Name
Abstract | ||
|---|---|---|
Neurons communicate via electrochemical currents, thus simulation is typically accomplished through modeling the dynamical nature of the neuron's electrical properties. In this paper we utilize Hodgkin-Huxley model and briefly compare it to Leaky integrate-and-fire model. The Hodgkin-Huxley model is a conductance-based model where current flows across the cell membrane due to charging of the membrane capacitance, and movement of ions across ion channels. The leaky integrate-and-fire model is widely used example of formal spiking neuron model. In it the action potentials are generated when the membrane potential crosses a fixed threshold value and the dynamics of the membrane potential is governed by a ‘leaky current’. Conductance-based models (HH models) for excitable cells are developed to help understand underlying mechanisms that contribute to action potential generation, repetitive firing and oscillatory patterns. These factors contribute in modeling the olfactory bulb's dynamic behaviors. Due to these characteristics, we have focused on the conductance-based neuronal models in this work. The model consists of input, mitral and granule layer, connected by synapses. A series of simulations accounting for various olfactory activities are run to explain certain effects of the dynamic behavior of the olfactory bulb (OB). These simulation results are verified against documented evidence in published Journal papers. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.procs.2013.09.235 | Procedia Computer Science |
Keywords | Field | DocType |
olfactory bulb,Hodgkin-Huxley,neuron models | Data mining,Olfactory bulb,Synapse,Membrane potential,Biological neuron model,Biological system,Computer science,Artificial intelligence,Ion channel,Neuron,Olfactory Learning,Hodgkin–Huxley model | Conference |
Volume | ISSN | Citations |
20 | 1877-0509 | 0 |
PageRank | References | Authors |
0.34 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| George Georgiev | 1 | 5 | 4.48 |
| Mrinal Gosavi | 2 | 0 | 0.34 |
| Iren Valova | 3 | 136 | 25.44 |
| Natacha Gueorguieva | 4 | 63 | 12.46 |