Carroll MS, Viemari JC, Ramirez JM. Patterns of inspiratory phase-dependent activity in the in vitro respiratory network. J Neurophysiol 109: 285-295, 2013. First published October 17, 2012 doi:10.1152/jn.00619.2012.-Mechanistic descriptions of rhythmogenic neural networks have often relied on ball-and-stick diagrams, which define interactions between functional classes of cells assumed to be reasonably homogenous. Application of this formalism to networks underlying respiratory rhythm generation in mammals has produced increasingly intricate models that have generated significant insight, but the underlying assumption that individual cells within these network fall into distinct functional classes has not been rigorously tested. In the present study we used multiunit extracellular recording in the in vitro pre-Bötzinger complex to identify and characterize the rhythmic activity of 951 cells. Inspiratory phasedependent activity was estimated for all cells, and the data set as a whole was analyzed with principal component analysis, nonlinear dimensionality reduction, and hierarchical clustering techniques. None of these techniques revealed categorically distinct functional cell classes, indicating instead that the behavior of these cells within the network falls along several continua of spiking behavior. neuronal networks; pre-Bötzinger complex; respiration; cell types RHYTHMIC ACTIVITY IS UBIQUITOUS in neural systems, from the slow rhythm generated by the suprachiasmatic nuclei that synchronizes mammalian circadian cycles (Dibner et al. 2010), to the broad frequency spectra covered by various oscillations generated within cortical networks (Buzsaki and Draguhn 2004;Tort et al. 2010;Wang 2010). Many of the underlying rules of rhythm generation have been established in neural circuits that generate rhythmic motor behaviors (Goulding 2009;Grillner 2006;Marder and Calabrese 1996). These motor activities are typically reciprocal in nature (e.g., expiration and inspiration, extension and flexion, protraction and retraction) and are often described by ball-and-stick 1 schematics that assume specific connectivity (sticks) between different types of cells or cell populations (balls). In these models each cell possesses functionally discrete properties firing in distinct phases with respect to a given global activity pattern (Guertin 2009; Selverston 2010). Ball-and-stick representations have been particularly powerful in invertebrate neuronal networks (Antonsen and Edwards 2003), because the firing and connectivity properties of physiologically and anatomically identified individual neurons could be reproducibly characterized across different individuals of the same species (Jing et al. 2010;Katz et al. 2010;Marder and Calabrese 1996;Newcomb et al. 2012; Ramirez, 1998;White and Nusbaum 2011). One of the many important lessons learned from these small invertebrate networks is that even in the case of a truly identified neuron, electrophysiological measures of single-cell properties do not suffice to uniquely specify a ...