Spiking Neural Networks: The Bridge Between the Human Brain and Artificial Intelligence

The human brain is a massive system comprised of approximately 100 billion neurons. These neurons transmit signals via electrical impulses called spikes. This means our brain processes not only "what happened?" but also "when?" Spiking Neural Networks (SNNs) mimic precisely this natural mechanism.

🔹 Why is it important?

• Classical neural networks (CNN, RNN) consume high energy because they constantly process data.

• SNNs, on the other hand, only process when events occur → resulting in 100x–1000x energy savings.

• This feature makes them ideal for real-time applications such as sensors, robotics, and autonomous vehicles.

🔹 Advantages:

• Brain-like time coding

• Event-driven processing

• Higher energy efficiency

• Potential to combine visual, temporal, and robotic tasks in a single network

🔹 Challenges:

• The education process is incompatible with classical methods

• Benchmark accuracies (e.g. ImageNet) are still low

• Software tools are under development

• How spikes best represent information is still being researched

🌍 In short: SNNs, inspired by the human brain, could push AI to a more natural, efficient, and sustainable level. However, significant hurdles remain.

👉 For more: https://drive.google.com/drive/folders/1RhvWKwE9eWX1Lw1uPlLXA_xEtSPi9w0N?usp=sharing