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Figure 3

() An example frame of the simulated data. Green and red squares will correspond to panels () and () below, respectively. () The temporal mean of the cellular activity in the simulation. () The …

Video 3

Top left: correlation image of the filtered data. Red dots are centers of initialized neurons. Top middle: candidate seed pixels (small red dots) for initializing neurons on top of PNR image. The …

We choose two example ROIs to illustrate how CNMF-E removes the background contamination and demixes nearby neural signals for accurate initialization of neurons’ shapes and activity. In the first example, we choose a well-isolated neuron (green box, Figure 3A+B ). We select three pixels located in the center, the periphery, and the outside of the neuron and show the corresponding fluorescence traces in both the raw data and the spatially filtered data ( Original Cheap Online Buy Cheap Fashion Style FOURLESFRCT Vogue Shoes Thick Bottom Wedge Heel Summer Discount Shop rxSuQag8
). The raw traces are noisy and highly correlated, but the filtered traces show relatively clean neural signals. This is because spatial filtering reduces the shared background activity and the remaining neural signals dominate the filtered data. Similarly, Summer Special New Product for Girls wear Huge Range Of Genuine Discount Low Price Fee Shipping The Cheapest Online 4AFG4yUWb
is an example showing how CNMF-E demixes two overlapping neurons. The filtered traces in the centers of the two neurons still preserve their own temporal activity.

After initializing the neurons’ traces using the spatially filtered data, we initialize our estimate of their spatial footprints. Note that simply initializing these spatial footprints with the spatiallyfiltered data does not work well (data not shown), since the resulting shapes are distorted by the spatial filtering process. We found that it was more effective to initialize each spatial footprint by regressing the initial neuron traces onto the raw movie data (see Materialsandmethods for details). The initial values already match the simulated ground truth with fairly high fidelity ( monroo gray and black irregular slim fit knitting dress For Sale Online UW16J
). In this simulated data, CNMF-E successfully identified all 200 neurons and initialized their spatial and temporal components ( New Lightweight Running Mesh Breathable Sport Shoes Women Fashion Style Buy Cheap 2018 New Sale Marketable Buy Cheap 2018 Newest For Cheap Cheap Online hS44ZT
). We then evaluate the quality of initialization using all neurons’ spatial and temporal similarities with their counterparts in the ground truth data. Figure 3G shows that all initialized neurons have high similarities with the truth, indicating a good recovery and demixing of all neuron sources.

Thresholds on the minimum local correlation and the minimum peak-to-noise ratio (PNR) for detecting seed pixels are necessary for defining the initial spatial components. To quantify the sensitivity of choosing these two thresholds, we plot the local correlations and the PNRs of all pixels chosen as the local maxima within an area of $\frac{l}{4}×\frac{l}{4}$ , where $l$ is the diameter of a typical neuron, in the correlation image or the PNR image ( Stock Monogram Chiffon Kids Unicorn Dress Good Selling Online Discount Low Shipping Fee bHlFQFOy
). Pixels are classified into two classes according to their locations relative to the closest neurons: neurons’ central areas and outside areas (see Materials andmethodsfor full details). It is clear that the two classes are linearly well separated and the thresholds can be chosen within a broad range of values ( Figure 3H ), indicating that the algorithm is robust with respect to these threshold parameters here. In lowerSNR settings, these boundaries may be less clear, and an incremental approach (in which we choose the highest-SNR neurons first, then estimate the background and examine the residual to select the lowest-SNR cells) may be preferred; this incremental approach is discussed in more depth in the Materials andmethodssection.

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