Olfactory bulb juxtaglomerular models (Carey et al., 2015)

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Accession:152111
" ...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. ..."
Reference:
1 . Carey RM, Sherwood WE, Shipley MT, Borisyuk A, Wachowiak M (2015) Role of intraglomerular circuits in shaping temporally structured responses to naturalistic inhalation-driven sensory input to the olfactory bulb. J Neurophysiol 113:3112-29 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Olfactory bulb;
Cell Type(s): Olfactory bulb main mitral cell; Olfactory receptor neuron; Olfactory bulb main interneuron periglomerular cell; Olfactory bulb main interneuron granule MC cell; Olfactory bulb main interneuron granule TC cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Olfaction;
Implementer(s):
Search NeuronDB for information about:  Olfactory bulb main mitral cell; Olfactory receptor neuron; Olfactory bulb main interneuron periglomerular cell; Olfactory bulb main interneuron granule MC cell; Olfactory bulb main interneuron granule TC cell;
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carey_2013
Data
DataProcessing
Loops
Models
Params
Vfields
readme.md
example.m
startup.m
                            
%% Add paths
startup

%% Set up the run parameters
tracefile = 'scaled_orn_input_depr'; % the trace file from the Data/Inputs/ORN_Input_Data directory
modelfnc = @MCRI_PGslow; % which model from Models directory, here MC-PG with slow synapse
ORNscale = 1; % the gain for ORN input


%% Run the model on all of the glomerulus inputs (this will take a very long time)
% In this example, we change the PGMCS.tc parameter from its default value
% to 170 (note the underscore replaces dot), and we turn the special
% parameter 'save_traces' on -- this will save voltage traces, etc. from
% the run. If 'save_traces' is omitted, then only the spike times will be
% saved.
doloop(tracefile, ORNscale, modelfnc, 'PGMCS_tc', '170', 'save_traces', 1)


%% Load the saved output
% The doloop function will save results with a filename following a
% particular pattern. Here we recreate it.
modelname = func2str(modelfnc);
scalingstr = regexprep(num2str(ORNscale),'\.','_');
extrastr = '_PGMCS_tc170_save_traces1'; % concatenate parameter names, values that were changed, '' if none    
loadname = sprintf('%s_%s_gain%s%s',modelname,tracefile,scalingstr,extrastr);
load(loadname);


%% Plot the MC and PG voltage traces together for the first glomerular input
plot(data(1).T, data(1).X(:,1))

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