Main Menu (Mobile)- Block

Main Menu - Block

janelia7_blocks-janelia7_fake_breadcrumb | block
Koyama Lab / Publications
custom | custom

Filter

facetapi-Q2b17qCsTdECvJIqZJgYMaGsr8vANl1n | block

Associated Lab

facetapi-PV5lg7xuz68EAY8eakJzrcmwtdGEnxR0 | block
facetapi-021SKYQnqXW6ODq5W5dPAFEDBaEJubhN | block
general_search_page-panel_pane_1 | views_panes

3 Publications

Showing 1-3 of 3 results
Your Criteria:
    12/25/08 | Compartmental neural simulations with spatial adaptivity.
    Rempe MJ, Spruston N, Kath WL, Chopp DL
    Journal of Computational Neuroscience. 2008 Dec;25(3):465-80. doi: 10.1007/s10827-008-0089-3

    Since their inception, computational models have become increasingly complex and useful counterparts to laboratory experiments within the field of neuroscience. Today several software programs exist to solve the underlying mathematical system of equations, but such programs typically solve these equations in all parts of a cell (or network of cells) simultaneously, regardless of whether or not all of the cell is active. This approach can be inefficient if only part of the cell is active and many simulations must be performed. We have previously developed a numerical method that provides a framework for spatial adaptivity by making the computations local to individual branches rather than entire cells (Rempe and Chopp, SIAM Journal on Scientific Computing, 28: 2139-2161, 2006). Once the computation is reduced to the level of branches instead of cells, spatial adaptivity is straightforward: the active regions of the cell are detected and computational effort is focused there, while saving computations in other regions of the cell that are at or near rest. Here we apply the adaptive method to four realistic neuronal simulation scenarios and demonstrate its improved efficiency over non-adaptive methods. We find that the computational cost of the method scales with the amount of activity present in the simulation, rather than the physical size of the system being simulated. For certain problems spatial adaptivity reduces the computation time by up to 80%.

    View Publication Page
    03/09/08 | Pyramidal neurons: dendritic structure and synaptic integration.
    Spruston N
    Nature Reviews Neuroscience. 2008 Mar;9(3):206-21. doi: 10.1038/nrn2286

    Pyramidal neurons are characterized by their distinct apical and basal dendritic trees and the pyramidal shape of their soma. They are found in several regions of the CNS and, although the reasons for their abundance remain unclear, functional studies--especially of CA1 hippocampal and layer V neocortical pyramidal neurons--have offered insights into the functions of their unique cellular architecture. Pyramidal neurons are not all identical, but some shared functional principles can be identified. In particular, the existence of dendritic domains with distinct synaptic inputs, excitability, modulation and plasticity appears to be a common feature that allows synapses throughout the dendritic tree to contribute to action-potential generation. These properties support a variety of coincidence-detection mechanisms, which are likely to be crucial for synaptic integration and plasticity.

    View Publication Page
    02/01/08 | Distribution of bursting neurons in the CA1 region and the subiculum of the rat hippocampus.
    Jarsky T, Mady R, Kennedy B, Spruston N
    Journal of Comparative Neurology. 2008 Feb 1;506(4):535-47. doi: 10.1002/cne.21564

    We performed patch-clamp recordings from morphologically identified and anatomically mapped pyramidal neurons of the ventral hippocampus to test the hypothesis that bursting neurons are distributed on a gradient from the CA2/CA1 border (proximal) through the subiculum (distal), with more bursting observed at distal locations. We find that the well-defined morphological boundaries between the hippocampal subregions CA1 and subiculum do not correspond to abrupt changes in electrophysiological properties. Rather, we observed that the percentage of bursting neurons is linearly correlated with position in the proximal-distal axis across the CA1 and the subiculum, the percentages of bursting neurons being 10% near the CA1-CA2 border, 24% at the CA1-subiculum border, and higher than 50% in the distal subiculum. The distribution of bursting neurons was paralleled by a gradient in afterdepolarization (ADP) amplitude. We also tested the hypothesis that there was an association between bursting and two previously described morphologically distinct groups of pyramidal neurons (twin and single apical dendrites) in the CA1 region. We found no difference in output mode between single and twin apical dendrite morphologies, which was consistent with the observation that the two morphologies were equally distributed across the transverse axis of the CA1 region. Taken together with the known organization of connections from CA3 to CA1 and CA1 to subiculum, our results indicate that bursting neurons are most likely to be connected to regular spiking neurons and vice versa.

    View Publication Page