Stimulus-timing-dependent plasticity of cortical frequency representation

Abstract

Adult cortical circuits possess considerable plasticity, which can be induced by modifying their inputs. One mechanism proposed to underlie changes in neuronal responses is spike-timing-dependent plasticity (STDP), an up- or downregulation of synaptic efficacy contingent upon the order and timing of presynaptic and postsynaptic activity. The repetitive and asynchronous pairing of a sensory stimulus with either another sensory stimulus or current injection can alter the response properties of visual and somatosensory neurons in a manner consistent with STDP. To examine whether such plasticity also exists in the auditory system, we recorded from neurons in the primary auditory cortex of anesthetized and awake adult ferrets. The repetitive pairing of pure tones of different frequencies induced shifts in neuronal frequency selectivity, which exhibited a temporal specificity akin to STDP. Only pairs with stimulus onset asynchronies of 8 or 12 ms were effective and the direction of the shifts depended upon the order in which the tones within a pair were presented. Six hundred stimulus pairs (lasting approximately 70 s) were enough to produce a significant shift in frequency tuning and the changes persisted for several minutes. The magnitude of the observed shifts was largest when the frequency separation of the conditioning stimuli was < approximately 1 octave. Moreover, significant shifts were found only in the upper cortical layers. Our findings highlight the importance of millisecond-scale timing of sensory input in shaping neural function and strongly suggest STDP as a relevant mechanism for plasticity in the mature auditory system.

Figure 1.

Determination of response window and conditioning stimuli. A, An average PSTH (5 ms resolution) was generated for each unit by averaging its responses over all mapping blocks and sweeps to determine its onset response window. Iso-intensity tuning curves, such as the preconditioning tuning curve in B, were based on the firing rates measured during this response window (dark bars), which included bins extending from the first one to exceed the spontaneous firing rate (measured 250–500 ms after stimulus onset) by at least 3 SDs to the one preceding the first bin after the response fell to <50% of the peak firing rate. Two frequencies were selected from the iso-intensity-tuning curve to construct conditioning pairs, a PF and a NPF. The former had to lie within the range of frequencies to which the unit responded strongly, the latter outside of that range.

Figure 2.

Experimental procedure and conditioning stimuli. A, After determining unit threshold at each recording site, an iso-intensity-tuning curve was obtained in an initial preconditioning mapping (M) block, which was followed by three alternating conditioning (C) and mapping blocks. In some cases, one or two additional mapping blocks were included after a delay, to probe the persistence of any changes in frequency tuning. B, Pairs of conditioning stimuli comprised two 5 ms pure tones of different frequencies (see Fig. 1). During positive conditioning the NPF tone preceded the PF tone. During negative conditioning, the order was reversed. The interval between the onsets of the two stimuli in a pair was either 8, 12, 16 or 30 ms, and the rest period between two consecutive pairs was fixed at 100 ms. Conditioning pairs were presented in blocks of 600.

Figure 3.

Spike timing precision of conditioning responses. A, Raster plots (bottom) and PSTHs (top) of action potentials fired during 600 pairs of positive (NPF preceding PF, red) and 600 pairs of negative conditioning (PF preceding NPF, blue) with an 8 ms interval. B, Cross-correlating the spike trains from positive and negative conditioning shown in A resulted in a cross-correlation function with a peak at 8 ms and a width at half-maximum of 10 ms. C, The majority of cross-correlation functions (n = 339) exhibit peak positions coinciding with the interval between the stimuli in the conditioning pair. Results are normalized so that a value of 0 means that the peak position of the cross-correlation function was equal to the interval and negative values indicate that the peak position was closer to 0 ms than expected from the interval used. D, Distribution of widths of cross-correlation functions across the population of units in this study.

Figure 4.

Example tuning curves. A, C, Positive conditioning examples. B, D, Negative conditioning examples. Preconditioning iso-intensity tuning curves are plotted in black. Postconditioning curves are either shown as the average of all three post-conditioning mapping blocks (plotted in red, as in A and B), or individually for the first (dark blue), second (mid blue) or third (light blue) post-conditioning mapping blocks, as in C and D. The persistence of the shifts in the iso-intensity frequency tuning curves is depicted in B and C by the postconditioning curves obtained at different delays (light green, first delay period; dark green, second delay period). Best frequencies (peaks of fitted Gaussians) are indicated by vertical lines above the tuning curves. Gaussian fits (dashed line) and error bars are shown for both preconditioning and postconditioning tuning curves in A, but, for clarity, for the preconditioning tuning curves only in B, C, and D.

Figure 5.

Temporal specificity of conditioning effects. A, Shifts in best frequency as a function of conditioning interval, normalized by tuning width (sample size ranged from n = 73 to n = 183 for each interval). Negative intervals correspond to negative conditioning and positive intervals to positive conditioning. Negative shifts mean shifts away from the NPF, positive ones shifts toward the NPF. B, Conditioning-dependent changes in tuning width and firing rate for the same population as in A. C, Best frequency shifts for a subpopulation of single units (n = 19–39 for different intervals). D, Best frequency shifts for a population of tuning curves recorded in two alert ferrets (n = 24–30). *p < 0.05, **p < 0.005, ***p < 0.0005, t test. Errors bars are ± one SEM.

Figure 6.

Average difference-tuning curves. Subtracting preconditioning from postconditioning tuning functions (see Materials and Methods) revealed the frequency regions where the response was potentiated or depressed by the conditioning procedures. A, Difference-tuning curves for positive (black, n = 311) and negative (gray, n = 273) conditioning with 8 and 12 ms intervals. While the NPF is known in each case, because the curves were aligned on this sound frequency, the PF marked on the x-axis represents the average value across all tuning curves. B, C, Difference-tuning curves for single-tone conditioning with an NPF or PF, respectively. The best frequency (BF) position marked on the x-axis represents the average BF across all curves. Errors bars are ± one SEM.

Figure 7.

Time scales of induction and persistence of plasticity. A, Shift in predicted direction as function of the number of conditioning pairs (n = 571). B, In the majority of experiments (n = 350), an additional, fifth, iso-intensity tuning curve was obtained ∼6 min after the last conditioning block. C, During some experiments another, sixth, tuning curve was obtained ∼12 min after the end of conditioning (n = 100). On average, tuning curve shifts persisted through the fifth mapping block but not the sixth. Only data from conditioning with 8 and 12 ms intervals were considered and the sign of the results from negative conditioning was reversed so that the data could be pooled and plotted as shifts in the predicted direction. *p < 0.05, **p < 0.005, ***p < 0.0005, t test. Errors bars are ± one SEM.

Figure 8.

Two factors affecting stimulus-timing-dependent plasticity. A, Best frequency shifts as a function of the frequency difference between the two stimuli in a conditioning pair (n = 54–326 for different frequency separations). B, Recordings were performed with 16-site electrodes inserted perpendicular to the cortical surface. C, Best frequency shifts as a function of recording depth. Mean shifts were calculated for pairs of recording sites, excluding sites 1 and 16 from which hardly any recordings were made, and plotted as function of recording depth and cortical layer (n = 26–110 for different depths). Only data from conditioning with 8 and 12 ms intervals were considered and the sign of the results from negative conditioning was reversed so that the data could be pooled and plotted as shifts in the predicted direction. The thickness of the cortical layers was measured at six locations (depicted by the red crosses in the inset; the small black crosses indicate the locations of the recording sites) over the surface of A1 for two representative ferrets. Colored bars on either side of the graph show the thickness of the cortical layers measured at the thinnest (left) and thickest (right) of those six positions. A1, Primary auditory cortex; AAF, anterior auditory field; D, dorsal; P, posterior. *p < 0.05, **p < 0.005, ***p < 0.0005, t test. Errors bars are ± one SEM.

Figure 9.

Dependence of stimulus-timing-dependent plasticity on tuning width, and on the firing rate and temporal precision of responses to conditioning stimuli. A, Shift size as a function of tuning curve width (n = 144–245). B, Shift size as a function of number of spikes fired during presentation of conditioning stimuli (n = 156–267). C, D, Spiking precision was measured using two features of the cross-correlation functions described in Figure 3: the difference between their peak position and the conditioning interval, and their widths. C, Shift size as a function of the difference between peak position and conditioning interval (n = 51–197). D, Shift size as a function of cross-correlation width (n = 28–139). Only data from conditioning with 8 and 12 ms intervals were considered and the sign of the results from negative conditioning was reversed so that the data could be pooled and plotted as shifts in the predicted direction. Errors bars are ± one SEM.

Figure 10.

Results from cross-correlation analysis. A, Cross-correlograms computed between recording sites separated by 0.1–0.9 mm on the same multisite recording probe, smoothed by 3 ms boxcar function. B, Correlation strength measured by the average height of the cross-correlogram peak as a function of recording site combination. C, Changes in correlation strength measured by the area under the cross-correlogram and its peak height as a function of conditioning interval for recording sites separated by up to 0.3 mm (n = 104–292 for different intervals). Errors bars are ± one SEM.