To Accompany: Thalamic Synchrony and the Adaptive Gating of Information Flow to Cortex Wang, Webber, & Stanley

SUPPLEMENTARY MATERIAL To Accompany: Thalamic Synchrony and the Adaptive Gating of Information Flow to Cortex Wang, Webber, & Stanley Supplementary Note 1: Parametric fits of spike count distributions The spike count distributions were parametrically fit. Although the spike count variance grew with the mean spike count, the fano factor deviated from unity for larger spike counts, inconsistent with a Poisson model. For the ideal observer analyses, spike count distributions were parameterized with a Gamma distribution Γ(α,Θ), where α is the shape parameter, and Θ is the scale parameter of the distribution. For the Gamma distribution, the mean (α*θ) and variance (α*θ 2 ) are not so strictly coupled as they are for the Poisson. For each recorded neuron, the parameters of this distribution were estimated from the observed mean spike count µ and spike count variance σ 2 as α = µ 2 /σ 2 and Θ = σ 2 /µ. This was repeated for each probe stimulus in the and Non adapted states. For the analyses, we assumed that the ideal observer (see Methods) performed the detection/discrimination task by pooling N neurons whose response properties are similar to the neuron recorded experimentally (Britten et al., 1992; Stuttgen and Schwarz, 28). We assumed that each neuron has the same Gamma distribution Γ(α,Θ). The sum of N neurons with the same Gamma distribution has the distribution of Γ(N*α,Θ). Note that since the fano factor of a Gamma distribution is Θ, the fano factor of the population spike count is also Θ, the same as the individual neurons in the group. For all analyses presented, an N value of 1 was utilized. See Supplementary Fig. 3a online for examples of the parametric fits for the detection paradigm, and Supplementary Fig. 3b online for examples of the parametric fits for the discrimination paradigm. To demonstrate that this approach captures the underlying distributions accurately, we repeated all analyses with different values of N (N=1, 5, 2), with no qualitative difference in the results. Supplementary Note 2: Control Analyses Detection. A control analysis was performed in the detection paradigm of Fig. 2e in the main text. To determine the effect of the spike count variance on the detection performance, we evaluated the hypothetical performance in both the Non adapted and states as if the neurons were to exhibit trial to trial spike count variance equal to the mean. For both states, the mean population spike count was held to that observed experimentally, but the trial to trial spike count variance was adjusted to match the mean (fano factor of 1). As shown on the right side in Fig. 2e (Control), the trend of degraded performance with adaptation was maintained in the control (p<1 2, n=3, Wilcoxon signed rank test), suggesting that the sub linear decrease in variance does little to offset the decrease in the mean spike count with adaptation. Discrimination. Two control analyses were performed in the discrimination paradigm of Fig. 3e in the main text. To confirm that the enhanced discrimination performance in the state was not caused by decreased variance associated with the decrease in the mean population spike count with adaptation, we measured the discrimination performance in two control cases Nature Neuroscience: doi:1.138/nn.267

in which the spike count was artificially adjusted in the Non adapted state according to the adaptation ratio (67%). The first control (Control1) was achieved by randomly removing 33% of the spikes from the experimentally measured response in the Non adapted state, resulting in a hypothetical sensitivity curve in the state that had the same shape as that in the Non adapted, but with a reduced magnitude that matched the experimentally observed variance in the state, consistent with a Poisson model of spiking. A second control (Control2) was performed in which the mean and variance of the parameterized Gamma distribution of the population spike count in the Non adapted state was scaled down to 67% to match the observed spike count reduction with adaptation. Neither control was statistically different from the Non adapted performance (p=.12 for Control1, and p=.25 for Control2, Wilcoxon signed rank test), showing that the change in performance with adaptation observed in the data was not a trivial consequence of the attenuated response in the state, but instead relied on the non uniform changes in the separation between the distributions with adaptation. Supplementary Note 3: Adaptation effects on discriminability of cortical angular tuning Angular Tuning. In a small set of cortical recordings (n=9 RSUs), the effects of adaptation on discriminability between angle of vibrissa deflection was tested. The same Adapt/Non adapt paradigm was utilized as throughout the rest of the manuscript, but the probe stimulus consisted of a high angular velocity deflection in one of 4 directions/angles using a 2 DOF piezoelectric actuator (Simons, 1983). As previously reported (Khatri and Simons, 27), we found that the angular tuning for the cortical neurons sharpened with adaptation, as captured by the increase in vector strength after adaptation (a measure of sharpness, Supplementary Fig. 5c online). Furthermore, we also found that as with angular velocity in the rest of our study, the discriminability between stimuli presented at these 4 cardinal directions was enhanced with adaptation (n=9 cortical RSUs, Supplementary Fig. 5a b online). The shift in discrimination performance with adaptation for a different stimulus feature suggests that our findings are general, and not just related to the specifics of this pathway or this particular parameter of the stimulus space (velocity) for this pathway. Supplementary Note 4: Correlation and Synchrony Correlation Analysis. As described in the main text, a subset of thalamocortical pairs was identified as likely to be monosynaptically connected through cross correlation analysis. Each pair was weakly driven with a 7 µm amplitude (at ~12 mm from face) 4 Hz sinusoidal deflection of the principal whisker (Bruno and Simons, 22; Bruno and Sakmann, 26). To collect enough spikes for a robust correlation analysis, recordings were taken for >2 minutes. For identified monosynaptically connected pairs, the mean number of thalamic spikes was 322, and the mean number of cortical spikes was 2167, which was found to be sufficient for the analysis. The cross correlation between a VPm neuron and the cortical neuron was computed in the following manner. Given an observed spike of the VPm neuron, the cross correlation was generated as a histogram of the cortical spike times relative to the VPm spike time, normalized by the number of VPm spikes. The binsize used in all correlation analyses was 1 Nature Neuroscience: doi:1.138/nn.267

ms. A thalamocortical pair was identified as monosynaptically connected based on a sharp peak in the cross correlogram at a very short (~2 ms) latency (Swadlow and Gusev, 21; Bruno and Simons, 22). Due to the high degree of reported connectivity between single neurons in a VPm barreloid and single neurons in the corresponding layer 4 barrel (Bruno and Sakmann, 26), it is likely that more of the pairs used in these analyses were monosynaptically connected than those conservatively reported here. In addition to using cross correlation analysis to assess connectivity, cross correlation analysis was also used for stimulus evoked activity to assess the synchrony across thalamocortical pairs, and the effects of adaptation on this relationship. The cross correlogram was constructed in the same manner as above, and the synchrony was defined as the central area under the cross correlogram within a synchrony window (Temereanca et al., 28): NCC Synchrony = 2 2 ( N + N ) / 2 where N CC is the total number of correlated events within a given synchrony window (+/ 7.5 ms in Fig. 6a, main text) in the raw cross correlogram; N ref and N target are the number of spikes of the reference VPm cell and the target VPm cell, respectively, that were used to calculate the cross correlogram. The shuffled cross correlogram was also generated for comparison (Bruno and Simons, 22). The shuffle-corrected cross-correlogram is shown in Supplementary Fig. 9 online. The synchrony as a function of time in response to the repetitive stimulus was also calculated for synchrony windows of +/- 2 ms and +/- 2 ms, showing quantitative but not qualitative differences from the results using the +/ 7.5 ms synchrony window (Supplementary Fig. 1 online). ref target Supplementary Note 5: Lemniscal and Paralemniscal Pathways It is important to note that it has been proposed that the lemniscal and paralemniscal pathways separately process vibrissa motion in parallel for texture coding and object contact, respectively (Ahissar et al., 2). The paralemniscal pathway is associated with whisking motions in active touch, projecting though the POm region of the thalamus, in contrast to the lemniscal pathway that is associated with both active whisking and passive contact, projecting through the dorsomedial region of the VPm. The thalamic data shown here were identified as VPm in origin, based on stereo taxic coordinates and response latencies (as described in Methods). However, the existence of a different ( extra ) lemniscal pathway through the ventrolateral region of VPm has recently been identified (Pierret et al., 2; Yu et al., 26). Although the two regions are quite close in proximity, making it difficult to target the dorsal or ventral regions specifically, the VPm neurons that were recorded simultaneously with a monosynaptically connected recipient layer 4 cortical neuron could be identified as lemniscal in origin. The remaining VPm neurons were not significantly different in terms of their firing activity and other measures, and thus were also likely located in the lemniscal tract. Nature Neuroscience: doi:1.138/nn.267

REFERENCES Ahissar E, Sosnik R, Haidarliu S (2) Transformation from temporal to rate coding in a somatosensory thalamocortical pathway. Nature 46:32-36. Britten KH, Shadlen MN, Newsome WT, Movshon JA (1992) The analysis of visual motion: a comparison of neuronal and psychophysical performance. J Neurosci 12:4745-4765. Bruno RM, Simons DJ (22) Feedforward Mechanisms of Excitatory and Inhibitory Cortical Receptive Fields. J Neurosci 22:1966-1975. Bruno RM, Sakmann B (26) Cortex Is Driven by Weak but Synchronously Active Thalamocortical Synapses. Science 312:1622-1627. Khatri V, Simons DJ (27) Angularly Nonspecific Response Suppression in Rat Barrel Cortex. Cereb Cortex 17:599-69. Pierret T, Lavallee P, Deschenes M (2) Parallel Streams for the Relay of Vibrissal Information through Thalamic Barreloids. J Neurosci 2:7455-7462. Simons DJ (1983) Multi-whisker stimulation and its effects on vibrissa units in rat SmI barrel cortex. Brain Res 276:178-182. Stuttgen MC, Schwarz C (28) Psychophysical and neurometric detection performance under stimulus uncertainty. Nat Neurosci 11:191-199. Swadlow HA, Gusev AG (21) The impact of 'bursting' thalamic impulses at a neocortical synapse. Nat Neurosci 4:42-48. Temereanca S, Brown EN, Simons DJ (28) Rapid Changes in Thalamic Firing Synchrony during Repetitive Whisker Stimulation. J Neurosci 28:11153-11164. Yu C, Derdikman D, Haidarliu S, Ahissar E (26) Parallel Thalamic Pathways for Whisking and Touch Signals in the Rat. PLoS Biol 4:e124. Nature Neuroscience: doi:1.138/nn.267

Cortical regular spiking units (RSUs) 1 µv 5 spks per s 2 µs Lag (ms) Supplementary Figure 1. Spike waveforms for cortical regular spiking units (RSUs). Shown for each of the cortical neurons of the main analysis (n=3) are the spike waveform (left) and the auto correlogram of spiking activity (right). For the spike waveform, the thick solid curve represents the mean and the lighter band represents the standard deviation. The auto correlogram was calculated from all corresponding spikes of the unit (binsize =.25 ms), and normalized by the total number of spikes and the binsize. Nature Neuroscience: doi:1.138/nn.267

1.6 1.2 Poisson Variance.8.4.4.8 1.2 1.6 Mean spike count Supplementary Figure 2. Relationship between spike count mean and variance is similar in Non adapted and states. Shown are the data in Fig. 1 in the main text, separated by whether the measurements were made in the Non adapted (black, open diamond) or (gray, filled circle) state. Exponential fits to the mean/variance relationship were similar for each case (fits were obtained using least-squares algorithm). Nature Neuroscience: doi:1.138/nn.267

a 1. 1..5 Noise.5 Noise Probability 1..5 1..5 Noise s1 s1 1..5 1..5 s1 Noise s1 4 8 Population spike count 4 8 b.6.4.4 s1.2 s1.2.4.4.2 s2.2 s2.4.4 Probability.2.4 s3.2.4 s3.2 s4.2 s4.4.4.2 s5.2 s5.6.6 s1.4.2 s1 s2 s3 s4 s5.4.2 s2 s3 s4 s5 5 1 15 2 25 Population spike count 5 1 15 2 25 Population spike count Supplementary Figure 3. Examples of parametric fits of Gamma distribution to population spike count. a. Shown are examples of cortical population spike count histograms and the corresponding parametric Gamma distribution fits (dashed curves) in the Non adapted (left) and (right) states (see Methods), for the Detection task (see Fig. 2 main text). b. Shown are examples of cortical population spike count histograms and the corresponding parametric Gamma distribution fits (dashed curves) in the Non adapted (left) and (right) states (see Methods), for the Discrimination task (see Fig. 3 main text). Each row represents a different vibrissa deflection Nature Neuroscience: velocity, denoted doi:1.138/nn.267 as s1 through s5.

p(r s1) p(r s2) p(r s3) p(r s4) p(r s5) Probablity of choosing s1, given s3 Probablity of choosing s5, given s3 Probablity of choosing s2, given s3 Probablity of choosing s3, given s3 Probablity of choosing s4, given s3 Supplementary Figure 4. Theoretical performance of the Bayesian decoder. Given the likelihood functions, the theoretical performance of the ideal observer using the Bayesian decoding strategy was determined when the stimuli were presented with equal probability. The probability that the Bayesian decoder chooses si when the stimulus presented was actually s3 is the area of the region of p(r s3 ) in which the p(r si) is maximal. Nature Neuroscience: doi:1.138/nn.267

a b Discrim. performance ** p=.19 1 spks per s 2 ms.5 12 dorsal 9 6.4.3 Non- 15 3.2 Nonadapted 18 21 33 1.5 sp rostral c 4 Vector strength ** p<.39 24 27 3 3 2 1 Nonadapted Supplementary Figure 5. Adaptation enhances discriminability between deflections at different angles. In a separate set of experiments, cortical RSUs were recorded from in response to a stimulus protocol identical to that in Fig. 3 in the main text, except that the probe stimulus consisted of a single punctate deflection of the vibrissa in one of 4 directions (rostral, dorsal, caudal, or ventral). a. The PSTH is shown for an example single unit in response to a probe deflection in each of the 4 cardinal directions, following the absence (Non adapted) or presence () of a 12 Hz periodic stimulus train in the rostral caudal plane. The central polar plot shows the spike count in the 3 ms window following the probe stimulus as a function of angle, for the Non adapted (dotted) and (solid) cases. b. As for velocity in Fig. 3 in the main text, an ideal observer was challenged with discriminating between the 4 directions of motion, showing a significant increase in discriminability in the as compared to Non adapted case (n=9 RSUs). c. Vector strength expresses the degree of angle selectivity. Consistent with previous findings (Khatri & Simons, 27), the vector strength was significantly increased with adaptation, the source of the enhancement in discriminability in b. See Supplementary Note 3. Nature Neuroscience: doi:1.138/nn.267

Thalamic VPm units 3 µv 2 µs 15 spks per s Lag (ms) Example thalamic units recorded by multi-electrode array Supplementary Figure 6. Spike waveforms for thalamic VPm units. Shown for each of the VPm neurons of the main analysis (n=32) are the spike waveform (left) and the auto correlogram of spiking activity (right). For the spike waveform, the thick solid curve represents the mean and the lighter band represents the standard deviation. The auto correlogram was calculated from all corresponding spikes of the unit (binsize =.25 ms), and normalized by the total number of spikes and the binsize. Nature Neuroscience: doi:1.138/nn.267

VPm 1 ** p <.1 Detection performance (AUROC).9.8 Nonadapted Supplementary Figure 7. Thalamic detection performance. The detection performance of the ideal observer of VPm spike count was better in the Non adapted as compared to state. Nature Neuroscience: doi:1.138/nn.267

a Normalized spike count response 1..8.6.4.2 VPm Experimental s3 s4 s5 2 4 6 8 1, 1,2 s1 s2 Velocity (deg s -1 ) b Normalized spike count response 1..8.6.4 CTX Experimental.2 s3 s4 s5 2 4 6 8 1, 1,2 Velocity (deg s s1 s2-1 ) c Simulated d Simulated Normalized spike count response 1..8.6.4.2 Using non-adapted VPm spikes s3 s4 s5 Normalized spike count response 2 4 6 8 1, 1,2 2 4 6 8 1, 1,2 s1 s2 Velocity (deg s -1 ) s1 s2 Velocity (deg s -1 ) 1..8.6.4.2 s3 s4 s5 Supplementary Figure 8. Adaptation fundamentally reshapes cortical but not thalamic sensitivity. a. Average normalized velocity sensitivity curves for the sample of VPm neurons in the Non adapted (dotted) and (solid) states (n=32 neurons). b. Average normalized velocity sensitivity curves for the sample of cortical RSUs in the Non adapted (dotted) and (solid) states (n=3 neurons). c. Sensitivity curve of simulated cortical neural response. Dotted curve shows the Non adapted response simulated from the experimentally observed Non adapted VPm spikes. The solid curve shows the cortical response simulated using the VPm input whose spike count matched that observed experimentally, but with a synchrony forced to that of the Non adapted state (see Results). d. Sensitivity curve of simulated cortical neural response. Dotted curve shows the Non adapted response simulated from the experimentally observed Non adapted VPm spikes. The solid curve shows the cortical response simulated using the actual VPm input. Error bars are +/ 1 SEM. Nature Neuroscience: doi:1.138/nn.267

Shuffle-corrected synchrony VPm.2.1 s3 s4 s5 2 4 6 8 1, 1,2 s1 s2 Velocity (deg s -1 ) Supplementary Figure 9. Shuffle corrected VPm synchrony measurements. Shown is the mean velocity dependence of the synchrony measured from the shuffle corrected cross correlogram (n=19 pairs, Non adapted (dotted), (solid) see Methods). Slopes were not significantly different from zero in either case (p=.32 for Non adapted, p=.35 for ), reflecting a lack of dependence of the noise correlations on angular velocity. Nature Neuroscience: doi:1.138/nn.267

a Adapting stimulus b Adapting stimulus Synchrony using +/- 2 ms synchrony window.4.2 83 ms Synchrony using +/- 2 ms synchrony window.8.4 83 ms 1 5 1 15 1 5 1 15 Pulse number Pulse number Supplementary Figure 1. Thalamic synchrony as a function of time. a. For VPm pairs recorded simultaneously, the adaptation served to reduce the timing precision across neurons, or desynchronize their firing activity (n=19 pairs). Synchrony was measured as the central area under the crosscorrelogram (+/- 2 ms synchrony window). b. Same as in a, with synchrony window of +/- 2 ms. In both cases, adaptation desynchronizes the thalamic activity, in a manner qualitatively similar to that shown for +/- 7.5 ms synchrony window in main text. Nature Neuroscience: doi:1.138/nn.267