2014
DOI: 10.3389/fncom.2014.00128
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Electrical responses of three classes of granule cells of the olfactory bulb to synaptic inputs in different dendritic locations

Abstract: This work consists of a computational study of the electrical responses of three classes of granule cells of the olfactory bulb to synaptic activation in different dendritic locations. The constructed models were based on morphologically detailed compartmental reconstructions of three granule cell classes of the olfactory bulb with active dendrites described by Bhalla and Bower (1993, pp. 1948–1965) and dendritic spine distributions described by Woolf et al. (1991, pp. 1837–1854). The computational studies wit… Show more

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Cited by 4 publications
(5 citation statements)
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References 48 publications
(71 reference statements)
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“…We selected parameters to model class II behavior of MCs [ 67 ] and to establish realistic f − I curves [ 9 ] ( Fig 2C ). Following conductance based models [ 5 , 7 ], we took GCs to be integrators [ 42 , 43 ]. Other work suggests that some GCs may display resonator properties, including subthreshold membrane potential oscillations [ 68 , 69 ], but the very low oscillation frequency may make them irrelevant to excitability classification [ 43 ], especially since some GCs appear to be entirely non-resonant [ 68 ].…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…We selected parameters to model class II behavior of MCs [ 67 ] and to establish realistic f − I curves [ 9 ] ( Fig 2C ). Following conductance based models [ 5 , 7 ], we took GCs to be integrators [ 42 , 43 ]. Other work suggests that some GCs may display resonator properties, including subthreshold membrane potential oscillations [ 68 , 69 ], but the very low oscillation frequency may make them irrelevant to excitability classification [ 43 ], especially since some GCs appear to be entirely non-resonant [ 68 ].…”
Section: Resultsmentioning
confidence: 99%
“…Computational studies are necessary for understanding how this odor information is reshaped, since we lack experimental methods for interrogating this network’s structure-function dependency. However, the sheer number of neurons, encompassing tens of thousands of excitatory cells and millions of inhibitory cells [ 1 ]; intricate network architecture [ 2 4 ]; and complex spiking dynamics [ 5 9 ] make detailed biophysical simulation impractical at large scale. Thus, many studies use random connections or simple distance-dependent functions to establish MC-GC connectivity [ 10 22 ] and often study smaller networks on the order of hundreds or even tens of neurons [ 10 , 11 , 13 , 14 , 19 , 22 24 ], allowing for highly complex, conductance-based neuronal frameworks [ 7 ].…”
Section: Introductionmentioning
confidence: 99%
“…2C). Following conductance based models [5, 7], we took GCs to be integrators [42, 43]. Other work suggests that some GCs may display resonator properties, including subthreshold membrane potential oscillations [63, 64], but the very low oscillation frequency may make them irrelevant to excitability classification [43], especially since some GCs appear to be entirely non-resonant [63].…”
Section: Resultsmentioning
confidence: 99%
“…Computational studies are necessary for understanding how this odor information is reshaped, since we lack experimental methods for interrogating this network's structure-function dependency. However, the sheer number of neurons, encompassing tens of thousands of excitatory cells and millions of inhibitory cells [1]; intricate network architecture [2][3][4]; and complex spiking dynamics [5][6][7][8][9] make detailed biophysical simulation impractical at large scale. Thus, many studies use random connections or simple distance-dependent functions to establish MC-GC connectivity [10][11][12][13][14][15][16][17][18][19][20][21][22] and often study smaller networks on the order of hundreds or even tens of neurons [10,11,13,14,19,[22][23][24], allowing for highly complex, conductance-based neuronal frameworks [7].…”
Section: Introductionmentioning
confidence: 99%
“…A variety of methods have been used to study the spatiotemporal patterning of olfactory system, such as biosensors and computational models (Arruda et al , 2013; Gilra and Bhalla, 2015; Kaplan and Lansner, 2014; Migliore and Shepherd, 2008; Simoes-de-Souza et al , 2014; Ye et al , 2010; Ying et al , 2012; Yu et al , 2013). Our laboratory has made certain achievements in cell-based sensors (Ling et al , 2010; Tian et al , 2015) and the olfactory modeling and simulation (Ye et al , 2010; Ying et al , 2012).…”
Section: Introductionmentioning
confidence: 99%