README.md

# 🧠 PulseNet Labs

Welcome to **PulseNet Labs**! We are an AI research initiative dedicated to advancing **Neuromorphic Computing** and **Spiking Neural Networks (SNN)** for the next generation of energy-efficient artificial intelligence.

## 🔬 Our Mission
As deep learning models scale exponentially in size and energy consumption, our mission is to pioneer biologically plausible, hardware-friendly AI architectures. We focus on bridging the gap between theoretical neuroscience and practical machine learning applications.

Our core research focuses on:
- **Spiking Neural Networks (SNNs)**: Developing event-driven architectures utilizing Leaky-Integrate-and-Fire (LIF) neuron models.
- **Energy-Efficient NLP**: Bringing neuromorphic efficiency to Natural Language Processing tasks, such as semantic embeddings and attention mechanisms.
- **Biologically Plausible Learning**: Exploring Contrastive Hebbian Learning, STDP (Spike-Timing-Dependent Plasticity), and Knowledge Distillation techniques tailored for SNNs.

## 🚀 Key Research & Projects
Our flagship development includes the **Spiking Sentence Embedder**, the first of its kind to introduce **Sparse Coincidence-Based Semantic Attention** integrated with temporal dynamics. 

By utilizing binary spike events rather than dense continuous values, our models drastically reduce theoretical energy consumption without sacrificing mathematical precision and zero-shot generalization capabilities.

- 📚 **Read our Publications**: [10.5281/zenodo.20739462](https://doi.org/10.5281/zenodo.20739462)
- ⚙️ **Core Tech Stack**: Rust, PyTorch, Hugging Face `transformers`

## 🤝 Open Science & Collaboration
We strongly believe in Open Science. All of our natively exported PyTorch SNN weights, custom architectures, and tokenizers are publicly hosted here to facilitate further research in neuromorphic engineering. 

We welcome collaborations from fellow researchers, cognitive scientists, and AI engineers. Let's build a greener, brain-inspired future for AI!