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Neural Data Analysis (via Tensor based techniques)

Tensor regression/Tensor decomposition (python)

Workflow:

  1. Load the "SharedData.mat" dataset on MATLAB and run the following scripts in the order highlighted below:
  • [spikeData, stateData] = neuronRaster_acrossSessions(1e-3); (Here, 1e-3 is the binWidth (1ms))
  • convert_spikeStructure_indexable
  • convert_cursorStateStruct_indexable
  1. Open the jupyter notebook "Tensor_analysis_gpr" and run the python script (for tensor decomposition and regression) as per requirement.

Autocorrelation matrix

If we need to do any sort of regression or classification, the assumption being made is that each sample (each trial/time-point) is independent and identically distributed.

-- It will be useful to see how the neuronal activity (across 96 neurons) correlate across time (for every task).

Workflow:

  1. Run this script:
  • autocorrelation_neuronalMatrix: This will evaluate the temporal correlation matrix (for different trial-averaged spike-bins) and plot an autocorrelation function (f(tau)).