This lecture opens the unit on Neural Computation and Learning, which discusses the neurobiological underpinnings of learning in biological systems and attempts at developing similar capabilities in artificial systems with artificial neural networks (including spiking neural networks). In this lecture, learning, memory, and neuroplasticity are introduced alongside a basic model of the canonical neuron with axons, dendrites, and action potentials. Time is spent discussing the differences in the function and costs of working, short-term, and long-term memory as well as the three different neuronal mechanisms underlying these three different forms of memory. Then the basics of biological learning, from sensory adaptation to non-associative learning (habituation and sensitization) to associative learning/conditioning (classical and operant) to latent learning, are presented along with basic models of how these different forms of learning can be built up from mechanisms of neuroplasticity discussed earlier. Finally, the different modalities of machine learning (unsupervised, self-supervised, reinforcement, and supervised learning) are presented and connected to the best-fitting biological learning paradigms (as well as potential neuronal mechanisms that could be used ot build such capabilities).
Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/mlw3u4yf19fbqnypjc39z/IEE598-Lecture7A-2025-04-08-Neural_Foundations_of_Learning-Notes.pdf?rlkey=5mhnsfmmgtiad050u69qov80x&dl=0
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