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--- p:subsmp1 [2020/04/01 03:44] – cjj
+++ p:subsmp1 [2020/04/01 07:53] (current) – [Principal component analysis] cjj
@@ Line 1: / Line 1: @@
 ======Subsampling miniscope data======
-{{ :p:subsmp1:subsamples_0425-3t1_pca.svg |}}
+Random subsets of the neurons are chosen for analysis. The results are compared to original system and among different sub-sample sizes.
+=====Principal component analysis=====
+| {{  :p:subsmp1:subsamples_0425-3t1_pca.svg?380  |}} | {{  :p:subsmp1:subsamples_bialek_pca.svg?380  |}} |
+The errorbars are calculated from the standard deviation of 8 random subsets for each given size. It's seems power law scaling $\lambda_k \propto k^{-\alpha}$ with $\alpha \approx 0.5$ is more accurate for a sub-sampled system of smaller size.
+=====Thermodynamics of subsamples=====
+Boltzmann learning is performed for each random sub sample for the model parameters, $h_i$ and $J_{i,j}$. The specific heat curves of the resulted models are calculated and as shown below.
 {{ :p:subsmp1:subsamples_0425-3t1.svg |}}
+The criticality condition ($T\approx 1.0$) appears pretty robust to sub sampling where only a portion of the neurons from a large network is observed.