The open neuromorphic compute engine.
Author spiking neural networks in Python. Compile them to neuromorphic hardware.
Website · Docs · Discussions
What is it?
thrindex is the toolkit for spiking neural networks (SNNs). You author a model, train it, compile it to a .thx file, and run that file on the behavioral simulator.
It gives you a PyTorch-native authoring API for building and training SNNs with surrogate gradients, a deterministic Rust simulation engine for fast, reproducible model validation, and a compiler that seals the trained model into a .thx file.
import thrindex as thx
import thrindex.snn as snn
model = snn.Sequential(
snn.Dense(784, 1000),
snn.LIF(threshold=1.0, tau_mem=5.0),
snn.Dense(1000, 10),
snn.LIF(threshold=1.0, tau_mem=5.0),
)
# train with surrogate gradients — standard PyTorch loop
loss = thx.train.rate_loss(model(encoded_input), labels)
loss.backward()
Install
pip install thrindex
Requires Python 3.11+ and PyTorch 2.0+.
Quickstart
import torch
import thrindex as thx
import thrindex.snn as snn
from thrindex.encoders import rate
from thrindex.train import rate_loss
# Build a two-layer LIF network
model = snn.Sequential(
snn.Dense(784, 1000),
snn.LIF(threshold=1.0, tau_mem=5.0, reset="subtract"),
snn.Dense(1000, 10),
snn.LIF(threshold=1.0, tau_mem=5.0, reset="subtract"),
)
# Rate-encode your input (explicit generator — no global RNG state)
gen = torch.Generator()
gen.manual_seed(42)
spikes = rate(x_batch, T=25, generator=gen) # [T, batch, 784]
# Forward + loss
spk_out = model(spikes) # [T, batch, 10]
loss = rate_loss(spk_out, labels)
# Standard PyTorch backward
loss.backward()
optimizer.step()
Key features
| Feature | Detail |
|---|---|
| SNN layers | LIF, Sequential, Dense, Conv2d, Flatten. Dense(delay=k) holds the input k steps. The first Conv2d takes input_height and input_width. |
| Surrogate gradients | Fast-sigmoid, analytically verified backward pass |
| Spike encoders | Rate (Bernoulli), latency (time-to-first-spike), delta (change coding) |
| Deterministic numerics | Parameterizable fixed-point Q<INT, FRAC> types; round-half-even; bit-identical across platforms |
| Typed errors | Stable E#### error codes — every error tells you what happened, why, and how to fix it |
| Sealed artifact | thx.compile writes a .thx file. The simulator runs dense, convolution, flatten, LIF, and dense delays. |
| Traces | model.forward_recorded(x) returns the same spikes plus per-layer rasters and the pre-reset membrane |
| Datasets | thrindex.datasets: SHD, N-MNIST, DVS Gesture, CIFAR10-DVS. Install with pip install "thrindex[datasets]" |
LIF neuron
The LIF module follows the exact per-timestep update order:
(i) mem_t = α · mem_prev + x_t # leak + integrate
(ii) spk_t = H(mem_t − threshold) # fire (Heaviside; surrogate in backward)
(iii) mem_out = mem_t − spk_t · threshold # reset (subtract mode)
where α = exp(−dt / τ_mem) (exact exponential discretization, dt = 1 ms).
Canonical LIF parameters: threshold, tau_mem, tau_syn, reset. Synaptic delay is snn.Dense(..., delay=k), not a LIF argument.
Encoders
from thrindex.encoders import rate, latency, delta
# Rate: Bernoulli-sample — always pass an explicit generator
gen = torch.Generator(); gen.manual_seed(0)
spikes = rate(x, T=25, generator=gen) # [T, *x.shape]
# Latency: higher value spikes earlier — deterministic, no RNG
spikes = latency(x, T=25)
# Delta: fires on change above threshold — deterministic, no RNG
spikes = delta(x_sequence, T=25, threshold=0.1)
Stack
python/thrindex/ Python SDK — strictly typed, zero logic
crates/thrindex-py/ PyO3 bridge (maturin)
crates/thrindex-sim/ Behavioral simulator — deterministic, seeded, CPU-parallel (M2)
crates/thrindex-compiler/ Compiler: capture → validate → quantize (M3)
crates/thrindex-numerics/ Fixed-point core — zero dependencies, consumed bit-for-bit
Rust engines are MIT/Apache-2.0. The Python SDK wraps them with no logic of its own.
Status
Templates, public docs, CI snippet tests, the conformance suite, and the energy benchmark harness are shipped. The SDK authors, compiles, and simulates.
Issues and discussions are open.
See SECURITY.md for responsible disclosure.
License
Apache 2.0 — see LICENSE.
1 maintainer
Metadata
1 maintainer
Release files for thrindex 0.6.0
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