Neural Heterogeneity Enhances Reliable Neural Information Processing: Local Sensitivity and Globally Input-slaved Transient Dynamics Cortical neuronal activity varies over time and across repeated trials, yet it consistently represents stimulus features. However, the dynamical mechanism underlying this reliable representation and computation remains elusive. This study uncovers a mechanism for reliable neural information processing by leveraging a biologically plausible network model that incorporates neural heterogeneity. First, we investigate neuronal timescale diversity, revealing that it disrupts intrinsic coherent spatiotemporal patterns, induces firing rate heterogeneity, enhances local responsive sensitivity, and aligns network activity more closely with input. The system exhibits globally input-slaved transient dynamics, which are essential for reliable neural information processing. Other forms of neural heterogeneity, such as non-uniform input connections, spike threshold heterogeneity, and network in-degree heterogeneity, play similar roles. These findings highlight the importance of neural heterogeneity in shaping consistent stimulus representation. This mechanism offers a potentially general framework for understanding the role of neural heterogeneity in reliable computation. Additionally, it informs the design of new reservoir computing models endowed with liquid wave reservoirs for neuromorphic computing.