Computation and Neural Systems by Anthony J. Bell (auth.), Frank H. Eeckman, James M. Bower

By Anthony J. Bell (auth.), Frank H. Eeckman, James M. Bower (eds.)

Computational neuroscience is healthier outlined through its specialize in realizing the frightened structures as a computational gadget instead of via a selected experimental strategy. Accordinlgy, whereas nearly all of the papers during this booklet describe research and modeling efforts, different papers describe the result of new organic experiments explicitly put within the context of computational matters. The distribution of topics in Computation and Neural Systems displays the present nation of the sphere. as well as the clinical effects offered right here, a number of papers additionally describe the continued technical advancements which are serious for the continuing development of computational neuroscience.
Computation and Neural Systems comprises papers offered on the First Annual Computation and Neural structures assembly held in San Francisco, CA, July 26--29, 1992.

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The major advantage of this n-tuples measure lies in its ability to collapse all n3 combinations into five vector sets quantifying variations of firing intervals between spikes. A Multi-Neuronal Vectorial Phase-space Analysis 53 Figure 1 shows the phase-space plot of the trajectories of the cross-interval vectors for three reference neurons using simulated spike trains recorded simultaneously. The cluster plots are used to display the "tips" of the cross-interval vector as points on the x-y plot.

Kenyon GT, PuffRD, Fetz EE,Biol. , 67 (1992) 133. van der Ma1sburg, in Brain Theory, Palm G, Aertsen (eds), (Spinger-Verlag, Berlin, 1986) 161. Kenyon GT, Tam DC, to appear in Proceedings of the Second Annual Conference on EEG and Nonlinear Dynamics, Jansen B ( ed) (World Sci. Pub. ) Kolmogorov AN, JDokl. Akad. , 124 (1959) 754. , 63 (1964) 23. ) (Springer, New York, 1981). 8 A MULTI-NEURONAL VECTORIAL PHASE-SPACE ANALYSIS FOR DETECTING DYNAMICAL INTERACTIONS IN FIRING PATTERNS OF BIOLOGICAL NEURAL NETWORKS David C.

And Sompolinsky H, "Statistical Mechanics of Neural Networks near Saturation," Ann. Phys, 173:30-67, 1987. , "The space of interactions in neural network models," J. Pys. A, 21:257-270, 1988. [7] Prugel-Bennett A. and Shapiro, J. , "Statistical mechanics of unsupervised hebbian learning," Submitted to J. Phys. , 1992. 5 A METHOD FOR ESTIMATING THE NEURAL INPUT TO A NEURON USING THE IONIC CURRENT MODEL Yoshimi Kamiyamat, Hiroyuki Ishii+ and Shiro Usuit t Department of Knowledge-based Information Engineering t Department of Information and Computer Sciences 1 INTRODUCTION Toyohashi University of Technology Toyohashi 441, Japan The ionic current properties of a neuron play a fundamental role for generating the voltage responses such as post synaptic potential and action potential.

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