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---

# Computational Neuroscience Dataset

- **Curated by:** Dr. Jerry A. Smith  
- **License:** CC-BY-4.0  
- **Domain:** Neuroscience and Artificial Intelligence  
- **Languages:** English  

This dataset was curated and designed specifically for fine-tuning large language models (LLMs) on computational neuroscience concepts and brain-inspired AI. It includes prompts and responses tailored to topics like **spiking neural networks**, **synaptic plasticity**, **neuromorphic computing**, and **theoretical neuroscience**.

---

## Overview

The **Computational Neuroscience Dataset** is a comprehensive collection of prompts and responses that cover a wide range of topics at the intersection of neuroscience and artificial intelligence. Its purpose is to enable the development of AI models that can provide insightful, context-aware, and domain-specific answers to questions about brain-inspired computation.

### Key Features:
1. **Domain-Specific Content:** Focused on neuroscience and bio-inspired AI.  
2. **Well-Structured Format:** Prompt-response pairs tailored for training LLMs.  
3. **Diverse Topics:** Covers both theoretical and practical aspects of computational neuroscience.

---

## Dataset Composition

### Topics Covered:
1. **Spiking Neural Networks:**  
   - Event-driven computation.  
   - Temporal dynamics and plasticity mechanisms (e.g., STDP).  
   - Applications in robotics and real-time systems.

2. **Synaptic Plasticity:**  
   - Hebbian learning principles.  
   - Homeostatic plasticity.  
   - Biological learning mechanisms for AI.

3. **Brain-Inspired Architectures:**  
   - Hierarchical and modular processing.  
   - Scalability and fault tolerance.  
   - Neural network designs inspired by cortical microcircuits.

4. **Neuromorphic Computing:**  
   - Sparse coding and energy efficiency.  
   - Asynchronous processing.  
   - Applications in hardware design and low-power AI.

5. **Theoretical Neuroscience:**  
   - Oscillations and criticality.  
   - Network synchronization.  
   - Large-scale brain simulations.

---

### Dataset Structure:
The dataset is structured as a collection of JSON objects, each containing a **prompt** and a corresponding **response**:

#### Example:
```json
{
    "prompt": "How can spiking neural networks improve real-time robotics?",
    "response": [
        "Explain how spiking neural networks process temporal patterns efficiently.",
        "Describe their low-energy consumption through event-driven computation.",
        "Provide examples like autonomous vehicles and robotic sensory integration."
    ]
}
Usage

Loading the Dataset
The dataset can be loaded using Hugging Face's datasets library:

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("your-huggingface-username/your-dataset-name")

# Access train split
train_data = dataset["train"]
Dataset Fields:
prompt: A string containing the question or topic.
response: A list of strings providing a detailed answer to the prompt.
Example Usage in Model Training:
This dataset is ideal for fine-tuning large language models:

from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments

# Load dataset
dataset = load_dataset("your-huggingface-username/your-dataset-name")

# Load model and tokenizer
model_name = "unsloth/llama-3-8b-Instruct-bnb-4bit"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define training arguments
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="steps",
    save_strategy="steps",
    per_device_train_batch_size=4,
    num_train_epochs=3,
    save_steps=10,
    logging_steps=10,
    learning_rate=2e-5
)

# Fine-tune model
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset["train"]
)

trainer.train()
Applications

This dataset is designed for:

Fine-Tuning AI Models: Train LLMs to provide domain-specific insights in computational neuroscience.
Educational Tools: Develop tools to teach neuroscience and AI concepts interactively.
Research Assistance: Build AI systems that assist in designing brain-inspired architectures or exploring theoretical neuroscience.
License

This dataset is distributed under the CC-BY-4.0 license. Please provide appropriate attribution if used.

Citation

If you use this dataset in your research or applications, please cite it as follows:

@misc{computational-neuroscience-dataset-2024,
  author = {Dr. Jerry A. Smith},
  title = {Computational Neuroscience Dataset for AI Fine-Tuning},
  year = {2024},
  url = {https://huggingface.co/datasets/your-huggingface-username/your-dataset-name},
}
