pattern-machine
Pattern-discovery engine - long-running personal research on spiking-NN pattern formation.
How do structural patterns emerge from raw temporal sensory streams when the only available signal is local - no labels, no backprop through time? Pattern Machine framed this as a spiking-neural-network architecture with local Hebbian-style learning, asking whether repeated temporal regularities could self-organize into reusable spatial primitives. A multi-year research thread, paused after two distinct iterations.
The 2022 iteration built a full patternmachine Python package with layers, patch and pattern grids, signal-grid sets, local learning, and pattern-similarity primitives, alongside a documented experiment series running from single-layer regression through 1D bouncing-ball dynamics, temporal-to-spatial mapping, high-dimensional multi-patch patterns, and pattern hallucination. Each experiment left behind animated GIFs, pickled trained patterns, and reproducible drivers - the thinking is auditable. The 2023 rewrite (under the i3AI org) collapsed that into a smaller, cleaner computational-graph core with explicit operations modules and a LIF-with-adaptive-threshold demonstration on PyTorch. Neither iteration produced a definitive answer to the underlying question, but the experimental artifacts show what worked and what didn't.