Prob. Inference of Short-Term Synaptic Plasticity in Neocort. Microcircuits (Costa et al. 2013)

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" ... As a solution (for Short Term Plasticity (STP) inference), we introduce a Bayesian formulation, which yields the posterior distribution over the model parameters given the data. First, we show that common STP protocols yield broad distributions over some model parameters. Using our result we propose a experimental protocol to more accurately determine synaptic dynamics parameters. Next, we infer the model parameters using experimental data from three different neocortical excitatory connection types. This reveals connection-specific distributions, which we use to classify synaptic dynamics. Our approach to demarcate connection-specific synaptic dynamics is an important improvement on the state of the art and reveals novel features from existing data."
1 . Costa RP, Sjostrom PJ, van Rossum MC (2013) Probabilistic Inference of Short-Term Synaptic Plasticity in Neocortical Microcircuits Front. Comput. Neurosci. [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism:
Cell Type(s):
Gap Junctions:
Simulation Environment: MATLAB;
Model Concept(s): Short-term Synaptic Plasticity;
Implementer(s): Costa, Rui Ponte [ruipontecosta at];
% Bayesian Inference of Tsodyks-Markram Short-term Synaptic Plasticity model
%  data_path: path to the xls data file
%  model_version: specifies the TM model formulation (1 - eTM, 2 - TM with facil, and 3 - TM)
%  f: protocol frequency
%  minp: minimum number of pulses
%  age: age to be selected from the .xls file

function [ps_map names rsq fig_dist fig_MAP] = TM_Bayesian(data_path, model_version, f, minp, age)

    global dt;
    dt = 1e-3;
    synbox_path = pwd;
    %% 0. Options
    % 0.1 Data
    freqs = f; %Presynaptic pulse frequency
    min_pulses = minp; %Number of pulses
    remove_drugs = {''};
    remove_areas = {''};
    remove_quality = {'Bad'};
    stime = min_pulses/freqs(1)-dt*2;
    cconv = 0;
    cnconv = 0;
    fig_dist = -1;
    fig_MAP = -1;
    dens = {};
        tags = {'Depression Timeconstant, D(s)', 'Facilitation Timeconstant, F(s)', 'Release Probability, U', 'Facilitation Rate, f'};
        tags = {'Depression Timeconstant, D(s)', 'Facilitation Timeconstant, F(s)', 'Release Probability, U'};
        tags = {'Depression Timeconstant, D(s)', 'Release Probability, U'};

    % 0.2 Optimization
    runs = 3;
    p_limits = [1.00e-04  5.00e-04   2;
               1.00e-04   5.00e-04   2;
               1.00e-04   5.00e-04   1;
               1.00e-04   5.00e-04   1;
               1.00e-04   5.00e-04   1];

        p_on = [1 1 1 0 1]; %Params: DFUf
        p_on = [1 1 1 0 0]; %Params: DFU
        p_on = [1 0 1 0 0]; %Params: DU    
    online_plot = 0;
    cond_reuse = 0;
    conv = 1; %Test convergence?

    %% 1. Load Model
    model = Plasticity.STP.Pheno.MarkramTsodyks98();

    %% 2. Specify protocol and data
    condition = Fitting.Loading.ePhys.STP.DL_STP.NONE_COND_NOTNORM;
    dl = Fitting.Loading.ePhys.STP.DL_STP(condition);
    dl.freqs = freqs; dl.min_pulses = min_pulses; dl.remove_drugs = remove_drugs; dl.remove_areas = remove_areas; dl.remove_quality = remove_quality;
    dl.age = age;

    [data CVs names STDs] = dl.load(data_path); %Load data
    [model.spikes model.stimes] = dl.setInput(stime); %Set spike train
    model.setDttimes(); %Create dttimes in the model (used for a more optimized)
        warning('The options selected do not match any datapoint');
        ps_map = []; names = []; rsq = [];

    %dl.stds = bsxfun(@times, ones(size(data)), CVs);
    dl.stds = STDs;

    %% 3. Load optimizer
    opt = Fitting.Optimization.MCMC.SliceSampling();
    opt.burnin = 2500;
    opt.n_samples = 7500;
        opt.width = [2 2 1 1];
        opt.width = [2 2 1];
        opt.width = [2 1];
    opt.thin = 1;
    opt.eval = @Fitting.Loading.ePhys.logpdf;

    model.run_fun = @model.run4Opt_Fast; %Analytical ver.
    opt.runs = runs;
    opt.p_limits = p_limits;
    opt.online_plot = online_plot;
    opt.cond_reuse = cond_reuse; % Starts the opt with the best from the other cond

    %% 4. Run Sampling
    opt.p_on = p_on;
    disp(' ');
    disp('>>> Starting posterior sampling..');
    [ps es] =, dl, model, [], tags, []);

    %Obtain Max a posterior params
    disp(' ');
    disp('>>> Getting maximum a posterior (MAP) solutions..');
    ps_map = opt.map_bestsample(opt.eval, ps, p_on, data, model, p_limits, dl.stds, Inf)';
    for i=1:size(data,1)
        dens{i} = kde(ps{i}(:,:)', 'rot');

    %Get R
    if(conv && runs>1) %Convergence diagnostic
        disp(' ');
        disp('>>> Testing convergence..')
        C = [1 0 0; 0 1 0; 0 0 1; 0 0 0];
        for ds=1:size(opt.dist,1) %For each dataset
            PSRF = opt.convergence(ds, opt.dist, C, tags, 0);
            PSRF = mean(PSRF,1);
            if(sum(PSRF<1.1 & PSRF>0.9)==sum(p_on))                
                disp([names{ds} ' converged!']);
                cconv = cconv + 1;
                disp([names{ds} ' didnt converged!']);
                cnconv = cnconv + 1;
        disp([num2str(cconv) '/' num2str(cnconv+cconv) ' data points converged!']);

    %% 5. Load analyzer
    ps_map = ps_map';
    an = Fitting.Analyser();
    disp(' ');
    disp('>>> Plotting..')
    [results ppr rsq rsq_tot adj_rsq adj_rsq_tot] = an.compareResults(data, names, ps_map, model, p_on, 0);
    fig_MAP = gcf;
    an.distPlot(tags, p_limits, dens, ps_map);
    fig_dist = gcf;

Costa RP, Sjostrom PJ, van Rossum MC (2013) Probabilistic Inference of Short-Term Synaptic Plasticity in Neocortical Microcircuits Front. Comput. Neurosci.[PubMed]

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