New research conducted by Professor Richard Naud of the University of Ottawa’s Brain and Mind Research Institute has unlocked the secrets of the brain using mathematics. Naud’s equation, developed to describe ‘dendritic excitability’, sheds light on how neurons communicate information across synapses in the brain.
The equation, published in Nature Computational Science, reveals that neurons process information in a dynamic and efficient manner, far surpassing the capabilities of a typical computer. Naud’s work has implications for artificial intelligence, learning, and the treatment of diseases such as Parkinson’s.
One key finding of the research is that dendrites, the branch-like appendages of neurons, do not fire in a predictable cause-and-effect manner. Instead, they communicate through variability, encoding information in the randomness of their spikes.
This variability plays a crucial role in learning, as it allows neurons to process two signals simultaneously and send a ‘learning signal’ to synapses. Naud’s team theorizes that mimicking this process in AI algorithms could lead to more energy-efficient and faster learning systems.
Furthermore, Naud’s work has implications for brain stimulation treatments for conditions like Parkinson’s and depression. By understanding how variability in the neural system works, researchers may be able to rewire brains and restore functionality through targeted electrical stimulation.
Overall, Naud’s groundbreaking research offers a new perspective on how the brain processes information and opens up exciting possibilities for future advancements in neuroscience and artificial intelligence.