A new computational model emulates how the brain's neocortex processes information and learns through self-supervised predictive learning. This model offers a deeper understanding of how our brains anticipate and interpret the world around us, which is crucial for understanding consciousness.
The model, developed by researchers, mimics the structure of the neocortex, using layers that correspond to different brain regions. Layer 2/3 predicts incoming sensory information, while Layer 5 receives the actual sensory input. The model learns by comparing these predictions with the actual sensory data, adjusting its internal connections to minimize errors.
This approach allows the model to learn predictive representations, similar to how our brains learn to anticipate events. The model also demonstrates how different layers of the cortex play distinct roles in processing information. This research highlights the importance of self-supervised learning in the brain's ability to understand and predict the world.
This research provides valuable insights into how the brain learns and processes information. Understanding these mechanisms could lead to advancements in artificial intelligence and our understanding of consciousness. The model's ability to mimic the brain's predictive capabilities offers a promising avenue for future research into the complexities of the human mind.