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phosphorylation and dephosphorylation of CaMKII, as well as long-termpotentiation, and long-term depression.1. S-J. R. Lee et al., Activation of CaMKII in single dendritic spines duringlong-term potentiation. NATURE, 458(7236):299-U58, MAR 19 2009.2. G. C. Faas., Calmodulin as a direct detector of Ca2þ signals. NATURENeuroscience, 14(3):301-304, MAR 2011.

837-Pos Board B606High Spatial and Temporal Resolution Depolarization Spatial Distributionand Membrane Potential Measurement of Neural Soma Based on SodiumIon Channel Optical ModelZha Fusheng.Haerbin Institute of Technology, Haerbin, China.To modern neuroscience, how to measure and quantify membrane potentialdistribution with spatial and temporal is a major challenge. For this challenge,in this paper, we take advantage of the sodium ion channel optical model toinvestigate depolarization distribution of rat nodose ganglion neuronal somatic(about 80mm2) membrane with high temporal resolution (about 0.01ms) andspatial resolution (about 1.9nm2). We can observe the incremental changeprocess of membrane local area membrane electric potential under a hightemporal and spatial resolution. in subthreshold stimulation (�50mV) andsuperthreshold stimulation (�40mV), time and space dynamic process of gen-eration, development, diffusion of neuron cell body membrane potential depo-larization. According to the simulation results, we measure the membranepotential at different time on an arbitrary position in the membrane of thecell body and find that the incremental peak of membrane potential in thecell membrane is randomly distributed. Such random distribution is causedby a random distribution of the sodium channels in the cell membrane andthe randomly opening up of sodium ion channels. One advantage of the modelis the ability to measure from all locations of cell membrane simultaneously.This is especially important in the study of many parts of an individual cellare active at the same time.

838-Pos Board B6073D-Simulation of Electrical Activity at Cell Membranes, Interacting withSelf-Generated and External Electric FieldsAndreas Neef, Andres Y. Agudelo Toro.MPI DS, Goettingen, Germany.The electric activity of neurons creates extracellular potentials. These fields actback onto the neurons, contributing to synchronization of population activity.External fields are therapeutically used for neuro-stimulation. The mutual inter-action between fields and membrane-currents is not captured by today’s con-cepts of cellular electrophysiology, as those are based on isolated membranesin infinite, isopotential extracellular space. Even the direct influence of fieldsis not correctly represented by the commonly used activating function. Whilea reduced set of Maxwell’s equations can be used to couple membrane currentsand electric fields, this approach is rarely taken because adequate computa-tional tools are missing. We present a computational method that implementsthis set of equations. It allows simulation under realistic conditions: sub-micron cell morphology, various ion channel properties and distributions anda conductive, non-homogeneous space. An implicit solver preserves numerical

stability even for large time-steps, limitedonly by the development of membrane po-tentials. This allows simulation times ofminutes instead of weeks, even for com-plex problems. The extracellular fields areaccurately represented, including second-ary fields, which originate at inhomogenei-ties of the extracellular space. We present a set of instructive examples.

839-Pos Board B608Modeling of the Coupling Strength between Axons and SemiconductorMicro-TubesDaniel Diedrich1, Cornelius Bausch1, Aune Koitmae1, Robert H. Blick1,2.1Institue of Applied Physics, University of Hamburg, Hamburg, Germany,2Electrical and Computer Engineering, University of Wisconsin-Madison,Madison, WI, USA.

Maximizing the capacitive coupling between action potentials in axons andsemiconductor probes, we developed coaxial semiconductor tubes wrappedaround cortical mouse axons.In this work we model electrostatic coupling of axons with micrometer semi-conductor tubes by means of a finite-element method. We first simulate thestatic and then the dynamic electrophysiological behavior of the axon accord-ing to the Hodgkin-Huxley equations. In a second step we model the axon sur-rounded by a single-walled microtube of GaAs. The inclusion of the axonwithin the microtube increases the contact area and therefore the couplingstrength.

840-Pos Board B609Assessing Degree of Functionality of Simple and Microcolumnar NeuronalNetworks by Measuring Aspects of Information Processing: A Computa-tional ApproachMaxwell P. Henderson, Luis Cruz.Drexel University, Philadelphia, PA, USA.Some areas in the cerebral cortex are characterized by microcolumns: arrays ofinterconnected neurons which may constitute a fundamental computational unitin the brain. These microcolumns (also known as minicolumns), are formed bysmall, vertical columns of neurons that can span all layers in the gray matter.Although correlative studies have established that microcolumns can lose theirprimary characteristics in normal aging, neurological, and neurodegenerativediseases, the exact function of these structures has not been established. Usingcomputer simulations of highly detailed neuronal networks, we study whetherthere is a functional advantage in neuronal networks with a microcolumnar ge-ometry as compared to other geometrically distributed networks. In particular,these simulations take into account microcolumnar, crystalline, and randomdistributions of neurons. At the assigned location of each neuron, we positionunique, geometrically precise neurons and determine synaptic connectivity ofthe networks via three models: fully-connected, gaussian, and hypergeometricconnectivity models, taking into account the physical distances between theneurons. By utilizing the neuronal simulation package NEURON, we performfunctional tests on these neuronal networks, such as information maintenanceand scaling effects of network growth. Results on the advantages and disadvan-tages of each network topology are presented, and arguments are formulatedthat could explain microcolumnar neuronal networks as a natural evolutionto maximize information processing in the brain.

841-Pos Board B610Generalized Framework to Study Brain Weighted NetworksLuis M. Colon-Perez, Michelle Couret, William Triplett, Thomas Mareci.University of Florida, Gainesville, FL, USA.In recent years, a great deal of attention has been given to the development ofnetwork theory and its application to the brain. Employing graph theory, highangular resolution diffusion imaging (HARDI) and tractography the brain canbe studied as a network by defining anatomical regions as nodes and white mat-ter fiber bundles as edges. Using a binary description of brain network connec-tivity, studies have shown that the brain is arranged following a small worldtopology. The topology of binary networks is determined by calculating the de-gree distribution, geodesic path length and clustering coefficient. These mea-sures are influenced by spatial and angular resolution, seed density andthresholding. In this study, we use a dimensionless edge weight to minimizethe effects of seed density and fiber scale in the network construction. This pro-vides a generalized framework for analyzing weighted networks, which mini-mizes the effects of data acquisition and processing. We compare the analysisof brain connectivity using binary networks with the analysis using weightednetworks, by employing generalizations of degree, geodesic path length andclustering coefficient. Human networks were created from HARDI data often repeated scans of one subject acquired using a 3T scanner, with an isotropicresolution of 2mm, 6 diffusion weightings of 100 s/mm2 and 64 with 1000s/mm2. HARDI rat data was acquired from 4 excised rat brains on a 17.6T mag-net, with an isotropic resolution of 190 mm, 7 diffusion weightings of 100s/mm2 and 64 with 2225 s/mm2. Diffusion displacement probabilities werecalculated with a model that estimates multiple fibers per voxel and allowsthe estimation of fiber crossings. Binary networks were analyzed using R andcompared to their weighted counterparts. The proposed framework suggeststhat weighted networks are more robust and less effected by noise andthresholding.