Diversity in Excitation-Inhibition Mismatch Underlies Local Functional Heterogeneity in the Rat Auditory Cortex

Abstract

Cortical neurons are heterogeneous in their functional properties. This heterogeneity is fundamental for the processing of different features of sensory information. However, functional diversity within a local group of neurons is poorly understood. Here, we demonstrate that neighboring cortical neurons in layer 5 but not those of layer 4 of the rat anterior auditory field (AAF) exhibited a surprisingly high level of diversity in tonal receptive fields. In vivo whole-cell voltage-clamp recordings revealed that the diversity of frequency representation was due to a spectral mismatch between synaptic excitation and inhibition to varying degrees. The spectral distribution of excitation was skewed at different levels, whereas inhibition was homogeneous and non-skewed, similar to the summed spiking activity of local neuronal ensembles, which further enhanced diversity. Our results indicate that AAF in the auditory cortex is involved in processing auditory information in a highly refined manner that is important for complex pattern recognition.

Keywords: auditory cortical field; excitation/inhibition balance; frequency and intensity tuning; synaptic circuit mechanism.

Figure 1. TRFs of single neurons in L5 of AAF differ from those of MUA

(A) Left, recording setup. Middle, an example mapping of auditory cortex. Color of each dot represents its characteristic frequency (CF). A1, primary auditory cortex; AAF, anterior auditory field; A, anterior; D, dorsal. “x” indicates a site with no detectable multiunit responses to sounds lower than 70 dB SPL. “o” indicates a site that does not show clear frequency tuning but has responses to noise at ≥ 60 dB SPL. Right, retrograde labelling in the thalamus by injecting red and green CTB tracers in the lower frequency regions of A1 and AAF, respectively. (B) Example reconstructed spike TRFs of neurons in L4 and L5 of AAF, which are plotted as an array of PSTHs for the responses to pure tones of different frequency-intensity combinations. Each PSTH trace represents the tone-evoked spike response averaged over 5 repetitions. Bin size, 10 ms. Scale, 0.5 spike count. Middle, color maps represent TRFs of average evoked spiking rate over 3 trials for MUA (upper) and over 5 trials for a single neuron (middle) at the same cortical location. Bottom inset, PSTH generated from the spike responses to all effective tones; the bar represents the duration of tone stimuli. Right, 50 superimposed randomly selected spike waveforms of the neuron with the red vertical lines marking the trough-to-peak interval (upper); red vector depicting the difference in stimulus preference between a single neuron and MUA (lower); reconstructed morphology of the recorded neuron. (C, D) Spike TRFs of four adjacent neurons in L4 (C) and L5 (D). Color map represents averaged spiking rate. Spike waveforms are shown to the right. Dashed curves outline the TRF of MUA at the same location. (E, F) Superimposed spike TRFs of adjacent neurons. Each shaded region represents the TRF of one individual neuron. Top, awake state; bottom, anesthetized state. Dashed curves outline the TRF of MUA at the cortical location.

Figure 2. Diverse stimulus preferences of local L5 neurons

(A) Difference in stimulus preference between a single neuron and the corresponding MUA. Left, awake condition (L4, n = 10; L5, n = 16); right, anesthetized condition (L4, n = 73; L5, n = 95). (B, C) Difference in preferred frequency and preferred intensity between a single neuron (SUA) and its corresponding MUA. **, p < 0.01, Welch t test. (D, E) Normalized distribution of TRF size ratio between SUA and MUA in the anesthetized state. Red curve, fitting with a normal distribution. (F) Comparison of the broadness of the distribution between L4 and L5. **, p < 0.01, F test of equality of variances.

Figure 3. Diversity of membrane potential response tuning

(A) Difference in BF between SUA and MUA quantified at 60 dB SPL (L4, n = 20; L5, n = 24). **, p < 0.01, Welch t test. The comparison of SD is boxed, **, p < 0.01, F test. (B, C) Comparison of frequency tuning between MUA (upper) and postsynaptic potential (PSP, after filtering out spikes) of a single neuron (lower) at 60 dB SPL. Red curve, fitting with normal or skew normal distribution. The colored arrow points to the BF. (D) Difference in the response range center between MUA and PSP. **, p = 0.51, t test. Bar = SD. (E) Difference in BF between MUA and PSP. **, p < 0.01, t test. Bar = SD. (F) Skewness of MUA and PSP tuning. Bar = SD. (G) SD of the skewness values of MUA and PSP tuning. **, p < 0.01, F test. (H) Difference in BF between PSP and MUA plotted against the skewness of PSP tuning.

Figure 4. Skewed excitatory inputs dominantly contribute to the tuning diversity in L5

(A, B) Tuning curves of MUA, PSP, excitation and inhibition at 60 dB SPL for an example neuron. The colored curves are fitted normal or skew normal distribution functions. The arrow points to the BF. Scale: 10 mV for PSP; 200 pA for Exc; 150 pA for Inh, 200 ms for all. (C) Difference in BF between synaptic input and MUA. Blue, excitation; red, inhibition. **, p < 0.01, Welch t test. (D) Difference in BF between synaptic input and PSP. **, p < 0.01, Welch t test. (E) Skewness of PSP, excitation, inhibition and MUA tuning in L4 neurons. Data points obtained from the same neuron were connected with lines. (F) Skewness of PSP, excitation, inhibition and MUA tuning in L5. **, p < 0.01, F test. (G) Difference in BF between PSP and MUA plotted against the skewness of excitatory tuning. Red line is the best-fit linear regression line. (H) Difference in BF between PSP and MUA plotted against the skewness of inhibitory tuning. (I) Difference in BF between excitation and inhibition (absolute value) in L4 and L5, **, p < 0.01, Welch t test. (J) Difference in BF between PSP and MUA plotted against that between excitation and inhibition.

Figure 5. Inhibition expands tuning diversity in L5

(A) Difference in BF between PSP and MUA and that between excitation and MUA. **, p < 0.01, paired t test. (B) Tuning curves of inhibition, excitation, PSP derived from excitation only, PSP derived from the integration of excitation and inhibition, and recorded PSP for an example L5 neuron. Scale: 10 mV and 200 ms. (C) Comparison of fitted normal or skew normal distribution curves of the same cell in (B). The vertical arrow points to the BF. The dashed line marks the BF of MUA. (D) Difference in BF between derived PSP and recorded PSP. Bar = SD. Right, same data but in absolute value. **, p < 0.01, paired t test. (E) Difference in BF between PSP (derived or recorded) and MUA. Data points obtained from the same neuron are connected with lines. *, p < 0.05, one-way ANOVA, post hoc test. Right, same data but in absolute value. *, p < 0.05, one-way ANOVA, post hoc test.

Figure 6. Onset latencies of synaptic inputs to L5 neurons and a proposed model

(A) Average recorded synaptic responses to BF tone in an example L5 neuron. Inh, inhibition; Exc, excitation. Arrow points to the synaptic onset. (B, C) Synaptic onset latencies of excitation and inhibition in L4 and L5 neurons. **, p < 0.01, paired t test. Bar = SD. (D) First row, symmetric tuning curves of output responses of a series of presynaptic (excitatory and inhibitory) neurons for an L5 cell. Second row, distribution of synaptic weights of the inputs, which is asymmetric for excitatory inputs but symmetric for inhibitory inputs. Third row, tuning curves of weighted synaptic inputs (output times synaptic weight). Fourth row, summing up the individual curves of weighted synaptic input (solid curve) produces asymmetric excitatory tuning and symmetric inhibitory tuning. The dashed curve represents the sum of synaptic inputs of equal strengths.