MicrocircuitDB: CA1 pyramidal neuron: synaptically-induced bAP predicts synapse location (Sterratt et al. 2012)

CA1 pyramidal neuron: synaptically-induced bAP predicts synapse location (Sterratt et al. 2012)

Download zip file Auto-launch
Help downloading and running models

Model Information
Model File
Model Views

Accession:144490

This is an adaptation of Poirazi et al.'s (2003) CA1 model that is used to measure BAP-induced voltage and calcium signals in spines after simulated Schaffer collateral synapse stimulation. In the model, the peak calcium concentration is highly correlated with soma-synapse distance under a number of physiologically-realistic suprathreshold stimulation regimes and for a range of dendritic morphologies. There are also simulations demonstrating that peak calcium can be used to set up a synaptic democracy in a homeostatic manner, whereby synapses regulate their synaptic strength on the basis of the difference between peak calcium and a uniform target value.
Reference:
1 . Sterratt DC, Groen MR, Meredith RM, van Ooyen A (2012) Spine calcium transients induced by synaptically-evoked action potentials can predict synapse location and establish synaptic democracy. PLoS Comput Biol 8:e1002545 [PubMed]

Citations Citation Browser

Model Information (Click on a link to find other models with that property)

Model Type:	Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s):	Hippocampus CA1 pyramidal GLU cell;
Channel(s):	I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I Mixed; I R; I_AHP;
Gap Junctions:
Receptor(s):	AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment:	NEURON;
Model Concept(s):	Dendritic Action Potentials; Synaptic Plasticity;
Implementer(s):	Sterratt, David ; Groen, Martine R [martine.groen at gmail.com];

Search NeuronDB for information about: Hippocampus CA1 pyramidal GLU cell; AMPA; NMDA; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I Mixed; I R; I_AHP;

Download the displayed file

poirazi-nmda-car

validation-plots

bpap-dendburst.hoc

bpap-graphics.hoc

bpap-record.hoc

bpap-scaling.hoc

bpap-sims-cell1.hoc

bpap-sims-cell2.hoc

bpap-sims-scaling.hoc

bpap-somainj.hoc

bpap-spiketrain.hoc

ca1_mrg_cell1.hoc

ca1_mrg_cell2.hoc

ca1_poirazi.hoc

ChannelBlocker.hoc

CrossingFinder.hoc

figure-example.R

figures-common.R

NmdaAmpaSpineSynStim.hoc

NmdaAmpaSynStim.hoc

ObjectClass.hoc

plotscalingresults_pergroup1.m

plotscalingresults5.m

PointProcessDistributor.hoc

ReferenceAxis.hoc

scaling_plots.m

SimpleSpine.hoc

TreePlotArray.hoc

validate-bpap.hoc

function[databinned]=binaverages(data,bininterval)
    nrbins=floor(size(data,1)/bininterval);
    for i=1:nrbins
        indexstart=bininterval*(i-1)+1;
        indexend=bininterval*i;
        databinned(i,:)=nanmean(data(indexstart:indexend,:));
    end
end

Load Model View