Neuromorphic computing hardware has been driven by the need for more efficient and adaptive processing systems, with recent breakthroughs in materials science leading to novel synapse devices like memristors and phase-change memory (PCM). These devices enable spiking neural networks (SNNs) on a chip, allowing real-time processing of complex data streams. Neuromorphic computing frameworks like Nengo and OpenSPIN-MEX facilitate the development of more sophisticated SNN models, while integration with artificial intelligence (AI) has opened up new possibilities for developing robust and adaptive AI systems. Challenges remain in neuromorphic computing, including efficient and scalable architectures, but future directions will be shaped by the integration of AI with neuromorphic computing, driving innovation in hardware and software.
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