuroinformatics, Vol. 2, No. 2. (2004), pp. 145-162. 2005-04-11T22:33:44-00:00 2004 Neuroinformatics 1539-2791 2 2 145 162 biology graph neuro http://www.citeulike.org/user/AbnerCYH/article/1824271 <i>Nonlinear Biomedical Physics, Vol. 1 (5 July 2007)</i> Graph theoretical analysis of complex networks in the brain doi:10.1186/1753-4631-1-3 Nonlinear Biomedical Physics, Vol. 1 (5 July 2007) 2007-10-26T08:46:15-00:00 2007 Nonlinear Biomedical Physics 1 biology complex dynamics graph neuro http://www.citeulike.org/user/AbnerCYH/article/1704979 <i>Clinical Neurophysiology, Vol. In Press, Corrected Proof</i><br /><br />Considering the brain as a complex network of interacting dynamical systems offers new insights into higher level brain processes such as memory, planning, and abstract reasoning as well as various types of brain pathophysiology. This viewpoint provides the opportunity to apply new insights in network sciences, such as the discovery of small world and scale free networks, to data on anatomical and functional connectivity in the brain. In this review we start with some background knowledge on the history and recent advances in network theories in general. We emphasize the correlation between the structural properties of networks and the dynamics of these networks. We subsequently demonstrate through evidence from computational studies, in vivo experiments, and functional MRI, EEG and MEG studies in humans, that both the functional and anatomical connectivity of the healthy brain have many features of a small world network, but only to a limited extent of a scale free network. The small world structure of neural networks is hypothesized to reflect an optimal configuration associated with rapid synchronization and information transfer, minimal wiring costs, resilience to certain types of damage, as well as a balance between local processing and global integration. Eventually, we review the current knowledge on the effects of focal and diffuse brain disease on neural network characteristics, and demonstrate increasing evidence that both cognitive and psychiatric disturbances, as well as risk of epileptic seizures, are correlated with (changes in) functional network architectural features. The application of graph theoretical analysis to complex networks in the brain Jaap Reijneveld Sophie Ponten Henk Berendse Cornelis Stam doi:10.1016/j.clinph.2007.08.010 Clinical Neurophysiology, Vol. In Press, Corrected Proof 2007-09-28T12:57:21-00:00 Clinical Neurophysiology In Press, Corrected Proof biology complex graph neuro http://www.citeulike.org/user/AbnerCYH/article/293287 <i>Proc Natl Acad Sci U S A, Vol. 102, No. 17. (26 April 2005), pp. 6125-6130.</i><br /><br />To examine the network-level organizing principles by which the brain achieves its real-time encoding of episodic information, we have developed a 96-channel array to simultaneously record the activity patterns of as many as 260 individual neurons in the mouse hippocampus during various startling episodes. We find that the mnemonic startling episodes triggered firing changes in a set of CA1 neurons in both startle-type and environment-dependent manners. Pattern classification methods reveal that these firing changes form distinct ensemble representations in a low-dimensional encoding subspace. Application of a sliding window technique further enabled us to reliably capture not only the temporal dynamics of real-time network encoding but also postevent processing of newly formed ensemble traces. Our analyses revealed that the network-encoding power is derived from a set of functional coding units, termed neural cliques, in the CA1 network. The individual neurons within neural cliques exhibit "collective cospiking" dynamics that allow the neural clique to overcome the response variability of its members and to achieve real-time encoding robustness. Conversion of activation patterns of these coding unit assemblies into a set of real-time digital codes permits concise and universal representation and categorization of discrete behavioral episodes across different individual brains. Identification of network-level coding units for real-time representation of episodic experiences in the hippocampus. L Lin R Osan S Shoham W Jin W Zuo JZ Tsien doi:10.1073/pnas.0408233102 Proc Natl Acad Sci U S A, Vol. 102, No. 17. (26 April 2005), pp. 6125-6130. 2005-08-16T15:04:54-00:00 2005 Proc Natl Acad Sci U S A 0027-8424 102 17 6125 6130 biology complex neuro