Fig. 3: Brain-inspired dynamic framework for neuromorphic computing.

a Attention-based dynamic response in neuroscience. The brain’s dynamic responses are associated with visual attention. Since attention is a limited resource, the brain only selectively processes a part of sensory input. The neural correlates of attention can be roughly divided into four structural levels^14,15. Attention neural circuit. The top-down versus bottom-up dichotomy is one of the classic classifications of attention neural circuits, which encompass multiple visual areas⁶³. Top-down deploys the attention to internal, behavioral goals of the brain, which can be present through the priority map. Bottom-up allocates attention according to the physical salience of a stimulus, which the salience map can illustrate. Visual area. The regulation of attention involves multiple brain areas, which generally results in changes in neuronal firing rate within the areas¹⁵. Neuron. Attention-related neuronal modulations¹⁶. Recordings from individual cells have shown that attention is associated with the change in neuron firing, which can enhance the quality of sensory representations. Synaptic. Attention fine-tunes neuronal communication by selectively modifying synaptic weights, enabling enhanced detection of important information in a noisy environment³⁴. b A typical spiking neuron model: Leaky Integrate and Fire (LIF). c Attention-based dynamic SNNs. The proposed dynamic framework exists as plug-and-play attention modules that optimize the membrane potential in a data-dependent manner in both temporal and channel dimensions. The dynamic framework provides two types of combinable strategies, refinement, and masking, to expand the strategy space and establish a better trade-off between accuracy and energy consumption.