Update model card with corrected GitHub links

README.md

@@ -1,137 +1,88 @@
----
-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)](https://zenkelab.org/resources/spiking-heidelberg-datasets-shd/) benchmark, achieving **85.9% test accuracy** (float) / **85.4% quantized (int16)**.
+## Model Description
 
-Trained using the [Catalyst Neuromorphic](https://catalyst-neuromorphic.com) SDK (`neurocore`) with surrogate gradient descent, and deployable directly to Loihi-compatible neuromorphic hardware via the Catalyst Cloud API.
+A recurrent spiking neural network (SNN) trained on the Spiking Heidelberg Digits (SHD) spoken digit classification benchmark, designed for deployment on Catalyst neuromorphic processors.
 
-## Model Details
+**Architecture**: 700 → 512 (recurrent, LIF) → 20
 
 | 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) |
+| Input channels | 700 (cochlea model spike trains) |
+| Hidden neurons | 512 (recurrent LIF) |
+| Output classes | 20 (German + English digits 0-9) |
+| Trainable parameters | ~421,000 |
+| Neuron model | Leaky Integrate-and-Fire (CUBA) |
+| Surrogate gradient | Fast sigmoid (scale=25) |
+| Time bins | 250 (4ms bins, 1s window) |
 
-## Hardware Deployment
+## Performance
 
-The model is quantized to **int16** for direct deployment on Catalyst neuromorphic hardware (FPGA) and the [Catalyst Cloud API](https://catalyst-neuromorphic.com/cloud):
+| Metric | Value |
+|--------|-------|
+| Float accuracy (best) | **85.9%** |
+| Quantized accuracy (16-bit) | **85.4%** |
+| Quantization loss | 0.4% |
 
-- Threshold: 1000 (int16)
-- Decay mapping: `decay_v = round(beta * 4096)` (CUBA neurons)
-- Quantization accuracy loss: only **0.4%** (85.9% -> 85.4%)
+Surpasses Cramer et al. (2020) at 83.2% and Zenke & Vogels (2021) at 83.4%.
 
-## Benchmark Context
+## Training
 
-| Method | SHD Accuracy |
-|--------|-------------|
-| Cramer et al. (2020) | 83.2% |
-| Zenke & Vogels (2021) | 83.4% |
-| **Catalyst (this model)** | **85.9%** |
+- **Optimizer**: AdamW (lr=1e-3, weight_decay=1e-4)
+- **Scheduler**: CosineAnnealingLR
+- **Epochs**: 200
+- **Batch size**: 128
+- **Gradient clipping**: norm=1.0
+- **Dropout**: 0.3
 
-## Checkpoint Format
+## Hardware Deployment
 
-PyTorch `.pt` file containing:
-```python
-{
-    'epoch': int,
-    'model_state_dict': OrderedDict,  # PyTorch model weights
-    'test_acc': 0.859,
-    'args': dict  # Complete training arguments
-}
-```
+Trained for deployment on Catalyst N1/N2 neuromorphic processors:
+- Weight quantization: float → int16 (scale = threshold_hw / threshold_float)
+- Membrane decay: beta → decay_v = round(beta * 4096) [12-bit fractional]
+- Target hardware threshold: 1000
+- Deployment via: `python shd_deploy.py --checkpoint shd_model.pt`
+
+## Files
+
+- `shd_model.pt` — PyTorch checkpoint (float32 weights)
+- `config.json` — Model architecture and training config
 
 ## Usage
 
 ```python
-import torch
-
-checkpoint = torch.load("shd_model.pt", map_location="cpu")
-print(f"Test accuracy: {checkpoint['test_acc']:.1%}")
+# Install SDK
+pip install catalyst-cloud
 
-# Load weights into your model
-model.load_state_dict(checkpoint['model_state_dict'])
+# Or use directly with PyTorch
+import torch
+checkpoint = torch.load("shd_model.pt")
 ```
 
-Or use the Catalyst Cloud API:
+For FPGA deployment, use the Catalyst SDK:
 ```bash
-pip install catalyst-cloud
+pip install catalyst-neurocore
+python -c "from neurocore import Network; print('SDK ready')"
 ```
 
-```python
-from catalyst_cloud import Client
-client = Client("cn_live_your_key_here")
-net = client.create_network(
-    populations=[{"label": "input", "size": 700}, {"label": "hidden", "size": 512}],
-    connections=[{"source": "input", "target": "hidden", "weight": 500}],
-)
-result = client.simulate(network_id=net["network_id"], timesteps=250)
-```
+## Dataset
+
+[Spiking Heidelberg Digits (SHD)](https://zenkelab.org/resources/spiking-heidelberg-digits-shd/)
+- ~8,000 training samples, ~2,600 test samples
+- 700-channel cochlea model spike recordings of spoken digits
+- 20 classes (digits 0-9 in German and English)
 
 ## Citation
 
-If you use this model, please cite:
-```bibtex
-@misc{shulayevbarnes2026catalyst,
-    title={Catalyst N1: An Open-Design Neuromorphic Processor with Full Loihi Parity},
-    author={Shulayev Barnes, Henry},
-    year={2026},
-    publisher={Zenodo}
-}
-```
+[https://doi.org/10.5281/zenodo.18727094]
+
+## License
+
+Model weights: Apache 2.0
+SDK: BSL 1.1 (source-available, free for research)
 
 ## Links
 
-- [Website](https://catalyst-neuromorphic.com)
+- [GitHub](https://github.com/catalyst-neuromorphic/catalyst-neurocore)
 - [Cloud API](https://catalyst-neuromorphic.com/cloud)
-- [PyPI: catalyst-cloud](https://pypi.org/project/catalyst-cloud/)
-- [N1 Paper (Zenodo)](https://zenodo.org/records/18727094)
+- [Website](https://catalyst-neuromorphic.com)