Intracellular Calcium Oscillation Model Parameters for LKCNN Classifier Training

Jaesung Choi published this dataset on figshare in 2026. It contains parameter values used for numerical simulations of an intracellular calcium oscillation model. The dataset includes 1000 synthetic trajectories per dynamical pattern across three distinct behavioral regions for training and testing an LKCNN classifier.

Use Cases

• Training a neural network classifier (LKCNN) based on synthetic trajectories of calcium oscillations.
• Analyzing the relationship between model parameters and dynamical behaviors (e.g., bursting, chaotic, quasiperiodic) based on the described regions.
• Benchmarking classification algorithms on synthetic time-series data based on the generated trajectories.
• Studying bifurcations and transitions in calcium signaling models based on the parameter sets across different regions.

Strengths

• Dataset is explicitly structured for machine learning, with 1000 trajectories per pattern for both training and testing.
• Data covers three distinct behavioral regions with multiple dynamical patterns, including chaotic and quasiperiodic states.
• License is CC-BY-4.0, permitting broad reuse with attribution.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small at 5.5 KB, indicating limited scope.

Provenance

Source

figshare

Collection Method

Numerical simulation of a biological model to generate synthetic data.

Time Range

The simulation time range is not specified.

Freshness

Last updated 2026-04-29 17:56:59; freshness should be verified.

Geography

The dataset has no geographic coverage; it is based on a theoretical model.