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 ϳ600,000 versions of a four-compartment model of the LP neuron and distributed 11 different currents into the compartments. From these, we selected ϳ1300 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 ϳ1300 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.

Variability, compensation, and modulation in neurons and circuits

“…1A). This illustrates an important general principle, namely, that similar electrophysiological behavior can arise from widely disparate sets of underlying conductances (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15). Fig.…”

Homeostasis of functional maps in active dendrites emerges in the absence of individual channelostasis

“…We fixed upper and lower bounds on each measurement such that they cover ∼80% of the corresponding experimental variability (Fig. S1) (14,15) and declared a given model to be experimentally valid if all 18 measurements from that model fell within these bounds (Table S3). We performed this validation procedure on each of the 20,420 models and found 228 models (∼1% of the total population) to match the criteria for validity.…”

“…3J) across the dendritic arbor. However, we did not know whether such nonunique mechanisms recruited toward maintaining functional map homeostasis implied correlated expression profiles of several specific pairs of VGICs or whether a change in one parameter was compensated by changes in many other parameters (14,15). To distinguish between these two possibilities, we analyzed pairwise correlations spanning all 32 parameters that underlie the entire population of valid models (Fig.…”

“…Whereas these results have significant ramifications for the interpretation of experiments involving either genetic deletion or pharmacological blockade of specific channels (14,15), we explored avenues to exploit the statistics of this variable impact of channel deletion toward understanding specific relationships between VGICs and measurements. Specifically, closer analyses of the variable impact of different VGICs on specific measurements revealed clear demarcations in the spread of variability across channels, measurements, and locations (Fig.…”

“…Specifically, the sustenance of the coexistent functional maps has to account for observations that several VGICs govern and modulate any given functional property within a single neuron (12)(13)(14)(15) and that there are spatially widespread, distance-dependent influences of ionic conductances across the dendritic arbor (16,17). Do these requirements impose constraints on the expression profiles and properties of somatodendritic ion channels, if several functional maps were to be coexistent and continuous on the same neuronal topograph?…”