| --- |
| 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. |
| . |
