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Spike-stream feature extraction for spiking neural systems.
Version: 0.1.0 | Julia: 1.9–1.12 | License: MIT OR Apache-2.0
SpikeStream.jl extracts statistics and SNN-friendly feature vectors from spike-event time streams. Kinetic / continuous signal dynamics (Hurst, Hawkes, GBM surprise, entropy, volatility) live in the Rust sibling kinetic-signals — not here.
| Function | Role |
|---|---|
spike_count |
Count spikes (optional time window) |
spike_density |
Spikes per unit time |
isi_stats |
Inter-spike interval mean/std/min/max/cv |
detect_bursts |
Contiguous short-ISI runs |
windowed_spike_features |
Rolling windows of features |
normalized_feature_vector |
Length-4 vector in [0, 1]
|
| Page | Description |
|---|---|
| Getting Started | Install and quick examples |
| Project Structure | Layout and deps |
| Architecture | Boundary vs kinetic-signals |
| API Reference | Function contracts |
| Output Ranges | Documented invariants |
| Window Semantics | Inclusive vs half-open |
| Fixtures | Frozen JSON tests (LIM-41) |
| Benchmarks | BenchmarkTools suite |
| Testing | Unit + fixture tests |
| CI and Quality | GHA matrix, format, Codecov |
| Ecosystem | Sibling packages |
| Glossary | Terms |
using SpikeStream
spike_times = [0.001, 0.005, 0.009, 0.040, 0.042, 0.044, 0.090]
spike_count(spike_times)
spike_density(spike_times; t_start=0.0, t_end=0.1)
isi_stats(spike_times)
detect_bursts(spike_times; max_isi=0.004, min_spikes=3)
windowed_spike_features(spike_times; window_size=0.03, step=0.03)
normalized_feature_vector(spike_times; t_start=0.0, t_end=0.1, max_density=200.0)Dual-licensed under MIT or Apache-2.0 at your option.
Last updated: July 29, 2026
Updated by: Grok Build: Grok 4.5
Package tip reference: d2229e2 (main, through PR #25)