Although the correlation between motoneuron size and type is strong, it is not 1:1 [i.e., motoneurons types overlap largely in soma and cell size (18, 53)]. within motoneuron types. Main results Our results show that each of these practices accentuates conditions of motoneuron recruitment based on the size principle, and minimizes conditions of mixed and reversed recruitment orders, which have been observed in animal and human recordings. Specifically, strict motoneuron orderly size recruitment occurs, but in a compressed range, after which mixed and reverse motoneuron recruitment occurs due to the overlap in electrical properties of different motoneuron types. Additionally, these Aminoguanidine hydrochloride practices underestimate the motoneuron firing rates and force data simulated by existing models. Significance Our results indicate that current modeling practices increase conditions of motoneuron recruitment based on the size principle, and decrease conditions of mixed and reversed recruitment order, which, in turn, impacts the predictions made by existing models on motoneuron recruitment, firing rate, and force. Additionally, mixed and reverse motoneuron recruitment Aminoguanidine hydrochloride generated higher muscle force than orderly size motoneuron recruitment in these simulations and represents one potential scheme to increase muscle efficiency. The examined model design practices, as well as the present results, are applicable to neuronal modeling throughout the nervous system. INTRODUCTION Since Wilfrid Rall first adapted the cable theory to develop computer models of neurons (1), computational modeling has become useful for providing insights and assisting in the interpretation of experimental findings. However, some limitations are present in even the most realistic models. First, models are, by their nature, constrained by the quality and quantity of experimental data available on the system described (2). Additionally, models must attempt to find solutions to systems with many independent variables, which is challenging, particularly in larger models such as those used in systems biology (3). However, modern computational resources, such as the Neuroscience Gateway (4), do much to alleviate constraints on available computational power. While reduced models employing simplified neuronal morphology remain useful for examining research questions of conditions which these models can accurately simulate, recent work has shown that significant abstractions in the modeling process Aminoguanidine hydrochloride can result in inaccurate predictions in other conditions. One example is the case of spinal motoneuron models, especially for firing behaviors mediated by dendritic active conductances (5). Therefore, the development process for computational models must balance design tradeoffs so that simulations incorporate sound design features appropriate for the scientific question and conditions under investigation. The overarching goal of the present study is to examine how model design choices influence simulation results. To achieve that, we first developed a multi-scale, high-fidelity computational model of the spinal motoneuron pool that innervates the cat MG muscle, including its respective motoneuron types: Small, slow-firing S-types, intermediate FR-types, and large, fast-firing FF-types. The cat MG is one of the most studied and well-characterized muscles in literature. Thus, there exist sufficient data to accurately simulate the process of spinal motoneuron KLRD1 recruitment and firing rates. Our model incorporated Aminoguanidine hydrochloride great detail on the cellular and electrical properties that influence the motoneuron recruitment process then underwent a rigorous verification process to validate its parameters Aminoguanidine hydrochloride and results against numerous independent experimental datasets. Second, we used the developed model to examine three key model design features: 1) The effect of overlapping cell properties of modeled motoneuron types, 2) the effect of representing motoneuron types with discrete versus generic cell models, and 3) the effect of simulating biological variability in the cell properties of modeled motoneurons. We studied these three design features because a) these cellular properties are important to firing behaviors and thus are expected to be important to replicating experimental data more closely, and b) because published computational models of motoneuron pools do not incorporate these features (6C12). Thus, the impact of their absence on simulation results is currently unknown. Our results show that incorporating overlap and biological heterogeneity of cell properties in modeled motoneurons and representing motoneuron types with discrete cell models expand the recruitment ranges of all motoneuron types and result in conditions of mixed and reverse recruitment of motoneuron types. Together, these effects limit strict orderly size recruitment (i.e., the size principle) to a.