title: Phase response characteristics of model neurons determine which patterns are expressed in a ring circuit model of gait generation.
abstract: In order to assess the relative contributions to pattern-generation of the intrinsic properties of individual neurons and of their connectivity, we examined a ring circuit composed of four complex physiologically based oscillators. This circuit produced patterns that correspond to several quadrupedal gaits, including the walk, the bound, and the gallop. An analysis using the phase response curve (PRC) of an uncoupled oscillator accurately predicted all modes exhibited by this circuit and their phasic relationships--with the caveat that in certain parameter ranges, bistability in the individual oscillators added nongait patterns that were not amenable to PRC analysis, but further enriched the pattern-generating repertoire of the circuit. The key insights in the PRC analysis were that in a gait pattern, since all oscillators are entrained at the same frequency, the phase advance or delay caused by the action of each oscillator on its postsynaptic oscillator must be the same, and the sum of the normalized phase differences around the ring must equal to an integer. As suggested by several previous studies, our analysis showed that the capacity to exhibit a large number of patterns is inherent in the ring circuit configuration. In addition, our analysis revealed that the shape of the PRC for the individual oscillators determines which of the theoretically possible modes can be generated using these oscillators as circuit elements. PRCs that have a complex shape enable a circuit to produce a wider variety of patterns, and since complex neurons tend to have complex PRCs, enriching the repertoire of patterns exhibited by a circuit may be the function of some intrinsic neuronal complexity. Our analysis showed that gait transitions, or more generally, pattern transitions, in a ring circuit do not require rewiring the circuit or any changes in the strength of the connections. Instead, transitions can be achieved by using a control parameter, such as stimulus intensity, to sculpt the PRC so that it has the appropriate shape for the desired pattern(s). A transition can then be achieved simply by changing the value of the control parameter so that the first pattern either ceases to exist or loses stability, while a second pattern either comes into existence or gains stability. Our analysis illustrates the predictive value of PRCs in circuit analysis and can be extended to provide a design method for pattern-generating circuits.
authors.affiliation: Department of Neurobiology and Anatomy, University of Texas Medical School, Houston 77225, USA.
authors.forename: C C
authors.initials: CC
authors.lastname: Canavier
authors.forename: R J
authors.initials: RJ
authors.lastname: Butera
authors.forename: R O
authors.initials: RO
authors.lastname: Dror
authors.forename: D A
authors.initials: DA
authors.lastname: Baxter
authors.forename: J W
authors.initials: JW
authors.lastname: Clark
authors.forename: J H
authors.initials: JH
authors.lastname: Byrne
mesh_terms: D000818:Animals; D005684:Gait / Q000502:physiology*; D008124:Locomotion / Q000502:physiology; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D010507:Periodicity