Frequency-Dependent Action of Neuromodulation

Abstract

In oscillatory circuits, some actions of neuromodulators depend on the oscillation frequency.However, the mechanisms are poorly understood. We explored this problem by characterizing neuromodulation of the lateral pyloric (LP) neuron of the crab stomatogastric ganglion. Many peptide modulators, including proctolin, activate the same ionic current (I MI ) in stomatogastric neurons. Because I MI is fast and non-inactivating, its peak level does not depend on the temporal properties of neuronal activity. We found, however, that the amplitude and peak time of the proctolin-activated current in LP is frequency-dependent. Because frequency affects the rate of voltage change, we measured these currents with voltage ramps of different slopes and found that proctolin activated two kinetically distinct ionic currents: the known I MI , whose amplitude is independent of ramp slope or direction, and an inactivating current (I MI-T ), which was only activated by positive ramps and whose amplitude increased with increasing ramp slope. Using a conductance-based model we found that I MI and I MI-T make distinct contributions to the bursting activity, with I MI increasing the excitability, and I MI-T regulating the burst onset by modifying the post-inhibitory rebound in a frequency-dependent manner. The voltagedependence and partial calcium permeability of I MI-T is similar to other characterized neuromodulatoractivated currents in this system, suggesting that these are isoforms of the same channel. Our computational model suggests that calcium permeability may allow this current to also activate the large calcium-dependent potassium current in LP, providing an additional mechanism to regulate burst termination. These results demonstrate a mechanism for frequency-dependent actions of neuromodulators. Significance statementOscillatory neurons respond to synaptic input in complex ways that depend on the polarity, amplitude, and rate of the input, and intrinsic properties of the cell. As a result, neuromodulator inputs that activate voltage-gated ionic currents can have indirect and state-dependent effects. We show that when a target of neuromodulation is a transient ionic current, an additional layer of complexity of the response emerges in which the oscillation frequency and the indirect influence of other ionic currents shape the amplitude and temporal properties of the neuronal response to the modulator.

Inter-Animal Variability in Activity Phase Is Constrained by Synaptic Dynamics in an Oscillatory Network

“…7 A ). This slow response is indicative of a form of short-term memory over a timescale of many cycles that is attributed to intrinsic properties ( Goaillard et al, 2010 ; Schneider et al, 2021 ). Some aspects of phase regulation are in fact dominated by intrinsic properties.…”

“…In the LP neuron, intrinsic properties have a great influence on φ LP on , as can be seen for example from the slow response of this neuron to repetitive dynamic clamp application of the same artificial synaptic input (Figure 7A). This slow response is indicative of a form of short-term memory over a timescale of many cycles that is attributed to intrinsic properties (Goaillard et al, 2010;Schneider et al, 2021). Some aspects of phase regulation are in fact dominated by intrinsic properties.…”

Neuromodulation Reduces Interindividual Variability of Neuronal Output

“…To measure the noninactivating I MI , the voltage of the LP neuron was ramped from −80 to 20 mV (75 mV/s) and back to −80 mV. The I–V curve was then calculated as the difference between the current responses to the negative ramp in Proc and control saline ( Schneider et al, 2021 ).…”

“…To measure the non-inactivating I MI , the LP neuron's voltage was ramped from -80 mV to 20 mV (75 mV/s) and back to -80 mV. The I-V curve was then calculated as the difference between the current responses to the negative ramp in Proc and control saline (Schneider et al, 2021). We used the maximum current level of each current in each experiment to calculate the variability of that component.…”

“…[Ca] is the calcium concentration outside (out) and inside (in) the cell. The internal calcium concentration is given by The difference between the LP model used in here compared to the model in Schneider et al (2021) is that here, the kinetics of I Ca , I A , and I h were tuned to capture rebound firing as seen in the biological data of this study. The protocol for rebound firing in the model is the same as in the biological experiments, where -5 nA is injected into the LP model soma for 10 s on and 10 s off.…”