Modeling of lamprey reticulospinal neurons: multiple distinct parameter sets yield realistic simulations.

For the lamprey and other vertebrates, reticulospinal (RS) neurons project descending axons to the spinal cord and activate motor networks to initiate locomotion and other behaviors. In the present study, a biophysically detailed computer model of lamprey RS neurons was constructed consisting of three compartments: dendritic, somatic, and axon initial segment (AIS). All compartments included passive channels. In addition, the soma and AIS had fast potassium and sodium channels. The soma included three additional voltage-gated ion channels (slow sodium and high- and low-voltage-activated calcium) and calcium-activated potassium channels. An initial manually adjusted default parameter set, which was based, in part, on modified parameters from models of lamprey spinal neurons, generated simulations of single action potentials and repetitive firing that scored favorably (0.658; maximum = 0.964) compared with experimentally derived properties of lamprey RS neurons. Subsequently, a dual-annealing search paradigm identified 4,302 viable parameter sets at local maxima within parameter space that yielded higher scores than the default parameter set, including many with much higher scores of approximately 0.85-0.87 (i.e., ~30% improvement). In addition, 5- and 2-conductance grid searches identified a relatively large number of viable parameters sets for which significant correlations were present between maximum conductances for pairs of ion channels. The present results indicated that multiple model parameter sets ("solutions") generated action potentials and repetitive firing that mimicked many of the properties of lamprey RS neurons. To our knowledge, this is the first study to systematically explore parameter space for a biophysically detailed model of lamprey RS neurons. A computer model of lamprey reticulospinal neurons with a default parameter set produced simulations of action potentials and repetitive firing that scored favorably compared with the properties of these neurons. A dual-annealing search algorithm explored ~50 million parameter sets and identified 4,302 distinct viable parameter sets within parameter space that yielded higher/much higher scores than the default parameter set. In addition, 5- and 2-conductance grid searches identified significant correlations between maximum conductances for pairs of ion channels.