The Stochastic Neural Analog Reinforcement Calculator (SNARC) is a model developed in the context of artificial intelligence and neural computation. This concept was explored in research by researchers like Stephen W. Smith, who investigated how neural networks can be used to model complex decision-making processes. SNARC typically involves the use of reinforcement learning, where agents learn to make decisions by receiving feedback from their environment.
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