Neuromodulation reduces interindividual variability of neuronal output

Abstract

In similar states, neural circuits produce similar outputs across individuals despite substantial interindividual variability in neuronal ionic conductances and synapses. Circuit states are largely shaped by neuromodulators that tune ionic conductances. It is therefore possible that, in addition to producing flexible circuit output, neuromodulators also contribute to output similarity despite varying ion channel expression. We studied whether neuromodulation at saturating concentrations can increase the output similarity of a single identified neuron across individual animals. Using the LP neuron of the crab stomatogastric ganglion (STG), we compared the variability of f-I curves and rebound properties in the presence of neuropeptides. The two neuropeptides we used converge to activate the same target current, which increases neuronal excitability. Output variability was lower in the presence of the neuropeptides, regardless of whether the neuropeptides significantly changed the mean of the corresponding parameter or not. However, the addition of the second neuropeptide did not add further to the reduction of variability. With a family of computational LP-like models, we explored how increased excitability and target variability contribute to output similarity and found two mechanisms: Saturation of the responses and a differential increase in baseline activity. Saturation alone can reduce the interindividual variability only if the population shares a similar ceiling for the responses. In contrast, reduction of variability due to the increase in baseline activity is independent of ceiling effects.Significance StatementThe activity of single neurons and neural circuits can be very similar across individuals even though the ionic currents underlying activity are variable. The mechanisms that compensate for the underlying variability and promote output similarity are poorly understood but may involve neuromodulation. Using an identified neuron, we show that neuropeptide modulation of excitability can reduce interindividual variability of response properties at a single-neuron level in two ways. First, the neuropeptide increases baseline excitability in a differential manner, resulting in similar response thresholds. Second, the neuropeptide increases excitability towards a shared saturation level, promoting similar maximal firing rates across individuals. Such tuning of neuronal excitability could be an important mechanism compensating for interindividual variability of ion channel expression.

Keywords: Bursting Neuron; Central Pattern Generator; Stomatogastric; Variability.

Graphical abstract

Figure 1.

Isolating the LP neuron. A, Schematic of the STNS (top) and the simplified pyloric network with electrodes to indicate which neurons were recorded from (bottom). Modulatory projection neurons are located in the commissural ganglia (CoG) and esophageal ganglion (OG) and project via the stn to the neurons in the STG. Neurons of the pyloric network are located in the STG. The network consists of a pacemaker group (one AB and two PD neurons), and several follower neurons, of which only the LP neuron provides direct chemical feedback to the pacemaker group. Chemical inhibitory synapses and their transmitters (glutamate or acetylcholine) are shown as circles, electrical coupling is depicted with resistor symbols. B–D, Extracellular recordings of the lvn and pdn, which carry axons of both LP (largest unit on lvn) and PD neurons (mid-sized units on lvn), or only of PD neurons, respectively, and intracellular recordings of LP and the two copies of PD. B, When the preparation is intact (all intrinsic neuromodulators present), the LP neuron receives strong periodic inhibition from the pacemaker group. C, After decentralization (removal of intrinsic neuromodulators by transecting the stn), the pyloric rhythm deteriorates but synaptic connections are still functional as illustrated by LP inhibition during PD bursts, and IPSPs in PD for each LP spike. D, After the addition of 10⁻⁵ m PTX, glutamatergic synapses between the pacemaker group and LP are blocked. The cholinergic synapse from PD to LP is still functional. When PDs are hyperpolarized (traces clipped), this synapse is silenced. Recordings in all three panels are from the same experiment and, with exception of the inset in B, on the same voltage scale.

Figure 2.

Variability of modulated components is not different from nonmodulated components across individuals. A, Example voltage-clamp recordings for nonmodulated and modulated currents. Current traces for I_HTK [transient (t) portion indicated by black arrow, persistent (p) by gray arrow], I_A (with total potassium currents I_K as gray overlay; I_A is the difference current between all potassium currents I_K and I_HTK); I_h; the synaptic current I_syn from PD to LP; and the neuromodulator-activated, voltage-gated current I_MI are shown with the corresponding voltage-clamp protocol as the inset. B, From these voltage traces, the features indicated with white markers were extracted: the maximum transient current for both potassium currents and additional the persistent current for I_HTK, the amplitude of the current at the end of the hyperpolarizing voltage step for I_h; the scaling factor for the sigmoid fit for I_syn; and the maximum inward current of the current fit for I_MI. C, The distribution of these parameters across experiments are shown. Each dot represents one experiment, and red lines mark the mean. Black is for control (decentralized), blue is for Proc. Transient I_HTK is shown in bold colors, persistent in transparent colors. Since I_MI is calculated as a difference current (Proc – ctrl), there are no data in control. D, Coefficients of variation (SD/mean) are in the same range for the nonmodulated currents (I_HTK, I_A, I_h) and modulated currents (I_syn, I_MI).

Figure 3.

Neuromodulation changes excitability and reduces interindividual variability. A, Example of f – I relationships in four modulatory conditions (black: control; blue: Proc; magenta: Proc + CCAP; gray: wash). Left panel: Intracellular recording at four levels of increasing current injection in control condition. Right panel: Instantaneous spike frequencies at each current level in different neuromodulatory states, shown as the average with SD, were fitted with a power function. The left inset shows how the fit parameters influence the appearance of the curve. B, Distribution of fit parameters. Each dot represents the value from one experiment, and red lines indicate means. Application of one (blue, Proc) or two (magenta, Proc + CCAP) neuromodulators significantly changed parameters (asterisks). C, Application of one or two neuromodulators reduced the variability of the fit parameters compared with control (black). D, f – I curves showed hysteresis depending on in creasing (filled circles) or decreasing (open circles) levels of current injection. Only frequencies between 2 and 4 nA current injection (bold symbols) were used to calculate hysteresis as the ratio between increasing and decreasing current levels. E, Distribution of hysteresis across experiments. Application of one or two neuromodulators significantly changed hysteresis (asterisks). F, Application of one or two neuromodulators reduced the variability of hysteresis compared with control (black). Removing the outlier in control (i.e., the maximum value), for both hysteresis and I₀, did not change the statistical significances. Control data for this figure are shown in Extended Data Figure 3-1. Raw data for B and E are provided in Extended Data Figure 3-2.

Figure 4.

Neuromodulation changes general rebound properties and reduces interindividual variability. A, Spike raster and intracellular recording of the last of the five sweeps of one example experiment in four modulatory conditions (black: control; blue: Proc; magenta: Proc + CCAP; gray: wash). B, Latency was measured as the time from the end of the hyperpolarizing current injection to the first spike, averaged across all five sweeps. Sweeps of the intracellular recording are shaded in gray, from dark to light for sweeps 1–5. C, Spike histogram (Ci, 200 ms bin size) and sigmoid fit to the cumulative spike histogram (Cii). Dots indicate sigmoid midpoint. A–C are from the same experiment. D, Parameter distribution for latency and fit parameters; t_1/2 is relative to the end of the current injection (A, t₀). Dots represent individual experiments; the red line indicates the mean. Application of neuromodulators significantly (asterisks) changes most parameters (n.s.: ANOVA not significant). E, Variability of all parameters is reduced in the presence of neuromodulators. Control data for this figure are shown in Extended Data Figure 4-1. Raw data for D are provided in Extended Data Figure 4-2.

Figure 5.

Neuromodulation changes steady-state rebound properties and reduces interindividual variability. A, Example intracellular recording of the LP neuron with 20 cycles of periodic inhibition. B, Spike raster and intracellular recording of all 20 sweeps of the same experiment in all four modulatory conditions (black: control; blue: Proc; magenta: Proc + CCAP; gray: wash). C, During the first sweeps the latency to the first spike successively decreased; therefore, we only included the last 10 sweeps (steady state) in the further analysis. The open circles indicate the sweep at which the latency was reduced to 63% of its total range. D, Spike histogram (Di, 100 ms bin size) and sigmoid fit to the cumulative spike histogram (Dii). Dots indicate sigmoid midpoint. A–D are from the same experiment. E, Parameter distribution for latency and fit parameters; t_1/2 is relative to the end of current injection (B, t₀). Dots represent individual experiments; the red line indicates the mean. Application of neuromodulators significantly changes parameters. F, Variability of all parameters is reduced in the presence of neuromodulators. Control data for this figure are shown in Extended Data Figure 5-1. Raw data for E are provided in Extended Data Figure 5-2.

Figure 6.

Increasing ${\overline{g}}_{\mathrm{MI}}$ in a family of LP models reduces the variability of rebound parameters. A, A family of 198 LP models was tuned to the rebound statistics from the biological experiments. Ai, Example voltage traces and spike histogram of one of the rebound LP models at different levels of added Δ ${\overline{g}}_{\mathrm{MI}}$. Aii, Paired plots of the rebound parameters from the biological experiments (black) and the family of LP models (orange). Aiii, The baseline ${\overline{g}}_{\mathrm{MI}}$ distribution (top) was shifted by either adding a fixed amount of ${\overline{g}}_{\mathrm{MI}}$ (Δ ${\overline{g}}_{\mathrm{MI}}$, middle) or a variable amount of ${\overline{g}}_{\mathrm{MI}}$ with a fixed mean (σ ${\overline{g}}_{\mathrm{MI}}$, bottom). B, Fits to the cumulative spike histograms (top row), the fit parameters and latency (middle row), and the corresponding measure of variability (bottom row) when adding increasing levels of ${\overline{g}}_{\mathrm{MI}}$ with a fixed distribution (Δ ${\overline{g}}_{\mathrm{MI}}$). Individual dots represent values from an individual LP model, blue bars indicate the mean. An asterisk above a CV bar indicates that the CV for this group is significantly different from the CVs of all other groups. C, Fits to the cumulative spike histograms (top row), the fit parameters and latency (middle row), and the corresponding measure of variability (bottom row) when adding variable levels of ${\overline{g}}_{\mathrm{MI}}$ with a fixed mean (σ ${\overline{g}}_{\mathrm{MI}}$). Individual dots represent values from an individual LP model, blue bars indicate the mean.

Figure 7.

Increasing g_MI in a family of LP models reduces the variability of f–I parameters. A, Schematic of the right shift of ${\overline{g}}_{\mathrm{MI}}$ to increase excitability (Ai) and f–I curves of a family of 85 LP models for selected Δ ${\overline{g}}_{\mathrm{MI}}$ values (Aii). B, Fit parameters (top row) and corresponding variability measures (bottom row). Dots represent individual experiments; blue bars indicate the mean.

Figure 8.

Increasing excitability in a family of LIF models partially reduces the variability of f–I parameters. A, f–I curves for a family of 500 LIF models at three different Δ ${\overline{g}}_{\mathrm{MI-L}}$ values. As in the LP models, increasing excitability shifts the curves to the left. B, Fit parameters (top row) and corresponding variability measures (bottom row). Dots represent individual experiments; blue bars indicate the mean.