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Learn to build a spiking neural network from the ground up using biologically-inspired principles without relying on traditional backpropagation or stochastic gradient descent algorithms in this 21-minute tutorial. Explore the fundamental concepts of neuromorphic computing by implementing spike-based neural computation that mimics how biological neurons communicate through discrete electrical impulses. Discover alternative learning mechanisms that move beyond conventional deep learning approaches, focusing on temporal dynamics and event-driven processing that characterize real neural systems. Gain hands-on experience constructing a network architecture that processes information through spike timing and frequency patterns rather than continuous activation functions. Master the implementation details of neuron models, synaptic connections, and learning rules that enable the network to adapt and learn without gradient-based optimization methods.
