From Traditional Neural Networks to Spiking Neural Networks: In-depth Analysis of the Neural Networks & Learning Project

This project is an open-source research repository developed by gandhico on GitHub, covering the complete technical spectrum of Artificial Neural Networks (ANN) and Spiking Neural Networks (SNN). It provides Julia/Python implementations, high-dimensional dataset processing workflows, and High-Performance Computing (HPC) training solutions. More than just a collection of code, this project serves as a systematic learning and research platform, also involving control research for the X-Plane12 flight simulator and the EHEKATL Blended Wing Body (BWB) aircraft, offering researchers and developers a complete toolchain from basic theory to advanced applications.

In the AI field, neural network technology is evolving from traditional ANN to more bionic and efficient SNN. As a comprehensive research platform for this transition, this project integrates mathematical modeling, simulation visualization, training paradigms, and data processing workflows, covering the complete technology stack from ANN to cutting-edge SNN. Its unique applications include control research for the X-Plane12 flight simulator and the experimental Blended Wing Body (BWB) aircraft EHEKATL, demonstrating the application potential of neural networks in complex dynamic systems.

Neural Network Models

• Continuous ANN: Uses traditional deep learning architectures and continuous activation functions, suitable for fields like image recognition and NLP.
• Discrete SNN: Uses spike signals to transmit information, closer to biological nervous systems, including advanced models like HyperLIF and Adaptive HyperLIF, solving the training stability issues of traditional SNN.

Dataset Support

• Standard Benchmarks: MNIST handwritten digit dataset (for quick algorithm validation).
• Neuromorphic Datasets: Event-driven datasets like N-MNIST and DVS128 Gesture, adapted to the temporal processing capabilities of SNN.

HPC Support

Provides batch submission scripts, multi-node parallel optimized training workflows, and standard paper reproduction workflows to meet the needs of large-scale experiments.

EHEKATL Blended Wing Body Aircraft Control

EHEKATL is an experimental BWB aircraft with inherent instability, high aerodynamic performance, and green propulsion potential. The project implements linear/nonlinear controllers in Python, achieving precise control in X-Plane12, providing a case study for real-time neural network control applications.

Comparison of ANN and SNN Characteristics

Feature	ANN	SNN
Information Encoding	Continuous Values	Discrete Spikes
Time Dimension	Static/Sequential	Explicit Time
Energy Consumption	High	Extremely Low (Event-driven)
Biological Similarity	Low	High
Training Difficulty	Mature (Backpropagation)	Challenging (Requires Alternative Algorithms)

The project supports both architectures simultaneously, providing a platform for comparative research.

Academic Research

Provides scholars in neuromorphic computing and brain-inspired intelligence with: complete references from theory to implementation, comparative implementations of multiple SNN variants, and benchmark test results on standard datasets.

Engineering Development

The low-power characteristics of SNN make it suitable for edge device deployment; the HPC module supports the transition from prototype to large-scale deployment.

Aerospace

The EHEKATL control case demonstrates the application of neural networks in complex dynamic systems, providing technical references for related fields.

This project is an open-source platform with both technical depth and breadth, covering the complete evolution from ANN to SNN, combining theoretical research with practical applications (such as aircraft control). It is a valuable starting point for developers/researchers who want to delve into neuromorphic computing, explore SNN training methods, or study real-time control applications. With the development of brain-inspired hardware like Intel Loihi and IBM TrueNorth, SNN is expected to play a more important role in future AI applications, and such open-source projects are key forces driving the progress of the field.