| | Models | Description |
| 1. |
2D model of olfactory bulb gamma oscillations (Li and Cleland 2017)
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This is a biophysical model of the olfactory bulb (OB) that contains three types of neurons: mitral cells, granule cells and periglomerular cells. The model is used to study the cellular and synaptic mechanisms of OB gamma oscillations. We concluded that OB gamma oscillations can be best modeled by the coupled oscillator architecture termed pyramidal resonance inhibition network gamma (PRING). |
| 2. |
3D model of the olfactory bulb (Migliore et al. 2014)
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This entry contains a link to a full HD version of movie 1 and the NEURON code of the paper:
"Distributed organization of a brain microcircuit analysed by three-dimensional modeling: the olfactory bulb" by M Migliore, F Cavarretta, ML Hines, and GM Shepherd. |
| 3. |
3D olfactory bulb: operators (Migliore et al, 2015)
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"... Using a 3D model of mitral and granule cell interactions supported by experimental findings, combined with a matrix-based representation of glomerular operations, we identify the mechanisms for forming one or more glomerular units in response to a given odor, how and to what extent the glomerular units interfere or interact with each other during learning, their computational role within the olfactory bulb microcircuit, and how their actions can be formalized into a theoretical framework in which the olfactory bulb can be considered to contain "odor operators" unique to each individual. ..." |
| 4. |
A computational model of oxytocin modulation of olfactory recognition memory (Linster & Kelsch 2019)
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Model of olfactory bulb (OB) and anterior olfactory nucleus (AON) pyramidal cells. Includes olfactory sensory neurons, mitral cells, periglomerular, external tufted and granule interneurons and pyramidal cells. Can be built to include a feedback loop between OB and AON. Output consists of voltage and spikes over time in all neurons. Model can be stimulated with simulated odorants. The code submitted here has served for a number of modeling explorations of olfactory bulb and cortex.
The model architecture is defined in "bulb.dat" with synapses defined in "channels.dat". The main function to run the model can be found in "neuron.c". Model architecture is constructed in "set.c" from types defined in "sim.c". A make file to create an executable is located in "neuron.mak". |
| 5. |
A two-layer biophysical olfactory bulb model of cholinergic neuromodulation (Li and Cleland 2013)
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This is a two-layer biophysical olfactory bulb (OB) network model to study cholinergic neuromodulation. Simulations show that nicotinic receptor activation sharpens mitral cell receptive field, while muscarinic receptor activation enhances network synchrony and gamma oscillations. This general model suggests that the roles of nicotinic and muscarinic receptors in OB are both distinct and complementary to one another, together regulating the effects of ascending cholinergic inputs on olfactory bulb transformations. |
| 6. |
ACh modulation in olfactory bulb and piriform cortex (de Almeida et al. 2013;Devore S, et al. 2014)
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This matlab code was used in the papers de Almeida, Idiart and Linster, (2013), Devore S, de Almeida L, Linster C (2014) .
This work uses a computational model of the OB and PC and their common cholinergic inputs to investigate how bulbar cholinergic modulation affects cortical odor processing. |
| 7. |
Gamma-beta alternation in the olfactory bulb (David, Fourcaud-Trocmé et al., 2015)
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This model, a simplified olfactory bulb network with mitral and granule cells, proposes a framework for two regimes of oscillation in the olfactory bulb:
1 - a weak inhibition regime (with no granule spike) where the network oscillates in the gamma (40-90Hz) band
2 - a strong inhibition regime (with granule spikes) where the network oscillates in the beta (15-30Hz) band.
Slow modulations of sensory and centrifugal inputs, phase shifted by a quarter of cycle, possibly combined with short term depression of the mitral to granule AMPA synapse, allows the network to alternate between the two regimes as observed in anesthetized animals. |
| 8. |
Large scale model of the olfactory bulb (Yu et al., 2013)
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The readme file currently contains links to the results for all the 72 odors investigated in the paper, and the movie showing the network activity during learning of odor k3-3 (an aliphatic ketone).
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| 9. |
Lateral dendrodenditic inhibition in the Olfactory Bulb (David et al. 2008)
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Mitral cells, the principal output neurons of the olfactory bulb, receive direct synaptic activation from primary sensory neurons. Shunting inhibitory inputs delivered by granule cell interneurons onto mitral cell lateral dendrites are believed to influence spike timing and underlie coordinated field potential oscillations. Lateral dendritic shunt conductances delayed spiking to a degree dependent on both their electrotonic distance and phase of onset. Recurrent inhibition significantly narrowed the distribution of mitral cell spike times, illustrating a tendency towards coordinated synchronous activity. This result suggests an essential role for early mechanisms of temporal coordination in olfaction. The model was adapted from Davison et al, 2003, but include additional noise mechanisms, long lateral dendrite, and specific synaptic point processes. |
| 10. |
Mitral cell activity gating by respiration and inhibition in an olfactory bulb NN (Short et al 2016)
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To explore interactions between respiration, inhibition, and olfaction,
experiments using light to active channel rhodopsin in sensory neurons expressing Olfactory Marker Protein were performed in mice and modeled in silico.
This archive contains NEURON models that were run on parallel computers to explore the interactions between varying strengths of respiratory activity and olfactory sensory neuron input and the roles of periglomerular, granule, and external tufted cells in shaping mitral cell responses. |
| 11. |
Olfactory bulb cluster formation (Migliore et al. 2010)
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Functional roles of distributed synaptic clusters in the mitral-granule cell network of the olfactory bulb. |
| 12. |
Olfactory bulb juxtaglomerular models (Carey et al., 2015)
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" ...We investigated how OB circuits shape inhalation-driven
dynamics in MCs using a modeling approach that was highly constrained by experimental
results. First, we constructed models of canonical OB circuits that included mono- and disynaptic
feedforward excitation, recurrent inhibition and feedforward inhibition of the MC. We then used
experimental data to drive inputs to the models and to tune parameters; inputs were derived from
sensory neuron responses during natural odorant sampling (sniffing) in awake rats, and model
output was compared to recordings of MC responses to odorants sampled with the same sniff
waveforms. This approach allowed us to identify OB circuit features underlying the temporal
transformation of sensory inputs into inhalation-linked patterns of MC spike output.
..." |
| 13. |
Olfactory bulb microcircuits model with dual-layer inhibition (Gilra & Bhalla 2015)
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A detailed network model of the dual-layer dendro-dendritic inhibitory microcircuits in the rat olfactory bulb comprising compartmental mitral, granule and PG cells developed by Aditya Gilra, Upinder S. Bhalla (2015).
All cell morphologies and network connections are in NeuroML v1.8.0. PG and granule cell channels and synapses are also in NeuroML v1.8.0. Mitral cell channels and synapses are in native python. |
| 14. |
Olfactory bulb mitral and granule cell column formation (Migliore et al. 2007)
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In the olfactory bulb, the processing units for odor discrimination are believed
to involve dendrodendritic synaptic interactions between mitral and granule cells.
There is increasing anatomical evidence that these cells are organized in columns,
and that the columns processing a given odor are arranged in widely distributed arrays.
Experimental evidence is lacking on the underlying learning mechanisms for how these
columns and arrays are formed.
We have used a simplified realistic circuit model to test the hypothesis that
distributed connectivity can self-organize through an activity-dependent dendrodendritic
synaptic mechanism.
The results point to action potentials propagating in the mitral cell lateral dendrites
as playing a critical role in this mechanism, and suggest a novel and robust learning
mechanism for the development of distributed processing units in a cortical structure.
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| 15. |
Olfactory bulb mitral and granule cell: dendrodendritic microcircuits (Migliore and Shepherd 2008)
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This model shows how backpropagating action potentials in the long lateral dendrites of mitral cells, together with granule cell actions on mitral cells within narrow columns forming glomerular units, can provide a mechanism to activate strong local inhibition between arbitrarily distant mitral cells. The simulations predict a new role for the dendrodendritic synapses in the multicolumnar organization of the granule cells. |
| 16. |
Olfactory Bulb mitral-granule network generates beta oscillations (Osinski & Kay 2016)
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This model of the dendrodendritic mitral-granule synaptic network generates gamma and beta oscillations as a function of the granule cell excitability, which is represented by the granule cell resting membrane potential. |
| 17. |
Olfactory Bulb Network (Davison et al 2003)
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A biologically-detailed model of the mammalian olfactory bulb, incorporating
the mitral and granule cells and the dendrodendritic synapses between them.
The results of simulation experiments with electrical stimulation agree
closely in most details with published experimental data. The model predicts
that the time course of dendrodendritic inhibition is dependent on the
network connectivity as well as on the intrinsic parameters of the synapses.
In response to simulated odor stimulation, strongly activated mitral cells
tend to suppress neighboring cells, the mitral cells readily synchronize
their firing, and increasing the stimulus intensity increases the degree of
synchronization. For more details, see the reference below. |
| 18. |
Olfactory bulb network: neurogenetic restructuring and odor decorrelation (Chow et al. 2012)
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Adult neurogenesis in the olfactory bulb has been shown experimentally
to contribute to perceptual learning. Using a computational network
model we show that fundamental aspects of the adult neurogenesis
observed in the olfactory bulb -- the persistent addition of new
inhibitory granule cells to the network, their activity-dependent
survival, and the reciprocal character of their synapses with the
principal mitral cells -- are sufficient to restructure the network
and to alter its encoding of odor stimuli adaptively so as to reduce
the correlations between the bulbar representations of similar
stimuli. The model captures the experimentally observed
role of neurogenesis in perceptual learning and the enhanced response
of young granule cells to novel stimuli. Moreover, it makes specific
predictions for the type of odor enrichment that should be effective
in enhancing the ability of animals to discriminate similar odor
mixtures. NSF grant DMS-0719944.
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| 19. |
Olfactory Computations in Mitral-Granule cell circuits (Migliore & McTavish 2013)
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Model files for the entry "Olfactory Computations in Mitral-Granule Cell Circuits" of the Springer Encyclopedia of Computational Neuroscience by Michele Migliore and Tom Mctavish.
The simulations illustrate two typical Mitral-Granule cell circuits in the olfactory bulb of vertebrates: distance-independent lateral inhibition and gating effects.
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| 20. |
Parallel odor processing by mitral and middle tufted cells in the OB (Cavarretta et al 2016, 2018)
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"[...] experimental findings suggest that
MC and mTC may encode parallel and complementary odor representations. We
have analyzed the functional roles of these pathways by using a morphologically
and physiologically realistic three-dimensional model to explore the MC and
mTC microcircuits in the glomerular layer and deeper plexiform layers. [...]"
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| 21. |
Self-organized olfactory pattern recognition (Kaplan & Lansner 2014)
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" ...
We present a large-scale network model with single and multi-compartmental Hodgkin–Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC).
Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants.
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The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian–Bayesian learning.
We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants.
Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures." |
| 22. |
Synchrony by synapse location (McTavish et al. 2012)
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This model considers synchrony between mitral cells induced via shared
granule cell interneurons while taking into account the spatial
constraints of the system. In particular, since inhibitory inputs
decay passively along the lateral dendrites, this model demonstrates
that an optimal arrangement of the inhibitory synapses will be near
the cell bodies of the relevant mitral cells. |
