Update README.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 sure this model architecture will revolutionised 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 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.

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:
- SharedEncoder: Multidimensional feature compressor
- CNNVision: Convolutional module for visual inputs
- GRULanguage: Recurrent module for sentence understanding
- ReplayMemory + Curiosity + Entropy: RL exploration engine
- Task Heads: Classifiers + Reinforcement actor-critic

🧠 Features

🪐 Multi-modal input support (Images, Language, Environment signals)
🔁 Hebbian + gradient learning
⚡ Spiking simulation for dynamic activity
🧠 Biologically-inspired synaptic dynamics
🧬 Curriculum learning and memory consolidation
🔍 Fully modular: plug-and-play layers

📊 Performance Summary

The following metrics are preliminary and not benchmarked on official test sets. They reflect internal experiments and are subject to change.

Task	Dataset	Metric	Result
Digit Recognition	MNIST	Accuracy	Not yet validated
Sentiment Analysis	IMDb	Accuracy	Not yet validated
Exploration Task	Synthetic GridWorld	Cumulative Reward	Learning observed qualitatively

💻 Training Data

MNIST: Handwritten digit classification
IMDb: Sentiment classification from text
Synthetic Grid Environment: Exploration and navigation

🧪 Intended Uses

Use Case	Description
Neuroscience AI Research	For brain-inspired network modeling
Cognitive Simulation	Test artificial memory, attention, curiosity
Multi-task Agent Prototyping	For language + vision + decision-making models
Educational Tool	Learn principles of bio-AI, spiking neurons, and RL

⚠️ Limitations

Currently trained on small-scale datasets
Needs GPU/TPU for efficient inference
Cognitive feedback not yet implemented for all pathways
Limited real-world generalization until scaled

Almusawee
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ModularBrainAgent