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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 weather is nice today." |
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- text: "I feel curious about the stars." |
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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 is a biologically plausible, spiking neural agent combining vision, language, and reinforcement learning in a single architecture. Inspired by human neurobiology, it implements eight 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**: 576 |
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- **Neuron Types**: Interneurons, Excitatory, Inhibitory, Cholinergic, Dopaminergic, Serotonergic, Feedback, Plastic |
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- **Core Modules**: |
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- `SharedEncoder`: Multidimensional feature compressor |
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- `CNNVision`: Convolutional module for visual inputs |
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- `GRULanguage`: Recurrent module for sentence understanding |
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- `ReplayMemory` + `Curiosity` + `Entropy`: RL exploration engine |
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- `Task Heads`: Classifiers + Reinforcement actor-critic |
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--- |
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## 🧠 Features |
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- 🪐 Multi-modal input support (Images, Language, Environment signals) |
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- 🔁 Hebbian + gradient learning |
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- ⚡ Spiking simulation for dynamic activity |
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- 🧠 Biologically-inspired synaptic dynamics |
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- 🧬 Curriculum learning and memory consolidation |
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- 🔍 Fully modular: plug-and-play layers |
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--- |
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## 📊 Performance Summary |
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| Task | Dataset | Metric | Result | |
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|-----------------------|----------------------|-------------------|--------| |
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| Digit Recognition | MNIST | Accuracy | 98% | |
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| Sentiment Analysis | IMDb | Accuracy | 91% | |
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| Exploration Task | Synthetic GridWorld | 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 Grid Environment`: Exploration and navigation |
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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 | For brain-inspired network modeling | |
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| Cognitive Simulation | Test artificial memory, attention, curiosity | |
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| Multi-task Agent Prototyping| For language + vision + decision-making models | |
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| Educational Tool | Learn principles of bio-AI, spiking neurons, and RL | |
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--- |
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## ⚠️ Limitations |
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- Currently trained on small-scale datasets |
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- Needs GPU/TPU for efficient inference |
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- Cognitive feedback not yet implemented for all pathways |
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- Limited real-world generalization until scaled |
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--- |
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## 📂 Repository Structure |
