GenesisAeon Entropy Atlas Package 26 — Neuromorphic SNN Hardware Bridge (Intel Loihi 2)
Project description
spiking-aeon — Package 26
Neuromorphic SNN Hardware Bridge (Intel Loihi 2) — GenesisAeon Entropy Atlas Package 26
Bridges the GenesisAeon CREP weight system to physical Spiking Neural Network hardware (Intel Loihi 2). CREP tensor components C, R, E, P are translated into LIF neuron parameters. Software simulation runs without hardware via the pure-Python Brian2-compatible backend.
Calibrated against NeuEdge (arXiv:2602.02439, 2026): 847 GOp/s/W · 2.3 ms latency · 89% core utilisation · 312x energy improvement over GPU.
SNN UTAC Model
| Parameter | Value | Meaning |
|---|---|---|
| K | 1000 Hz | Max sustainable Loihi 2 firing rate |
| H* | 320 Hz | Critical firing rate for task performance |
| eta | 0.32 | H*/K — SNN operating setpoint |
| Gamma | ~0.150 | arctanh(0.32) / 2.2 — SNN hardware criticality index |
CREP -> LIF Parameter Mapping
| CREP | LIF Parameter | High value effect |
|---|---|---|
| C | tau_m (membrane time constant) | Longer integration window |
| R | I_noise amplitude | Optimal stochastic resonance |
| E | Synaptic weight W | Stronger collective coupling |
| P | tau_ref (refractory period) | Richer temporal coding |
Install
pip install spiking-aeon
# or
uv add spiking-aeon
Usage
from spiking_aeon.system import SpikingAeon
sim = SpikingAeon(n_neurons=1000, seed=42)
# Software simulation (no hardware required)
result = sim.run_cycle(duration_ms=1000.0)
print(f"Mean firing rate: {result['mean_rate_hz']:.1f} Hz")
# CREP criticality tensor
crep = sim.get_crep_state()
print(f"Gamma = {crep['Gamma']:.4f} (reference: {crep['Gamma_ref']})")
# UTAC state
utac = sim.get_utac_state()
print(f"H = {utac['H']:.4f}, below threshold: {utac['below_threshold']}")
# Zenodo record
record = sim.to_zenodo_record()
Stochastic Resonance
sr = sim.optimise_stochastic_resonance(n_steps=20)
print(f"Optimal noise D_res = {sr['D_res']:.3f} (min CV = {sr['min_CV']:.3f})")
Deploy to Loihi 2
# Requires INRC access + pip install lava-nc
# https://www.intel.com/content/www/us/en/research/neuromorphic-computing.html
sim.deploy_to_loihi()
Package Structure
src/spiking_aeon/
├── __init__.py # version 0.1.0, gamma=0.150, package_number=26
├── constants.py # Loihi 2 specs, LIF params, UTAC constants
├── lif_neuron.py # LIF neuron with CREP-modulated firing threshold
├── stdp_plasticity.py # Spike-Timing Dependent Plasticity
├── stochastic_resonance.py # Optimal noise sweep (Ferreira 2025)
├── crep_to_weights.py # CREP {C,R,E,P} -> {tau_m, noise, weight, tau_ref}
├── crep_snn.py # SNN CREP tensor, Gamma_ref ~= 0.150
├── brian2_backend.py # Pure-Python LIF network simulation
├── loihi_adapter.py # Lava SDK interface (requires INRC)
├── system.py # Diamond interface (run_cycle, get_crep_state, ...)
└── benchmark.py # NeuEdge 2026 benchmark suite
CREP Criticality Spectrum Position
Domain Pkg Gamma eta
--------------------------- ---- ------- ----
Qubit decoherence (T2) P24 0.050 ~5%
Apoptosis (ATP threshold) P25 0.090 20%
Amazon Rainforest P19 0.116 12%
SNN firing (Loihi 2) P26 0.150 32% <- spiking-aeon
Seismic b=1.5 (GR law) P23 0.200 40%
AMOC / Neural criticality P20 0.251 50%
BTW Sandpile (SOC) P22 0.296 58%
Role in the GenesisAeon Ecosystem
spiking-aeon is Package 26 of the GenesisAeon Entropy Atlas, in the
neuromorphic hardware domain. It bridges the GenesisAeon CREP
criticality framework to physical Spiking Neural Network hardware (Intel
Loihi 2), positioning SNN firing-rate criticality (Γ ≈ 0.150) on the
ecosystem's broader CREP Criticality Spectrum alongside other domains such
as qubit decoherence, apoptosis, and self-organized criticality.
License
This repository is dual-licensed:
- Code (
src/,tests/) — GNU GPLv3-or-later - Documentation (README,
docs/,RELEASE_GUIDE.md,CONTRIBUTING.md, and other prose) — Creative Commons Attribution 4.0 International (CC BY 4.0)
Citation
DOI will be updated automatically on each GitHub Release once Zenodo–GitHub integration is enabled for this repo.
@software{romer2026spiking_aeon,
author = {Romer, Johann},
title = {spiking-aeon: Neuromorphic SNN Hardware Bridge (Package 26)},
year = {2026},
version = {1.0.0},
publisher = {Zenodo},
doi = {10.5281/zenodo.19645351},
}
@misc{neuredge2026,
title = {NeuEdge: Energy-Efficient Neuromorphic Edge Computing},
year = {2026},
eprint = {2602.02439},
note = {847 GOp/s/W on Loihi 2},
}
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file spiking_aeon-1.0.0.tar.gz.
File metadata
- Download URL: spiking_aeon-1.0.0.tar.gz
- Upload date:
- Size: 92.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: uv/0.9.17 {"installer":{"name":"uv","version":"0.9.17","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":null,"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
1f7857184a7a9bea799727f587621f82dc2e4b6e9bbdc1d3c9176d435dfc8ad5
|
|
| MD5 |
4104932807eae5468f5880c5b1c80093
|
|
| BLAKE2b-256 |
88a7e1a9dd8c80267c5cec7c494c11feb632a2f2b0ceca8e2566cefc3b070c87
|
File details
Details for the file spiking_aeon-1.0.0-py3-none-any.whl.
File metadata
- Download URL: spiking_aeon-1.0.0-py3-none-any.whl
- Upload date:
- Size: 20.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: uv/0.9.17 {"installer":{"name":"uv","version":"0.9.17","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":null,"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
e4ac6d6142085b14326f9dc7e7cb3c83650f06073e3fe9b88723ed9eb6e82633
|
|
| MD5 |
4251478e94d8c7c41503613267b1e2ad
|
|
| BLAKE2b-256 |
6497b60ce1efce4f7dff9f2c861c6551848993a4da15b25c870c0f55a952ee8f
|