How does the analysis work ?

There is always two files for each analysis : the one for the injected cells and the one for spontaneous activity. In the spontaneous ones, the parameters such as :

• injection_start
• ref_neuron
• ...
  must be entered by hand.

For correlation analysis, same algorithm with only the line where the MI function is called changed to correlate (signal).

Simulation codes :

The time var are in ms

All the analysises start at the lign if 'Vm' ... The code before is not mine but my tutor's as for the run.py

Analysis_gauss :

Definition of the parameters by hand :

• the interval on which we will calculate the MI
• the number_of_annulus which is in fact in how many part we divide the disk around the central neuron
• the reference neuron, ref_neuron, center of the Annulus
• the ray r of the disk around ref_neuron
• the Time_delay with when it starts, when it ends and the step

The algorithm calculate the distance between each neuron (except the injected ones) and the central ones and assign them an annulus depending on the distance. The empty annulus are deleted.

Then for each time, calculate the mean MI on each annulus. After it, it saves the time when the max of the MI occurs for each annulus and plot it (for the raw and filtered with savgol_filter data) with a linear regression. The slope of the obtained line is the speed of diffusion.

Analysis_colormap_vm :

It plots the colormap of Vm at each time and can also plot the vm of each neuron as a function of time.

Analysis_gradient :

It plots the MI matrix with contour based on filtered data set, the norm of each gradient vector and finally, average MI and average Gradient Norm for each cell.

Analysis_contour_MI_Vm :

It plots the MI matrix with contour based on filtered data set.

Analysis_delay_2cell :

It plots the VM of two cells, their MI and their position on the map.

Analysis_mean_spikes :

Definition of the parameters by hand :

• the time_window on which we will calculate the mean number of spikes
• the precision is the size of the square window on which the mean is computed
• the Time_delay with when it starts, when it ends and the step It plots a map of the number of spikes for each neuron in the time_window, the mean number of spikes in the square window starting frow each neuron as its low left corner and the Vm.

Mices DATA

The data are saved as a 100x100x511 list (size x size x time) Each algorithm starts by opening a folder and then analyses each set of data in it. The parameters are the same as the one for simulation and the running is the same as well.

Analysis_delay_2cells

Same as above

Analysis_diffusion_spont :

Plots time of max as a function of the annulus, linear regression and annulus plotting.

Analysis_gradient :

Same as simulation but without contour.

Analysis_nostim and Analysis_mouses :

Plot VM and MI in the same figure, and can also do the analysis on the annulus.

Analysis_complete :

Takes the data of the four mices (3-4-5-6) and for each :

• plots the mean VM in function of time
• plots the VM and MI in the same figure
• plots the norm of the gradients of the VM and MI
• plots the mean value of the VM taken in each pixel, same for the MI, and the gradient norm When done, it computes the mean for all the data. Possibility to pu a threshold to filter the data (between 0.001 and 0.0006 advised)

Final Images

Organisation

This file contains at leat one image by possible case (spontaneous/simulated, diluted/not diluted, simulation/mice...) and a description of the parameters to enter in which analysis to obtain the picture.

Ongoing

Currently working on these kind of data given by the Anlysis_complete code to study the propagation.