Neuromorphic Computing and Its Applications in Decision Making

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.