bhvRDM for representations in CNNs about neurora

[ZitongLu1996]

Very nice question!
In some cases, the correlation coefficient is used to describe the similarity ignoring the value is positive or negative. So I use a np.abs() function here... In many functions in module rdm_cal, you can find a parameter abs. By this parameter, user can select to calculate the 1-r or 1-abs(r). I' sorry that I didn't add this choice when sub_opt=1 in function bhvRDM. Under this special condition, we cannot calculate the Pearson correlation and we can only calculate the distance. But sorry again, I forget to add a choice for users to select whether computing the absolute distance or not for this condition. I'll update NeuroRA soon!
Thanks for your careful reading my codes!

[ZitongLu1996]

Hi, Arjun! I just updated NeuroRA!
You can download the new version 1.1.3.4: pip install --upgrade neurora
In this version, if you set abs=False, the dissimilarity would be 1-dis not 1-abs(dis).

If you want to put the nodes' values of each layer in CNN, I recommend that you can let the shape of the data_ly_i as [n_conditions, n_nodes, 1] for layer i in a CNN model as the input for bhvRDM function. like:
RDM_ly_i = bhvRDM(data, sub_opt=0, abs=False)
By this replacement (nodes as the subjects), you can obtain the RDM for each CNN layer with the dissimilarity in a RDM by calculating the 1 - correlation coefficient between vector1=[valueofnode1_condition1, ... , valueofnoden_condition1] and vector2=[valueofnode1_condition2, ... , valueofnoden_condition2]. And so on...

Hope this help! If you have any other questions, you can ask me.

[ZitongLu1996]

I think my suggestions above can work. RDM_ly_i = bhvRDM(data, sub_opt=0, abs=False) by this line-code, the calculation is base on Pearson correlation (you can see the codes line 113 - 142, especially line 135 in rdm_cal.py) and the dissimilarities in RDM are calculated by 1-r.
I think I have answered your question...

[ZitongLu1996]

Maybe you are puzzled about how to put the data of a CNN into bhvRDM? For example, in a certain CNN, its layer i has 64 feature maps (size of each feature map is 128 by 128). You can spread this 64 by 128 by 128 array into a vector which shape will be [1048576]. So n_nodes=1048576 here, and as the suggestion I told yesterday, the shape of the data as the input of layer i in certain CNN for RDM calculation should be [n_conditions, n_nodes, 1]. Then use RDM_ly_i = bhvRDM(data, sub_opt=0, abs=False), you can get a RDM corresponding to layer i.
Hope this more detailed explanation help!

[arjung128]

Hi Zitong!

Thank you very much for your prompt reply. I was very impressed by how quickly you responded to my question and updated the library -- Thanks a ton!

I'm sorry, I do not fully understand: bhvRDM still uses absolute distance and not the Pearson correlation for obtaining the RDM, correct? I am not sure how / whether I can use this function to get RDM values of CNN representations (3D) based on the Pearson correlation using this function... is this possible? Or are you suggesting I can write my own function to do this with the equation in your response?

Thanks once again! :)

[arjung128]

Oh I see now. Sorry, I had missed line 135. Thanks so much, Zitong!