Rapid Rebalancing of Excitation and Inhibition by Cortical Circuitry

Abstract

Excitation is balanced by inhibition to cortical neurons across a wide range of conditions. To understand how this relationship is maintained, we broadly suppressed the activity of parvalbumin-expressing (PV⁺) inhibitory neurons and asked how this affected the balance of excitation and inhibition throughout auditory cortex. Activating archaerhodopsin in PV⁺ neurons effectively suppressed them in layer 4. However, the resulting increase in excitation outweighed Arch suppression and produced a net increase in PV⁺ activity in downstream layers. Consequently, suppressing PV⁺ neurons did not reduce inhibition to principal neurons (PNs) but instead resulted in a tightly coordinated increase in both excitation and inhibition. The increase in inhibition constrained the magnitude of PN spiking responses to the increase in excitation and produced nonlinear changes in spike tuning. Excitatory-inhibitory rebalancing is mediated by strong PN-PV⁺ connectivity within and between layers and is likely engaged during normal cortical operation to ensure balance in downstream neurons.

Figure 1. Suppressing PV+ inhibitory neurons increases PN spiking responses throughout the depth of cortex

a–c, In the classic model of feedforward inhibition, suppressing PV+ activity (a) causes a reduction of synaptic inhibition (b, red traces) without affecting synaptic excitation (b, green traces) in recorded PNs. This multiplicatively increases PN spiking without altering tuning (c). Black: control, orange: Arch•PV (illumination). Panel b is a conductance-based integrate and fire model, panels a and c are cartoons. d, PV-Cre expression in a cross to a tdTomato reporter line. Colocalization of native tdTomato fluorescence (red) and PV antibody label (green). 96% of tdTomato-expressing cells expressed PV (n=638 cells; 4 sections, 1 mouse). Section thickness, 30 μm; scale bar, 100 μm. e, Coronal section through auditory cortex showing the colocalization of membrane-bound Arch-GFP (green) and PV antibody labeling (red). 95% of labeled cells expressed Arch-GFP (n=624 cells, 9 sections, 3 mice). Section thickness 30 μm; scale bar, 100 μm. f, Schematic of experimental setup. We recorded from PNs in all layers of cortex (n=85 PNs, extracellular and intracellular recordings). Laser illumination (532 nm) is represented in orange in all figures. g, Depth distribution of presumed PN recordings obtained using the blind patch technique. h, PNs in all layers showed an increase in trial-averaged firing rate on Arch•PV trials. Points show the median response across best stimuli in the Arch•PV and control conditions; error bars show IQR. “Best stimuli” for each cell were the stimuli that evoked spiking responses in the 75th percentile on control trials, i.e. the most effective 1/4th of the stimulus array (WN or CF tones, 0–70 dB).

Figure 2. PV+ suppression increases excitation and inhibition to PNs

a, We recorded from cells in all layers (n=23 cells recorded in voltage-clamp). Depths binned at 50 μm. b–c, Sound-evoked EPSCs and IPSCs for two example PNs. Black, control trials; red/green, Arch•PV trials, mean±SEM. Upper red/black traces are IPSCs (inhibition); lower green/black traces are EPSCs (excitation). Sound presentation indicated by black bars. The amplitude of excitatory and inhibitory synaptic currents increased on Arch•PV trials. This was true for most neurons (for EPSCs, 20/23 cells; IPSCs, 15/23 cells). In no cells did we observe a significant decrease in inhibition. Stimuli: (a) 25 ms WN from 0–70 dB (5 dB steps), 15±2 paired repetitions; (b) 25 ms tones from 2–80 kHz (5 frequencies/octave) at 60 dB, 11±2 paired repetitions. Note separate scales for EPSCs and IPSCs.d–e, Changes in peak current (d) and synaptic charge (e) for the example cells in panels b–c; EPSCs at left, IPSCs at right. Raw values are expressed as a percentage of the maximum control response. Arrows in (d) show the maximum response evoked by a single stimulus in the control and Arch•PV conditions. f, Summary of changes in peak current amplitudes and synaptic charge (n=23 neurons; IPSCs in red, EPSCs in green). Values are normalized to the maximum trial-averaged response, across stimuli, on control trials (see arrows for the example in panel d).

Figure 3. Excitatory-inhibitory balance and timing are preserved

a, E-I co-tuning. EPSC and IPSC responses to tones for example cell in Figure 2c, means across trials. Control, black; Arch•PV, orange. EPSC and IPSC amplitudes were highly correlated across stimuli on control trials (Spearman’s ρ=0.91, p<0.05). The amplitude of evoked currents approximately doubled on Arch•PV trials but EPSCs and IPSCs remained co-tuned (ρ=0.82, p<0.05). b, Group data for E-I correlation (ρ), for n=20 cells showing a significant change in excitatory input on Arch•PV trials. c, E-I ratio. Left: We computed the ratio E/(E+I) from the peak amplitude of trial-averaged currents, as illustrated. Right: Comparison of amplitude ratios on control and Arch•PV trials, n=20 cells. Grey points: E/(E+I) for best stimuli (75th percentile), black points: median±IQR across best stimuli for each cell. d, E-I delay. Left: We computed the excitatory lead time E_lead from trial-averaged currents, as illustrated: E_lead = inhibitory latency minus excitatory latency, measured at half maximum. Right: Grey points: E_lead for best stimuli, black points: median±IQR across best stimuli for each cell, n=20 cells.

Figure 4. Laser illumination increases spiking in Arch-expressing PV+ neurons in L2/3

a, Possible sources of increased inhibition to PNs. PV+ suppression disinhibits PN1 and increases excitatory input to downstream neurons PN2 and IN2. This unidentified inhibitory neuron IN2 could represent a distinct PV-negative inhibitory cell type, or potentially PV+ neurons themselves. PV+ neurons could provide increased inhibition to PNs if the increase in excitatory drive was strong enough to overcome the suppressive effect of Arch. b, To test this idea, we targeted extracellular recordings to PV+ cells in L2/3 and L4 using 2-photon microscopy. Left: PV+ neurons expressed membrane-bound Arch-GFP; right: PV- controls did not. Cells filled gradually with red Alexa dye over the course of the recording, or with current pulses at termination. c, Extracellular spike width and spontaneous firing rate for n=21 Arch-GFP-positive PV+ cells (red) and n=11 Arch-GFP-negative PN controls (black) collected in the same penetrations. Inset shows peak-aligned waveforms computed from 100 spikes. d, Example L2/3 PV+ neurons showing increased spike count on Arch•PV+ trials. Stimuli, WN at 0–70 dB; values are mean±SEM of 20 repetitions. See Figure S1 for example cells in L4. e, Spike times for example cell 4dii, pooled across all repetitions of the stimulus. Arrows, median spike time at 70 dB. f, Percent change in spike count plotted against recording depth for PV+ neurons in L2/3 and L4. Note the transition from suppression to enhancement near the L3–L4 boundary. Open circles, 2-photon targeted recordings (n=21 PV+ neurons); black squares, blind-patch recordings from Figure S1 (n=5 PV+ neurons). Black arrowheads correspond to example cells in 4d. Values are median, IQR across best stimuli (75th percentile). g, Integrate-and-fire model of a circuit with 2 layers of feedforward inhibition, representing cortical layers 2/3 and 4. When a hyperpolarizing current is applied to both PV+ neurons (I_Arch = −0.5 nA), PV+ activity is suppressed in the first layer (gi) but enhanced in the second layer (giii). E-I balance is disrupted in the first PN (gii), but the increase in PV+ activity “rebalances” synaptic input to the second PN (giv). Blue: excitatory input to the first layer of the model.

Figure 5. Focal suppression of PV+ neurons in L2/3 increases PV+ activity in the same layer

a, Feed-forward circuit shown in 4g, re-oriented to show the predicted effects of PV+ suppression within a layer. b, Coronal section from a PV-Cre/tdTomato mouse injected with cre-dependent Arch-GFP AAV. All PV+ neurons expressed tdTomato (red) but only infected PV+ cells in L2/3 expressed Arch-GFP (green). Section thickness, 50 μm; scale bar, 100 μm. Inset: cartoon of the Arch expression site (dashed ellipse). c, Example recordings from neighboring L2/3 PV+ cells at the edge of the expression site. Left: Both neurons expressed tdTomato (red); cell i also expressed Arch-GFP (green). Right: Trial-averaged spike counts on control and Arch•PV trials. The Arch-positive PV+ cell (i) was suppressed on laser trials, whereas the Arch-negative PV+ cell (ii) showed increased spiking responses. d, Sound-evoked spiking responses on Arch•PV trials, as a percentage of the control response. Group data from n=5 mice. di, Arch-positive PV+ cells were suppressed on laser trials (n=6 recordings). dii, Neighboring PNs (n=7) showed increased responses on laser trials. diii, Arch-negative PV+ cells at the edge of the expression site (n=7) showed increased responses on laser trials. Points show the median change across best stimuli; error bars, IQR. Stimuli: WN, 0–70 dB.

Figure 6. Network models for excitatory-inhibitory rebalancing

a, Cascaded Feed Forward (CFF) network model. b, Excitatory and inhibitory input to PNs in stages 3, 5, 7 with PV+ suppression (I_Arch=−0.50 nA). Excitatory conductance, g_e (green); inhibitory conductance, g_i (red). c, The increase in inhibition is not suppressed by stronger Arch currents. Plot shows the change in spiking for PV+ cells in layers 1 and 2 with increasing suppression (I_Arch = −0.6 to −1.8 nA). d, Excitatory and inhibitory input to PNs with increasing suppression, stages 1–15. With stronger suppression, synaptic inputs increase in magnitude but remain balanced. e, Inhibition Stabilized Network (ISN) model. The ISN consists of a pool of interconnected excitatory (E) and inhibitory neurons (I) with weighted connections W_EE, W_EI, W_IE, W_II. The average firing rates of the pools are represented as continuous variables, r_E and r_I. See STAR Methods for details. When recurrent connectivity is strong (all weights = 1.0), a brief stimulus I_Stim elicits a balanced excitatory and inhibitory response (a single black line in 6f–g). f, In f–g, a suppressive current I_Arch is applied to the inhibitory pool. The strength of recurrent excitation (W_{EE, EI}) is varied, all other parameters are fixed (I_Stim = 0.5, duration 800 ms, I_{Arc h} = −0.13, duration 1200 ms). Top: Plot of the excitatory and inhibitory response (peak firing rate) for increasing values of W_{EE, EI}. Bottom: Example traces. When excitatory connections are weak (W_{EE, EI} = 0.2), I_Arch suppresses the inhibitory response below control levels. When excitatory connections are strong (W_{EE, EI} = 0.6), the increase in excitatory input to the inhibitory pool outweighs the suppressive current and produces a net increase in the inhibitory response. Control response is shown in black, Arch•PV response is shown in red and green. g, Excitatory and inhibitory rates with increasing suppression; W_{EE, EI} = 0.75.

Figure 7. Nonlinearities in the process of spike generation shape tuning on Arch•PV trials

a, Spike threshold. Spike rasters and trial-averaged membrane potential traces for an example PN (20 repetitions). Inset: spike count; mean±SEM, all trials. Subthreshold PSPs were completely masked by spike threshold on control trials. b, PSP duration for an example PN (stimulus, 45 dB WN). Duration was measured at half-maximal amplitude of the control response (black lines). Spikes removed for visibility. c, Temporal limit on response duration. PSP duration, n=12 PNs. Filled grey circles: duration of responses to best stimuli (50th percentile); open circles: median±IQR across best stimuli for each cell. d, Three example cells illustrate changes in response reliability (p_spike) and spike number (n_spike). We defined p_spike as the probability that a sound-evoked response contained at least one spike, and n_spike as the number of spikes fired on responses that contained at least one spike. Left panels in i–iii: Trial-averaged spike counts for a CF tone at increasing intensities, 0–70 dB (mean±SEM). Arrowheads, effect size (d) for changes in spike count at best intensity. Right panels: Same data, partitioned into p_spike (top) and n_spike (bottom). n_spike is shown for the subset of intensities that evoked spikes, shaded blue in the left plot; error bars, SD. di, In general, PNs with low-probability spiking responses (p_spike ≪1.0 on control trials) showed a multiplicative increase in the trial-averaged response. This was due to an increase in trial-to-trial reliability (p_spike), rather than the number of spikes fired on a single trial (n_spike); note that this cell responded with at most 1 spike in both conditions. dii, PNs with near-saturated responses at the highest intensities (here, where nspike~=4) showed a disproportional increase in response to weak stimuli. Note the change at 25–45 dB where p_spike<1.0, n_spike<4 on control trials. However, due to the temporal constraint on the response (panels b–c) there was almost no change in the spiking response to preferred stimuli (60–70 dB; n_spike did not exceed 4). diii, Finally, PNs with steep and saturating spiking responses on control trials showed almost no change in spiking on Arch•PV trials. In this example, response reliability saturates at p_spike=1.0, n_spike=1. e, Control response at best intensity vs. laser effect size d. Anesthetized, black (n=73 cells); awake, blue (n=33 cells). Due to the limit on spike numbers, there was a strong negative correlation between the magnitude of the trial-averaged control response and the effect of illumination. ρ, Spearman’s rank correlation. Black arrows, example cells shown in panel d. f, Dynamic range. IRFs were normalized to a maximum of 1 and fit with a generalized logistic function for sigmoidal curves (see STAR Methods for details). The dynamic range was computed from the fit to the data, Δ dB from 20–80% of the maximum response. g, Dynamic range was reduced across the population on Arch•PV trials, in both anesthetized (top) and awake (bottom) mice. Asterisk indicates significance at the group level (rank-sum; see text).

Figure 8. Changes in PN frequency tuning

a, Example of a linear increase in the spiking response. i: Trial-averaged spike count, 20 stimulus repetitions in each condition. Spiking responses increased on Arch•PV trials, but the shape of the frequency tuning curve was preserved. ii: The increase in trial-averaged spike count was completely accounted for by increased trial-to-trial reliability (p_spike). iii: Control response to each tone, vs. the change in the response on Arch•PV trials. The increase was proportional to the control response, showing linearity (ρ: Pearson’s linear correlation). Stimuli, 2–80 kHz at 6 frequencies/octave, intensity 35 dB. Baseline spike rates were not subtracted. b, Nonlinear example. This neuron showed little change in the response to preferred frequencies (~8–16 kHz), where p_spike~1, but showed a dramatic increase at lower frequencies, where the control response was weak. Here, the increase was not correlated with the control response to tones. Stimuli, 2–80 kHz at 4 frequencies/octave, 70 dB. c, We evaluated the linearity of the change in frequency tuning for n=54 cells recorded in anesthetized mice, as in a-b. Stimulus, 2–80 kHz at 6 frequencies/octave, +20 dB above the intensity threshold of the neuron. Only 19/54 PNs showed a linear increase in spiking on Arch•PV trials (35% of our sample). d, To visualize the changes in tuning curve shape, we normalized the control (black) and Arch•PV curves (orange) to the maximum response on Arch•PV trials, and then subtracted them. The resulting “difference curve” (purple) was smoothed at ½ octave. Examples correspond to the neurons in panels a and b. e, Difference curves for all n=54 PNs. Cells are sorted by the difference at best frequency. Three examples are shown at right.