Neuromorphic computing is no longer just theory—it’s reshaping how machines process information by mimicking the human brain. In this episode, we dissect the architecture, function, and disruptive potential of Spiking Neural Networks (SNNs)—the third generation of neural networks designed for energy-efficient, real-time, biologically inspired AI.
We explore how SNNs differ from traditional ANNs and deep learning models, the role of temporal coding and STDP in learning, and why memristors and in-memory processing matter. The discussion also covers real-world applications in computer vision, robotics, and edge AI, and dives into specialized neuromorphic chips from IBM, Intel, and startups like Innatera.
Finally, we tackle the hard questions: Can SNNs outperform today's deep learning? What’s holding them back? And how close are we to truly brain-like computing?