In this paper, the authors built an artificial neural network, to provide insights into statistical learning, based on mimicking corticothalamic circuitry, neocortex interconnections and thalamic nuclei. Indeed, decades of studies have been devoted to building up the predictive learning approach, a mechanism that could be the driving process in statistical learning. This problem is known as statistical learning in neuroscience and it has been the centre of years and years of research. How can we learn languages from pure raw sensory experiences when infants? The more you think of it the more sounds complicated how infants’ brain manages to fix and arrange sounds and transform them, later in few months, in sounds and a real language. Enjoy :) Statistical Learning in Speech: A Biologically Based Predictive Learning Model Authors showed how a simple Ising model, near to the non-equilibrium, can model phase transitions in the brain, mimicking antithetic dynamics such as neuronal avalanches 🔥 and neuronal oscillations 📶. The third paper is something I loved, as it’s related to statistical physics - my hidden passion.
It’s a mathematical one, a fantastic read, where Reinforcement Learning is reviewed in terms of reward prediction-error and based on biological assumptions: 🔝. I guess many of you have always wondered how infants can put all the sounds together and learn languages 🗣️ Rohrlich and O’Reilly from the University of California Davis try to assess this question, developing fully biologically inspired neural networks, which proves experimental findings on infants - already tested in 1996! The second paper comes from Harvard researchers. Reading and being aware of the evolution and new insights in neuroscience not only will allow you to be a better “Artificial Intelligence” guy 😎, but also a finer neural network architectures creator 👩💻!Īugust proposes three complicated papers, that I tried to make simple and digestible. Neuroscience is the root of nowadays artificial intelligence 🧠🤖.
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