Rate coding (spikegen.rate) at gain 1 and a lower gain
Latency coding (spikegen.latency) with tau, threshold, linear,
normalize, and clip variants (tutorial 2.3)
Delta modulation (spikegen.delta) with on/off spikes (tutorial 2.4)
Random spike generation from scratch via spikegen.rate_conv (tutorial 3)
Matplotlib exports (MP4s, GIFs, rasters, reconstructions) into build/
Training: a fully-connected LIF spiking network with a
surrogate-gradient cross-entropy loss, streaming live loss and both
batch + held-out accuracy
Datasets: train on MNIST, Fashion-MNIST, KMNIST, QMNIST, USPS,
EMNIST digits/letters, or (grayscaled) CIFAR-10, all normalised to
28x28 so one architecture fits all
Model management: save checkpoints, list/load/delete them, and
continue training an already-trained model
Browser interface: a dark-themed grid dashboard that streams encoded
spike data over WebSockets and renders plots live in the client, with a
training panel (dataset picker, network config, live charts, model
management, predictions)
Compute selection: a CPU/GPU device dropdown (GPU by default, with
automatic CPU fallback) and a live CPU-RAM / VRAM resource monitor
Interpreter spine (Phase 1): topology presets, a neuron registry, NIR
export, an independent NIR interpreter, and numerical drift validation
(see below)
Dual-mode introspection (Phase 2): an educational mode that records
per-step U[t]/I[t]/S[t], trajectory metrics, encoding/decoding
reports, surrogate-gradient curves, a neuron comparison lab, and a
production-mode benchmark harness (see below)
Unified dashboard (Phase 3): an Educational/Production mode toggle,
topology/neuron/surrogate pickers, neuron-state trajectory and NIR graph
viewers, a drift-validation panel, trajectory-metrics/encoding/surrogate/
benchmark analysis panels, and seven guided walkthroughs (see below)
Event datasets (Phase 4): N-MNIST, DVS128 Gesture, CIFAR10-DVS, and
Spiking Speech Commands through Tonic, with a modality-aware dataset
picker, an event-to-spike bridge, and polarity-aware rasters (see below)
Targets and interoperability (Phase 5): a deployment-target registry
with an honest capability matrix, per-target deployment reports, external
NIR import/export, and a round-trip fidelity guarantee (see below)
Production workflows (Phase 6): a reproducibility manifest and config
hash, a searchable checkpoint registry with metadata diffing, opt-in
training scale-ups (AMP, gradient checkpointing, truncated BPTT,
multi-GPU), a stored benchmark suite with regression gating, opt-in JSON
logging and a metrics snapshot, packaged console scripts, and Docker
CPU/GPU profiles (see below)
Model hub (WS-A): a bundled curated catalog (10 verified entries across
five frameworks) plus optional live Hugging Face access, an isolated
downloader with progress/cancel and checksum verification, and an
inspect โ compat โ promote import funnel, surfaced through spikeforge-hub, six
WebSocket actions, and the HubPanel browser (see below)
Backend execution (WS-B): a substitution executor that applies a
target's declared rewrites with a report and drift check, and executable
reference, norse, and lava_loihi2 backends behind one compile_run
entry point, surfaced through deploy/rewrite/run (see below)
Sequence primitives (WS-C): per-stage heterogeneous neurons, ten new
stage kinds with explicit NIR contracts, and the sequence_mlp/sequence_attn
demonstration presets (see below)
Event runtime and energy (WS-D): a sparse/event-driven runner with a
dense-parity check, SOP/MAC/AC counting, and a spikeforge-energy report that maps
op counts to a declared per-target cost table (see below)
Operational maturity (WS-E): opt-in persisted metrics, optional
TensorBoard/W&B tracking sinks, determinism tooling, and a generated docs
site (see below)
Interop fold-ins (WS-F): event-dataset training, an ONNX bridge,
nirtorch extraction of third-party PyTorch modules, weight-level
quantization (whose drift check can now also simulate activation/membrane
rounding, see Implications and boundaries ยง3),
non-square sensor geometry, and per-step hidden-layer animation (see below)