title: How multiple conductances determine electrophysiological properties in a multicompartment model.
abstract: Most neurons have large numbers of voltage- and time-dependent currents that contribute to their electrical firing patterns. Because these currents are nonlinear, it can be difficult to determine the role each current plays in determining how a neuron fires. The lateral pyloric (LP) neuron of the stomatogastric ganglion of decapod crustaceans has been studied extensively biophysically. We constructed approximately 600,000 versions of a four-compartment model of the LP neuron and distributed 11 different currents into the compartments. From these, we selected approximately 1300 models that match well the electrophysiological properties of the biological neuron. Interestingly, correlations that were seen in the expression of channel mRNA in biological studies were not found across the approximately 1300 admissible LP neuron models, suggesting that the electrical phenotype does not require these correlations. We used cubic fits of the function from maximal conductances to a series of electrophysiological properties to ask which conductances predominantly influence input conductance, resting membrane potential, resting spike rate, phasing of activity in response to rhythmic inhibition, and several other properties. In all cases, multiple conductances contribute to the measured property, and the combinations of currents that strongly influence each property differ. These methods can be used to understand how multiple currents in any candidate neuron interact to determine the cell's electrophysiological behavior.
authors.affiliation: Volen Center, Brandeis University, Waltham, Massachusetts 02454, USA. altaylor@brandeis.edu
authors.forename: Adam L
authors.initials: AL
authors.lastname: Taylor
authors.forename: Jean-Marc
authors.initials: JM
authors.lastname: Goaillard
authors.forename: Eve
authors.initials: E
authors.lastname: Marder
mesh_terms: D000818:Animals; D003445:Crustacea; D055724:Electrophysiological Phenomena / Q000502:physiology; D009431:Neural Conduction / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000166:cytology / Q000502:physiology; D055366:Synaptic Potentials / Q000502:physiology