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A spiking neural network (SNN) is a type of artificial neural network that more closely models the behavior of biological neurons by using discrete spike events to transmit information. Unlike traditional neural networks, SNNs process data temporally and are considered a promising approach to achieving efficient and biologically plausible computation.
Loihi is a neuromorphic research chip developed by Intel designed to mimic the architecture and function of the human brain for efficient artificial intelligence applications. It features spiking neural networks and event-driven computation to enable low-power learning and adaptation.
SpiNNaker is a neuromorphic computing platform designed to simulate large-scale spiking neural networks in real time. Developed to model brain-like computation, it uses a massively parallel architecture of low-power ARM cores to achieve high efficiency and scalability.
A memristor is a fundamental passive circuit element that maintains a relationship between electric charge and magnetic flux linkage, effectively acting as a resistor with memory. It has potential applications in non-volatile memory, neuromorphic computing, and next-generation electronics.
TrueNorth is a neuromorphic CMOS integrated circuit developed by IBM designed to mimic the brain’s architecture for efficient computing. It integrates one million programmable neurons and 256 million synapses, enabling novel approaches to machine learning and sensory processing.