Reverse-Engineering the Wetware: Spiking Networks and the End of Matrix Math
11 hours ago
- #AI
- #neuroscience
- #machine-learning
- Human perception involves top-down feedback loops, unlike passive AI models.
- The brain uses Predictive Coding, constantly generating and correcting simulations of the world.
- Biological learning relies on Spike-Timing-Dependent Plasticity (STDP) instead of backpropagation.
- Dopamine acts as a Reward Prediction Error (RPE), similar to Temporal Difference (TD) Learning in AI.
- Neuromorphic chips like Intel’s Loihi 2 are designed to mimic biological neural networks efficiently.
- Local learning rules like Target Propagation and Feedback Alignment offer alternatives to backpropagation.
- AI and neuroscience are converging, offering new insights into biological intelligence.