Cortical Learning is the study of how the neocortex learns, remembers, and understands the world through sparse, predictive representations. Open research. Open source. Open minds.
Inspired by the groundbreaking work of Jeff Hawkins and Numenta, this platform is the open home for research, implementations, and discussion around the neocortex's learning algorithms.
Modern deep learning is powerful but fundamentally different from biology. Cortical Learning studies the actual mechanisms the neocortex uses: sparse distributed representations, continuous online learning, and prediction at every level.
Only ~2% of neurons fire at any time. This sparsity creates robust, high-capacity memory that is fault-tolerant and energy efficient.
The cortex constantly predicts the next input. When predictions match reality, learning is minimal. When they don't, the brain updates its model.
Each cortical column maintains a model of the world from its own perspective. Thousands of columns vote to create unified perception.
Cortical principles are already powering next-generation systems in anomaly detection, robotics, and autonomous agents that learn continuously without catastrophic forgetting.
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