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license: apache-2.0
tags:
  - spiking-neural-network
  - neuromorphic
  - loihi
  - temporal-classification
  - audio
datasets:
  - zenke-lab/spiking-heidelberg-digits
metrics:
  - accuracy
model-index:
  - name: Catalyst SHD SNN
    results:
      - task:
          type: audio-classification
          name: Spoken Digit Recognition
        dataset:
          name: Spiking Heidelberg Digits (SHD)
          type: zenke-lab/spiking-heidelberg-digits
        metrics:
          - name: Test Accuracy (float32)
            type: accuracy
            value: 85.9
          - name: Test Accuracy (int16 quantized)
            type: accuracy
            value: 85.4

Catalyst SHD Spiking Neural Network

A recurrent spiking neural network trained on the Spiking Heidelberg Digits (SHD) benchmark, achieving 85.9% test accuracy (float) / 85.4% quantized (int16).

Trained using the Catalyst Neuromorphic SDK (neurocore) with surrogate gradient descent, and deployable directly to Loihi-compatible neuromorphic hardware via the Catalyst Cloud API.

Model Details

Parameter	Value
Architecture	Recurrent LIF (Leaky Integrate-and-Fire)
Input neurons	700 (cochlea spike channels)
Hidden neurons	512 (recurrent)
Output classes	20 (digits 0-9, German + English)
Total parameters	~1.49M
Time bins	250 (dt = 4ms, 1s duration)

Training Configuration

Hyperparameter	Value
Epochs	200
Batch size	128
Optimizer	AdamW
Learning rate	1e-3 (cosine annealing)
Weight decay	1e-4
Dropout	0.3
Gradient clipping	1.0 norm
Surrogate gradient	Fast sigmoid (scale=25.0)
Membrane decay (hidden)	0.95 (learnable)
Membrane decay (output)	0.9 (learnable)
Threshold	1.0
Reset mechanism	Hard reset: v = v * (1 - spike)

Hardware Deployment

The model is quantized to int16 for direct deployment on Catalyst neuromorphic hardware (FPGA) and the Catalyst Cloud API:

Threshold: 1000 (int16)
Decay mapping: decay_v = round(beta * 4096) (CUBA neurons)
Quantization accuracy loss: only 0.4% (85.9% -> 85.4%)

Benchmark Context

Method	SHD Accuracy
Cramer et al. (2020)	83.2%
Zenke & Vogels (2021)	83.4%
Catalyst (this model)	85.9%

Checkpoint Format

PyTorch .pt file containing:

{
    'epoch': int,
    'model_state_dict': OrderedDict,  # PyTorch model weights
    'test_acc': 0.859,
    'args': dict  # Complete training arguments
}

Usage

import torch

checkpoint = torch.load("shd_model.pt", map_location="cpu")
print(f"Test accuracy: {checkpoint['test_acc']:.1%}")

# Load weights into your model
model.load_state_dict(checkpoint['model_state_dict'])

Or use the Catalyst Cloud API:

pip install catalyst-cloud

from catalyst_cloud import CatalystClient
client = CatalystClient(api_key="your-key")
result = client.simulate(network_config={...})

Citation

If you use this model, please cite:

@misc{shulayevbarnes2026catalyst,
    title={Catalyst N1: An Open-Design Neuromorphic Processor with Full Loihi Parity},
    author={Shulayev Barnes, Henry},
    year={2026},
    publisher={Zenodo}
}

Catalyst-Neuromorphic
/

shd-snn-benchmark