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+# 🧠 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!