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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
-
----
-
-## 📂 Repository Structure