Neuromorphic Computing and Its Applications in Decision Making
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The proposed review combines an analysis of event-driven encoding, spike-timing-dependent plasticity (STDP), and reward-modulated learning rules with a discussion of how these mechanisms translate into real-time, low-power decision-making pipelines for robotics, autonomous navigation, and financial signal processing.
A simulated spiking decision-network is described and evaluated on benchmark event-based datasets, comparing its accuracy, latency, and energy consumption against conventional artificial neural network (ANN) baselines.
The results indicate that neuromorphic decision-making systems can achieve competitive accuracy while consuming substantially less energy and responding with lower latency, making them attractive for edge and real-time autonomous applications.
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