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Follow on Google News | "High achiever" neurons carry the brunt of memoriesA novel neural model sheds light on how the brain stores and manages information.
By: Carl Correns Foundation for Mathematical Biology In a June 30th preprint on bioRxiv (https://www.biorxiv.org/ The finding supports a "hub-and-spoke" This model not only provides insights into human cognition but could have major implications for building better AI systems. Unlike typical AI models that rely on vast amounts of data to learn, a neural ensemble-based model could potentially adapt and learn from fewer examples. For instance, a traditional AI model would require many images to train it to identify shapes. However, this model could be trained on the basic identifying properties of each shape (e.g., a square has four equal sides) from a handful of examples, teaching the model to recognize these shapes in various contexts without extensive training data. Next, Dr. Scheler, Dr. Schumann, and collaborator Prof. Röhrbein, are focused on deploying models such as this one to help other researchers build better AI systems. They are in the initial stages of launching a startup that offers a platform for developers to build network models rooted in biology. "By providing these ready-made components, users can create models that are more cognitively oriented and computationally efficient than traditional statistical machine learning models", says Dr. Scheler. End
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