Circuits that contain the Neuron : Olfactory bulb main interneuron periglomerular GABA cell

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    Models   Description
1. 2D model of olfactory bulb gamma oscillations (Li and Cleland 2017)
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. A computational model of oxytocin modulation of olfactory recognition memory (Linster & Kelsch 2019)
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".
3. A two-layer biophysical olfactory bulb model of cholinergic neuromodulation (Li and Cleland 2013)
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.
4. ACh modulation in olfactory bulb and piriform cortex (de Almeida et al. 2013;Devore S, et al. 2014)
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.
5. Mitral cell activity gating by respiration and inhibition in an olfactory bulb NN (Short et al 2016)
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.
6. Olfactory bulb juxtaglomerular models (Carey et al., 2015)
" ...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. ..."
7. Olfactory bulb microcircuits model with dual-layer inhibition (Gilra & Bhalla 2015)
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.
8. Self-organized olfactory pattern recognition (Kaplan & Lansner 2014)
" ... 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. ... 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."
9. Understanding odor information segregation in the olfactory bulb by MC/TCs (Polese et al. 2014)
Odor identification is one of the main tasks of the olfactory system. It is performed almost independently from the concentration of the odor providing a robust recognition. This capacity to ignore concentration information does not preclude the olfactory system from estimating concentration itself. Significant experimental evidence has indicated that the olfactory system is able to infer simultaneously odor identity and intensity. However, it is still unclear at what level or levels of the olfactory pathway this segregation of information occurs. In this work, we study whether this odor information segregation is performed at the input stage of the olfactory bulb: the glomerular layer.

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