2. High-level interface

Characterizing neural populations with svGPFA models involves the following steps:

1. Building an empty model

   model = stats.svGPFA.svGPFAModelFactory.SVGPFAModelFactory.buildModel(
       conditionalDist=stats.svGPFA.svGPFAModelFactory.PointProcess,
       linkFunction=stats.svGPFA.svGPFAModelFactory.ExponentialLink,
       embeddingType=stats.svGPFA.svGPFAModelFactory.LinearEmbedding,
       kernels=kernels)
   

   by specifying a conditional distribution (e.g., point-process, PointProcess), an embedding type (e.g., linear, LinearEmbedding), a link function (e.g., exp()) and providing a set of kernels (Kernel).

2. Setting initial parameters

   model.setParamsAndData(
       measurements=spikes_times,
       initial_params=params["initial_params"],
       eLLCalculationParams=params["ell_calculation_params"],
       priorCovRegParam=params["optim_params"]["prior_cov_reg_param"])
   

3. Estimating parameters

   svEM = stats.svGPFA.svEM.SVEM()
   lowerBoundHist = svEM.maximize(model=model, measurements=spikeTimes,
                                  initialParams=initialParams,
                                  quadParams=quadParams,
                                  optimParams=optimParams)
   

   by providing a set of measurements, spikeTimes, initial parameters, initialParams, quadrature parameters, quadParams and optimisation parameters, optimParams.

4. Assessing goodness-of-fit

   diffECDFsX, diffECDFsY, estECDFx, estECDFy, simECDFx, simECDFy, cb = \
       gcnu_common.stats.pointProcesses.tests.\
           KSTestTimeRescalingNumericalCorrection(spikes_times=spikes_times_GOF,
                                                  cif_times=trial_times_GOF,
                                                  cif_values=cif_values_GOF,
                                                  gamma=ksTestGamma)
   fig = svGPFA.plot.plotUtilsPlotly.\
       getPlotResKSTestTimeRescalingNumericalCorrection(
           diffECDFsX=diffECDFsX, diffECDFsY=diffECDFsY, estECDFx=estECDFx,
           estECDFy=estECDFy, simECDFx=simECDFx, simECDFy=simECDFy, cb=cb,
           title=title)
   fig.show()
   

   

5. Plotting model’s parameters

   fig = svGPFA.plot.plotUtilsPlotly.getPlotLatentAcrossTrials(times=trials_times.numpy(), latentsMeans=testMuK, latentsSTDs=torch.sqrt(testVarK), indPointsLocs=indPointsLocs, latentToPlot=latentToPlot, trials_colors=trials_colors, xlabel="Time (msec)")
   fig.show()