Block Reference

SynapChart ships with a built-in library of blocks organised into six categories. Click a category to see every block, its ports, and its parameters.

Category	Description
Data I/O	Load and save data files — NumPy arrays, CSV, and CRCNS HC-11 session files
LFP / EEG	Continuous signal processing: bandpass filtering, phase extraction, PSD, oscillation detection, and channel selection
Spikes	Spike train analysis: binning, rate maps, tuning curves, place field detection, phase precession, and Bayesian decoding
Visualization	Matplotlib-based plots: rasters, PSDs, tuning curves, phase precession scatters, decoded posteriors, and theta sequence heatmaps
Custom Blocks	User-defined blocks generated by the block wizard or written directly in Python
Flow Control	Batch execution drivers and utilities: dataset iterator, result collector, and string constant

Port types

Blocks communicate through typed ports. The most common types are:

Type	Carries
NeuroData[raw_signal]	Generic continuous signal (samples × channels)
NeuroData[lfp]	LFP signal — same shape, typed for downstream validation
NeuroData[spike_times]	1-D array of spike timestamps in seconds
NeuroData[multi_spike_times]	Multi-cell spike times with per-spike cell IDs in metadata
NeuroData[spike_matrix]	Binned spike counts (n_units × n_time_bins)
NeuroData[position]	Linearised 1-D position with timestamps
NeuroData[tuning_curve]	Single-cell firing rate vs position
NeuroData[tuning_curves_population]	Population rate maps (n_cells × n_bins)
NeuroData[place_fields]	Per-cell place field boundaries (in metadata)
NeuroData[phase_precession]	Per-cell × per-field r and slope values
NeuroData[decoded]	Posterior probability matrix (n_pos_bins × n_time_bins)
NeuroData[theta_cycles]	(N_cycles × 2) peak-to-peak cycle boundaries
NeuroData[theta_sequence]	Averaged theta sequence matrix
NeuroData[epochs]	Epoch boundaries (start/stop times)
str	Plain string — used for file paths and constants