CaliAli parameter settings

The CaliAli pipeline uses a structured approach to initialize, configure, and manage parameters across different processing stages. The function CaliAli_parameters() acts as the central hub for handling parameters, ensuring modularity and flexibility.

📌 Key Features of CaliAli Parameter Management

1️⃣ Modular Parameter Handling

CaliAli organizes its processing pipeline into separate submodules, each with its own set of parameters:

Downsampling
Preprocessing (detrending, background processing)
Motion Correction (rigid and non-rigid)
Inter-Session Alignment
CNMF-E (Calcium signal extraction and demixing)

2️⃣ Flexible Input Handling

CaliAli parameters can be set in two ways:

Default Initialization:
If no inputs are provided, default parameters are loaded:

opt = CaliAli_parameters();

Custom Parameter Structure:
Users can provide an existing structure to modify specific parameters:

opt = CaliAli_parameters(existing_opt);

The function CaliAli_demo_parameters() demonstrates how to create this structure. The recommended approach for analyzing your own data is to modify and duplicate this code to suit your needs: open(CaliAli_demo_parameters());

Adjusting CaliAli Parameters.

CaliAli requires setting 33 parameters. However, in practice you only need to strictly focus on three:

Frame rate: sf
Neuron filtering size: gSig which correspond to 1/5 of the average neuron size in pixels.
System Memory:

Parameter Name	Default Value	Description	How to Choose
`memory_size_to_use`	`total_system_memory_GB`	Total available memory for computation	Adjust based on available RAM.
`memory_size_per_patch`	`16`	Memory allocated per patch	Adjust based on available RAM and number of patches.

For advanced users, a detailed description of other parameters and methods for setting them can be found in: Parameter Index

When you run CaliAli_options=CaliAli_demo_parameters(); you will get a structure as follow:

◼ CaliAli_options
├─ downsampling
│  ├─ BVsize
│  │  Value: [1.5 2.25]
│  ├─ file_extension
│  │  Value: 'avi'
│  └─ ...
├─ preprocessing
│  ├─ dendrite_filter_size
│  │  Value: [0.5 0.6 0.7 0.8]
│  ├─ dendrite_theta
│  │  Value: 30
│  └─ ...
└─ ...