arXiv:2611.07830v1 Announce Type: new Abstract: Multisensory Physical AI systems operating in isolated conditions must make timely and reliable decisions within strict energy and hardware budgets. In this context, the present analytical study investigates the feasibility of reliable multimodal sensor fusion using a neuromorphic approach based on memristor-based Integrate-and-Fire neuron circuits driven by aggressively compressed representations of sensory information. Rather than relying on fixed processing and learning parameters, the proposed framework introduces scenario-dependent adaptability into individual stages of the neuromorphic decision-making pipeline, including sensor-data compression, nonlinear spike encoding, reliability assessment, and memristive learning-forgetting dynamics. By coupling multiple sensor inputs through adaptive memristive synapses, the framework investigates how reliable and correlated observations can strengthen learned associations, while weak or obsolete relationships gradually decay through nonlinear forgetting. The resulting neuronal responses are evaluated across ten representative driving scenarios derived from the NuScenes dataset by comparing their encoded output patterns with predefined synthetic ground-truth codes representing the expected actuator responses. The simulations demonstrate that scenario-adaptive activity-vector sizing achieves a 61.5% reduction in retained sensory information, while improving mean bit-wise agreement from approximately 55% to 61.2% compared with a fixed maximum-resolution configuration. These findings highlight the potential of introducing scenario-aware adaptability directly into the building blocks of a neuromorphic system, providing a foundation for future hardware implementations that could significantly transform the capabilities of Physical AI systems operating under stringent resource and environmental constraints.