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How physical systems can learn by themselves: Kirk Lecture

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SPL - New statistical physics in living matter: non equilibrium states under adaptive control

In order for artificial neural networks to learn a task, one must solve an inverse design problem. What network will produce the desired output? We have harnessed AI approaches to design physical systems to perform functions inspired by biology. But artificial neural networks require a computer in order to learn in top-down fashion by minimizing a cost function. By contrast, the brain learns bottom up on its own, with each neuron adjusting itself and its synapses without knowing what all the other neurons are doing, and without the aid of an external computer. We have introduced an approach to bottom-up learning that has been realized in physical systems that learn on their own.

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Average on weekdays: 9031.95812808 kWh Part II Classroom, Department of Genetics Mill Lane Lecture Theatres, Room 3

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