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