Update README.md

README.md

@@ -2,27 +2,40 @@
 tags:
 - brain-inspired
 - spiking-neural-network
+- biologically-plausible
+- modular-architecture
 - reinforcement-learning
 - vision-language
 - pytorch
-- modular-architecture
-- biologically-plausible
+- curriculum-learning
+- cognitive-architecture
+- artificial-general-intelligence
 license: mit
 datasets:
 - mnist
 - imdb
-- custom-dataset
+- synthetic-environment
 language:
 - en
 library_name: transformers
 widget:
-- text: "the blueprint and bridge to neuroscience and artificial Intelligence"
+- text: "The weather is nice today."
+- text: "I feel curious about the stars."
 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: Text Classification
+      name: Language Sentiment Analysis
     dataset:
       type: imdb
       name: IMDb
@@ -30,12 +43,84 @@ model-index:
       - type: accuracy
         value: 0.91
   - task:
-      type: image-classification
-      name: Image Classification
+      type: reinforcement-learning
+      name: Curiosity-driven Exploration
     dataset:
-      type: mnist
-      name: MNIST
+      type: synthetic-environment
+      name: Synthetic Environment
     metrics:
-      - type: accuracy
-        value: 0.98
----
+      - 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**: 576
+- **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
+
+| Task                  | Dataset              | Metric            | Result |
+|-----------------------|----------------------|-------------------|--------|
+| Digit Recognition     | MNIST                | Accuracy          | 98%    |
+| Sentiment Analysis    | IMDb                 | Accuracy          | 91%    |
+| Exploration Task      | Synthetic GridWorld  | Cumulative Reward | 112.5  |
+
+---
+
+## 💻 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