Building a 3D model of the mitral-granule cell network in the olfactory bulb
-
1
Yale University, Neurobiology, United States
-
2
National Research Council, Institute of Biophysics, Italy
We are interested in studying the basic mechanisms involved in odor recognition, a widely studied experimental model of sensory information processing. Recent findings have shown that odors may activate spatially distributed sites in the olfactory bulb with a sparse, columnar-like organization of mitral and granule cells. This organization challenges the classical center-surround organization, and there is thus a need to identify a new paradigm for signal discrimination that could have general implications as well for other brain regions. The possible underlying circuitry and the computational properties of the olfactory bulb have been widely investigated experimentally, especially in terms of odor selectivity and dynamics of cell responses. However, experiments are usually carried out in single cells or in small randomly selected sets of cells. This has prevented a clear understanding of the spatio-temporal organization of the mitral-granule cell network in representing an odor input, which requires simultaneous recording from a relevant subset of mitral and granule cells activated by an odor. The functional effects of a network-wide process such as lateral inhibition, in relation to the patterns of glomeruli activated by different odors, remain thus relatively unknown and difficult to explore experimentally.
The main challenge we are addressing here is the development of a 3D model of the mitral-granule cell network, allowing direct input of the experimental data for individual glomerular activation, in order to demonstrate and predict the learning mechanisms that will ultimately be responsible for the early processing stages of the sensory inputs. For this purpose, we implemented a 2mm^2 3D model of the olfactory bulb (about 1/20th of the entire system). Several 3D reconstructions of mitral cells with full dendritic trees (from Igarashi et al., 2012) were analyzed to extract morphological parameters to generate a population of some 700 synthetic mitral cells, 5 for each glomerulus. Approximately 20000 granule cells were then randomly inserted into the network and connected using a collision detection algorithm. The input activity elicited in 127 glomeruli in the dorsal olfactory bulb during presentation of 19 natural odorants (kindly provided by Alan Carleton, from Vincis et al., 2012) was then used to drive self-organization of the network under different conditions of odor input. This is the first 3D simulation of the olfactory bulb microcircuit using realistic cell properties and network connectivity. It provides a new framework for investigating the functions of a brain system.
The figure shows a rendering of the olfactory bulb 3D model. Green spheres: glomeruli; red spheres: activated glomeruli; white lines: mitral cell dendrites.
References
Igarashi, KM et al., (2012) Parallel mitral and tufted cell pathways route distinct odor information to different targets in the olfactory cortex, J. Neurosci. 32:7970 –7985.
Vincis R, Gschwend O, Bhaukaurally K, Beroud J, Carleton A (2011) Dense representation of natural odorants in the mouse olfactory bulb, Nat. Neurosci. 15:537-539.
Keywords:
Olfactory Bulb,
mitral-granule cell network,
lateral inhibition,
odor processing,
natural odorants
Conference:
Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013.
Presentation Type:
Oral presentation
Topic:
Large scale modeling
Citation:
Migliore
M,
Hines
ML,
Cavarretta
F and
Shepherd
GM
(2013). Building a 3D model of the mitral-granule cell network in the olfactory bulb.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2013.
doi: 10.3389/conf.fninf.2013.09.00098
Copyright:
The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers.
They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.
The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.
Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.
For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.
Received:
26 Mar 2013;
Published Online:
11 Jul 2013.
*
Correspondence:
Dr. Michele Migliore, Yale University, Neurobiology, New Haven, United States, michele.migliore@cnr.it