Biologically Inspired Olfactory Learning Architecture
Author(s) -
George Georgiev,
Mrinal Gosavi,
Iren Valova,
Natacha Gueorguieva
Publication year - 2013
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2013.09.235
Subject(s) - computer science , olfactory bulb , biological system , neuroscience , conductance , olfactory system , biological neuron model , hodgkin–huxley model , artificial intelligence , physics , artificial neural network , biology , condensed matter physics , central nervous system
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
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