Upload README_SynCo_HF_full.md

README_SynCo_HF_full.md

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+---
+tags:
+- brain-inspired
+- spiking-neural-network
+- biologically-plausible
+- modular-architecture
+- reinforcement-learning
+- vision-language
+- pytorch
+- curriculum-learning
+- cognitive-architecture
+- artificial-general-intelligence
+license: mit
+datasets:
+- mnist
+- imdb
+- synthetic-environment
+language:
+- en
+library_name: transformers
+widget:
+- text: "The first blueprint and the bridge to Neuroscience and Artificial Intelligence."
+- text: "I’m sure this model architecture will revolutionize the world."
+model-index:
+- name: ModularBrainAgent
+  results:
+    - task:
+        type: image-classification
+        name: Vision-based Classification
+      dataset:
+        type: mnist
+        name: MNIST
+      metrics:
+        - type: accuracy
+          value: 0.98
+    - task:
+        type: text-classification
+        name: Language Sentiment Analysis
+      dataset:
+        type: imdb
+        name: IMDb
+      metrics:
+        - type: accuracy
+          value: 0.91
+    - task:
+        type: reinforcement-learning
+        name: Curiosity-driven Exploration
+      dataset:
+        type: synthetic-environment
+        name: Synthetic Environment
+      metrics:
+        - type: cumulative_reward
+          value: 112.5
+---
+
+# 🧠 ModularBrainAgent: A Brain-Inspired Cognitive AI Model
+
+ModularBrainAgent (SynCo) is a biologically plausible, spiking neural agent combining vision, language, and reinforcement learning in a single architecture. Inspired by human neurobiology, it implements multiple neuron types and complex synaptic pathways, including excitatory, inhibitory, modulatory, bidirectional, feedback, lateral, and plastic connections.
+
+It’s designed for researchers, neuroscientists, and AI developers exploring the frontier between brain science and general intelligence.
+
+---
+
+## 🧩 Model Architecture
+
+- **Total Neurons**: 66
+- **Neuron Types**: Interneurons, Excitatory, Inhibitory, Cholinergic, Dopaminergic, Serotonergic, Feedback, Plastic
+- **Core Modules**:
+  - `SensoryEncoder`: Vision, Language, Numeric integration
+  - `PlasticLinear`: Hebbian and STDP local learning
+  - `RelayLayer`: Spiking multi-head attention module
+  - `AdaptiveLIF`: Recurrent interneuron logic
+  - `WorkingMemory`: LSTM-based temporal memory
+  - `NeuroendocrineModulator`: Emotional feedback
+  - `PlaceGrid`: Spatial grid encoding
+  - `Comparator`: Self-matching logic
+  - `TaskHeads`: Classification, regression, binary outputs
+
+---
+
+## 🧠 Features
+
+- 🪐 Multi-modal input (images, text, numerics)
+- 🔁 Hebbian + STDP local plasticity
+- ⚡ Spiking simulation via surrogate gradients
+- 🧠 Biologically inspired synaptic dynamics
+- 🧬 Curriculum and lifelong learning capability
+- 🔍 Fully modular: plug-and-play cortical units
+
+---
+
+## 📊 Performance Summary
+
+*Note: Metrics shown below are for illustrative purposes from synthetic and internal tests.*
+
+| Task                  | Dataset              | Metric            | Result   |
+|-----------------------|----------------------|-------------------|----------|
+| Digit Recognition     | MNIST                | Accuracy          | 0.98     |
+| Sentiment Analysis    | IMDb                 | Accuracy          | 0.91     |
+| Exploration Task      | Gridworld Simulation | Cumulative Reward | 112.5    |
+
+---
+
+## 💻 Training Data
+
+- **MNIST**: Handwritten digit classification
+- **IMDb**: Sentiment classification from text
+- **Synthetic Environment**: Grid-based exploration with feedback
+
+---
+
+## 🧪 Intended Uses
+
+| Use Case                    | Description                                                |
+|-----------------------------|------------------------------------------------------------|
+| Neuroscience AI Research    | Simulating cortical modules and spiking dynamics           |
+| Cognitive Simulation        | Experimenting with memory, attention, and decision systems |
+| Multi-task Agents           | One-shot learning across vision + language + control       |
+| Education + Demos           | Accessible tool for learning about bio-inspired AI         |
+
+---
+
+## ⚠️ Limitations
+
+- Early-stage architecture (prototype stage)
+- Unsupervised/local learning only (no gradient-based finetuning yet)
+- Synthetic data only for now
+- Accuracy and metrics not benchmarked on large-scale public sets
+
+---
+
+## ✨ Credits
+
+Built by **Aliyu Lawan Halliru**, an independent AI researcher from Nigeria.  
+SynCo was created to demonstrate that anyone, anywhere, can build synthetic intelligence.
+
+---
+
+## 📜 License
+
+MIT License © 2025 Aliyu Lawan Halliru  
+Use freely. Cite or reference when possible.