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• W2002246101 abstract "Current views of sensory adaptation in the rat somatosensory system suggest that it results mainly from short-term synaptic depression. Experimental and theoretical studies predict that increasing the intensity of sensory stimulation, followed by an increase in firing probability at early sensory stages, is expected to attenuate the response at later stages disproportionately more than weaker stimuli, due to greater depletion of synaptic resources and the relatively slow recovery process. This may lead to coding ambiguity of stimulus intensity during adaptation. In contrast, we found that increasing the intensity of repetitive whisker stimulation entails less adaptation in cortical neurons. In a series of recordings, from the trigeminal ganglion to the thalamus, we pinpointed the source of the unexpected pattern of adaptation to the brainstem trigeminal complex. We suggest that low-level sensory processing counterbalances later effects of short-term synaptic depression by increasing the throughput of high-intensity sensory inputs Current views of sensory adaptation in the rat somatosensory system suggest that it results mainly from short-term synaptic depression. Experimental and theoretical studies predict that increasing the intensity of sensory stimulation, followed by an increase in firing probability at early sensory stages, is expected to attenuate the response at later stages disproportionately more than weaker stimuli, due to greater depletion of synaptic resources and the relatively slow recovery process. This may lead to coding ambiguity of stimulus intensity during adaptation. In contrast, we found that increasing the intensity of repetitive whisker stimulation entails less adaptation in cortical neurons. In a series of recordings, from the trigeminal ganglion to the thalamus, we pinpointed the source of the unexpected pattern of adaptation to the brainstem trigeminal complex. We suggest that low-level sensory processing counterbalances later effects of short-term synaptic depression by increasing the throughput of high-intensity sensory inputs Synaptic depression models suggest more adaptation for stronger sensory stimuli We find that in the whisker pathway strong stimuli adapt less than weaker stimuli The observed adaptation behavior cannot be explained by synaptic depression Intensity-dependent adaptation originates at the brainstem and may prevent ambiguity In all sensory modalities, elevation in stimulation intensity usually causes a transient increase in firing rate followed by a slow decline toward a lower level. The functional role of this adaptation process is not clear, but it has been hypothesized to serve several different roles (Wark et al., 2007Wark B. Lundstrom B.N. Fairhall A. Sensory adaptation.Curr. Opin. Neurobiol. 2007; 17: 423-429Crossref PubMed Scopus (308) Google Scholar). Adaptation alters the sensitivity of neuronal circuits to match the prevailing conditions in order to efficiently encode sensory stimuli (Adorján et al., 1999Adorján P. Piepenbrock C. Obermayer K. Contrast adaptation and infomax in visual cortical neurons.Rev. Neurosci. 1999; 10: 181-200Crossref PubMed Scopus (30) Google Scholar, Brenner et al., 2000Brenner N. Bialek W. de Ruyter van Steveninck R. Adaptive rescaling maximizes information transmission.Neuron. 2000; 26: 695-702Abstract Full Text Full Text PDF PubMed Scopus (454) Google Scholar, Fairhall et al., 2001Fairhall A.L. Lewen G.D. Bialek W. de Ruyter Van Steveninck R.R. Efficiency and ambiguity in an adaptive neural code.Nature. 2001; 412: 787-792Crossref PubMed Scopus (541) Google Scholar, Maravall et al., 2007Maravall M. Petersen R.S. Fairhall A.L. Arabzadeh E. Diamond M.E. Shifts in coding properties and maintenance of information transmission during adaptation in barrel cortex.PLoS Biol. 2007; 5: e19Crossref PubMed Scopus (176) Google Scholar, Müller et al., 1999Müller J.R. Metha A.B. Krauskopf J. Lennie P. Rapid adaptation in visual cortex to the structure of images.Science. 1999; 285: 1405-1408Crossref PubMed Scopus (334) Google Scholar, Sharpee et al., 2006Sharpee T.O. Sugihara H. Kurgansky A.V. Rebrik S.P. Stryker M.P. Miller K.D. Adaptive filtering enhances information transmission in visual cortex.Nature. 2006; 439: 936-942Crossref PubMed Scopus (227) Google Scholar). Adaptation may also improve the detectability of rare stimuli by suppressing responses to frequent stimuli while enhancing or leaving responses to novel stimuli unchanged (Dragoi et al., 2002Dragoi V. Sharma J. Miller E.K. Sur M. Dynamics of neuronal sensitivity in visual cortex and local feature discrimination.Nat. Neurosci. 2002; 5: 883-891Crossref PubMed Scopus (156) Google Scholar, Ulanovsky et al., 2003Ulanovsky N. Las L. Nelken I. Processing of low-probability sounds by cortical neurons.Nat. Neurosci. 2003; 6: 391-398Crossref PubMed Scopus (677) Google Scholar). Other studies have hypothesized that adaptation mediates predictive coding (Lundstrom et al., 2008Lundstrom B.N. Higgs M.H. Spain W.J. Fairhall A.L. Fractional differentiation by neocortical pyramidal neurons.Nat. Neurosci. 2008; 11: 1335-1342Crossref PubMed Scopus (420) Google Scholar), enabling neural responses to precede temporally correlated sensory inputs. Several neural mechanisms are involved in sensory adaptation (Kohn, 2007Kohn A. Visual adaptation: physiology, mechanisms, and functional benefits.J. Neurophysiol. 2007; 97: 3155-3164Crossref PubMed Scopus (485) Google Scholar), including intrinsic (Carandini and Ferster, 1997Carandini M. Ferster D. A tonic hyperpolarization underlying contrast adaptation in cat visual cortex.Science. 1997; 276: 949-952Crossref PubMed Scopus (278) Google Scholar, Díaz-Quesada and Maravall, 2008Díaz-Quesada M. Maravall M. Intrinsic mechanisms for adaptive gain rescaling in barrel cortex.J. Neurosci. 2008; 28: 696-710Crossref PubMed Scopus (37) Google Scholar, Sanchez-Vives et al., 2000aSanchez-Vives M.V. Nowak L.G. McCormick D.A. Cellular mechanisms of long-lasting adaptation in visual cortical neurons in vitro.J. Neurosci. 2000; 20: 4286-4299PubMed Google Scholar, Sanchez-Vives et al., 2000bSanchez-Vives M.V. Nowak L.G. McCormick D.A. Membrane mechanisms underlying contrast adaptation in cat area 17 in vivo.J. Neurosci. 2000; 20: 4267-4285PubMed Google Scholar) and inhibitory mechanisms (Barlow, 1990Barlow H.B. A Theory about the Functional Role and Synaptic Mechanism of Visual After-Effects.in: Blackmor C. Vision: Coding and Efficiency. Cambridge University Press, Cambridge1990: 363-375Google Scholar, Carvalho and Buonomano, 2009Carvalho T.P. Buonomano D.V. Differential effects of excitatory and inhibitory plasticity on synaptically driven neuronal input-output functions.Neuron. 2009; 61: 774-785Abstract Full Text Full Text PDF PubMed Scopus (64) Google Scholar, Chelaru and Dragoi, 2008Chelaru M.I. Dragoi V. Asymmetric synaptic depression in cortical networks.Cereb. Cortex. 2008; 18: 771-788Crossref PubMed Scopus (23) Google Scholar, Wainwright et al., 2002Wainwright M.J. Schwartz O. Simoncelli E.P. Natural Image Statistics and Divisive Normalization: Modeling Nonlinearities and Adaptation in Cortical Neurons. MIT Press, Cambridge, MA2002Google Scholar). Synaptic depression in particular has been suggested to play a major role in adaptation of sensory inputs in different modalities, including the visual (Chance et al., 1998Chance F.S. Nelson S.B. Abbott L.F. Synaptic depression and the temporal response characteristics of V1 cells.J. Neurosci. 1998; 18: 4785-4799PubMed Google Scholar), auditory (Wehr and Zador, 2005Wehr M. Zador A.M. Synaptic mechanisms of forward suppression in rat auditory cortex.Neuron. 2005; 47: 437-445Abstract Full Text Full Text PDF PubMed Scopus (296) Google Scholar), and somatosensory (Chung et al., 2002Chung S. Li X. Nelson S.B. Short-term depression at thalamocortical synapses contributes to rapid adaptation of cortical sensory responses in vivo.Neuron. 2002; 34: 437-446Abstract Full Text Full Text PDF PubMed Scopus (376) Google Scholar, Heiss et al., 2008Heiss J.E. Katz Y. Ganmor E. Lampl I. Shift in the balance between excitation and inhibition during sensory adaptation of S1 neurons.J. Neurosci. 2008; 28: 13320-13330Crossref PubMed Scopus (83) Google Scholar, Katz et al., 2006Katz Y. Heiss J.E. Lampl I. Cross-whisker adaptation of neurons in the rat barrel cortex.J. Neurosci. 2006; 26: 13363-13372Crossref PubMed Scopus (76) Google Scholar, Khatri et al., 2004Khatri V. Hartings J.A. Simons D.J. Adaptation in thalamic barreloid and cortical barrel neurons to periodic whisker deflections varying in frequency and velocity.J. Neurophysiol. 2004; 92: 3244-3254Crossref PubMed Scopus (99) Google Scholar) systems (for review see Zucker and Regehr, 2002Zucker R.S. Regehr W.G. Short-term synaptic plasticity.Annu. Rev. Physiol. 2002; 64: 355-405Crossref PubMed Scopus (2875) Google Scholar). In the rat somatosensory system, stimuli delivered to the facial vibrissae are conveyed to layer 4 of the primary somatosensory cortex via three synapses (Petersen, 2003Petersen C.C. The barrel cortex—integrating molecular, cellular and systems physiology.Pflugers Arch. 2003; 447: 126-134Crossref PubMed Scopus (43) Google Scholar) and through at least four parallel pathways (Urbain and Deschênes, 2007Urbain N. Deschênes M. A new thalamic pathway of vibrissal information modulated by the motor cortex.J. Neurosci. 2007; 27: 12407-12412Crossref PubMed Scopus (53) Google Scholar, Yu et al., 2006Yu C. Derdikman D. Haidarliu S. Ahissar E. Parallel thalamic pathways for whisking and touch signals in the rat.PLoS Biol. 2006; 4: e124Crossref PubMed Scopus (164) Google Scholar). The lemniscal pathway, which provides powerful short latency input to the primary somatosensory cortex, originates in afferent fibers that innervate whisker follicles and synapse onto neurons in the principalis nucleus of the brainstem trigeminal complex (PrV). These PrV neurons send axons to the ventral posterior medial nucleus of the thalamus (VPM), which in turn delivers sensory input to layer 4 cortical cells. Several studies suggested a major role for synaptic depression in layer 4 adaptation (Chung et al., 2002Chung S. Li X. Nelson S.B. Short-term depression at thalamocortical synapses contributes to rapid adaptation of cortical sensory responses in vivo.Neuron. 2002; 34: 437-446Abstract Full Text Full Text PDF PubMed Scopus (376) Google Scholar, Gabernet et al., 2005Gabernet L. Jadhav S.P. Feldman D.E. Carandini M. Scanziani M. Somatosensory integration controlled by dynamic thalamocortical feed-forward inhibition.Neuron. 2005; 48: 315-327Abstract Full Text Full Text PDF PubMed Scopus (412) Google Scholar, Heiss et al., 2008Heiss J.E. Katz Y. Ganmor E. Lampl I. Shift in the balance between excitation and inhibition during sensory adaptation of S1 neurons.J. Neurosci. 2008; 28: 13320-13330Crossref PubMed Scopus (83) Google Scholar, Katz et al., 2006Katz Y. Heiss J.E. Lampl I. Cross-whisker adaptation of neurons in the rat barrel cortex.J. Neurosci. 2006; 26: 13363-13372Crossref PubMed Scopus (76) Google Scholar, Khatri et al., 2004Khatri V. Hartings J.A. Simons D.J. Adaptation in thalamic barreloid and cortical barrel neurons to periodic whisker deflections varying in frequency and velocity.J. Neurophysiol. 2004; 92: 3244-3254Crossref PubMed Scopus (99) Google Scholar). Less is known about adaptation at earlier stages of the somatosensory system, although studies of VPM also suggest a role for synaptic depression in thalamic adaptation (Castro-Alamancos, 2002bCastro-Alamancos M.A. Properties of primary sensory (lemniscal) synapses in the ventrobasal thalamus and the relay of high-frequency sensory inputs.J. Neurophysiol. 2002; 87: 946-953PubMed Google Scholar, Deschênes et al., 2003Deschênes M. Timofeeva E. Lavallée P. The relay of high-frequency sensory signals in the Whisker-to-barreloid pathway.J. Neurosci. 2003; 23: 6778-6787PubMed Google Scholar). Studies of short-term synaptic depression led to highly successful mathematical descriptions of this phenomenon (Tsodyks and Markram, 1997Tsodyks M.V. Markram H. The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability.Proc. Natl. Acad. Sci. USA. 1997; 94: 719-723Crossref PubMed Scopus (1070) Google Scholar). Assuming adaptation in the lemniscal pathway results mostly from synaptic depression gives rise to several interesting predictions. First, these models predict that an increase in stimulus intensity, which is followed by higher firing probability at early sensory stages, will raise the effective frequency of stimulation, resulting in greater depression during repetitive stimulation due to depletion of synaptic resources and the relatively slow recovery processes (see Figure 1A and Supplemental Experimental Procedures for details). In addition, during adaptation, responses to low-intensity stimuli may actually surpass those to higher-intensity stimuli. This may prevent the decoding of stimulus intensity from response magnitude, leading to coding ambiguity during adaptation (Figure 1B). We tested these hypotheses in vivo, in cortical, thalamic, brainstem, and first-order sensory stages and found that our results do not agree with the predictions of short-term synaptic depression models, suggesting that other mechanisms act to counterbalance the effects of synaptic depression in early sensory processing. Models of short-term synaptic depression predict that increasing stimulation intensity will result in greater response adaptation, which may lead to ambiguity in the coding of stimulus intensity during adaptation (Figures 1A and 1B; see Introduction for more details). We tested this hypothesis in the rat barrel cortex, whose inputs are thought to adapt mainly due to short-term synaptic depression. To that end, we applied repetitive whisker stimulation at frequencies within the natural whisking range when performing object discrimination tasks (Berg and Kleinfeld, 2003Berg R.W. Kleinfeld D. Rhythmic whisking by rat: retraction as well as protraction of the vibrissae is under active muscular control.J. Neurophysiol. 2003; 89: 104-117Crossref PubMed Scopus (278) Google Scholar) (18 Hz, 20 stimuli). Stimuli were delivered at two very different intensities while recording intracellularly from cortical cells located within or having dendritic innervations in layer 4 and presumably receive direct thalamic input (n = 7; see Figures 1C and 1D for example neuron). The high-intensity stimulation was set to the maximal deflection of the stimulator, and the weak stimulation was selected online such that it evoked a significantly smaller response. The exact amplitude and velocity of the weak stimulation was different across the population, since a sharp drop in response probability was observed at very different stimulation intensities. This could be due to the fact that different whiskers were used in this study and the different angular tuning properties of the recorded neurons (Andermann and Moore, 2006Andermann M.L. Moore C.I. A somatotopic map of vibrissa motion direction within a barrel column.Nat. Neurosci. 2006; 9: 543-551Crossref PubMed Scopus (104) Google Scholar, Kerr et al., 2007Kerr J.N. de Kock C.P. Greenberg D.S. Bruno R.M. Sakmann B. Helmchen F. Spatial organization of neuronal population responses in layer 2/3 of rat barrel cortex.J. Neurosci. 2007; 27: 13316-13328Crossref PubMed Scopus (183) Google Scholar). Across all recordings, we verified that the response probability evoked by the first stimulus for the weak stimulation was at least 10% smaller than for the strong stimulus. Adaptation of cortical cells was quantified by the peak average subthreshold response for each stimulus divided by the peak of the first response, providing a peak response adaptation ratio (Figure 1F). We also calculated the F1 index (responsiveness index; see Supplemental Experimental Procedures), which measures the ratio of the power at the stimulation frequency of the recorded response and that of an ideal nonadapted response (<1 corresponding to adaptation, >1 corresponding to facilitation; Figure 1F, inset). In contrast to the short-term synaptic depression model, we found that not only does the voltage response to high-intensity stimulus not adapt more than in the low-intensity condition, it actually adapts slightly less than the response to weaker stimulation (Figures 1E and 1F). In six out of seven cells, the F1 index of the response to high-intensity stimulus was higher than that to low-intensity stimulus (p < 0.02, one-sided two-sample Wilcoxon signed-rank test). Importantly, at these stimulation conditions, we observe no intersection of the response curves, as may occur theoretically (Figure 1B), avoiding possible coding ambiguity and allowing for stimulus intensity to be decoded from response magnitude even during adaptation. However, the amplitude of the voltage response can be affected by inhibition (Heiss et al., 2008Heiss J.E. Katz Y. Ganmor E. Lampl I. Shift in the balance between excitation and inhibition during sensory adaptation of S1 neurons.J. Neurosci. 2008; 28: 13320-13330Crossref PubMed Scopus (83) Google Scholar, Moore and Nelson, 1998Moore C.I. Nelson S.B. Spatio-temporal subthreshold receptive fields in the vibrissa representation of rat primary somatosensory cortex.J. Neurophysiol. 1998; 80: 2882-2892PubMed Google Scholar), which may mask the actual adaptation of the excitatory input to the cell. To overcome this, we used conductance estimates (see Experimental Procedures). When considering only the estimated excitatory inputs to these cells, we found similar results (Figures 1G and 1H), suggesting that the excitatory input to cortical cells that presumably receive direct thalamic input adapts less in response to high-intensity stimuli than in the case of low-intensity stimuli. The above results indicate that our initial hypothesis does not hold in layer 4 of the primary somatosensory cortex, and thus sensory adaptation cannot be fully explained by short-term plasticity models. This intriguing outcome led us to seek the source of this phenomenon subcortically. Could our observations in the cortex be explained at the first stage of neuronal encoding of whisker mechanical movements? To examine this possibility, we measured the peripheral firing responses to whisker stimulation prior to the recruitment of any synapses. We recorded the responses of single units in the trigeminal ganglion (TG, n = 13). These neurons are the first-order sensory neurons in the vibrissal pathway and innervate the whisker follicles directly. Although TG neurons respond with high temporal precision (Arabzadeh et al., 2006Arabzadeh E. Panzeri S. Diamond M.E. Deciphering the spike train of a sensory neuron: counts and temporal patterns in the rat whisker pathway.J. Neurosci. 2006; 26: 9216-9226Crossref PubMed Scopus (122) Google Scholar, Gottschaldt and Vahle-Hinz, 1981Gottschaldt K.M. Vahle-Hinz C. Merkel cell receptors: structure and transducer function.Science. 1981; 214: 183-186Crossref PubMed Scopus (160) Google Scholar, Jones et al., 2004Jones L.M. Lee S.H. Trageser J.C. Simons D.J. Keller A. Precise temporal responses in whisker trigeminal neurons.J. Physiol. 2004; 92: 665-668Google Scholar, Shoykhet et al., 2000Shoykhet M. Doherty D. Simons D.J. Coding of deflection velocity and amplitude by whisker primary afferent neurons: implications for higher level processing.Somatosens. Mot. Res. 2000; 17: 171-180Crossref PubMed Scopus (132) Google Scholar), we verified that arbitrary choice of bin size does not affect our results by quantifying adaptation at the two extremes of temporal precision. On the one hand, we measured the ratio of the population PSTH peak response to each stimulus in the train and that of the first stimulus at 1ms accuracy, which is approximately the jitter of single responses (peak response adaptation ratio, Figure 2B ). On the other hand, we compared the total number of spikes evoked by each stimulus to that evoked by the first stimulus, regardless of exact spike timing (spike count adaptation ratio; Figure 2D). The population PSTH is commonly used as an estimate for the input to downstream layers (Sarid et al., 2007Sarid L. Bruno R. Sakmann B. Segev I. Feldmeyer D. Modeling a layer 4-to-layer 2/3 module of a single column in rat neocortex: interweaving in vitro and in vivo experimental observations.Proc. Natl. Acad. Sci. USA. 2007; 104: 16353-16358Crossref PubMed Scopus (62) Google Scholar); therefore, these measures serve as a proxy for the input to downstream sensory processing stations. These data verify that stronger stimuli result in higher firing probability (Figure 2A) and a greater number of spikes per stimulus (Figure 2C). We found that responses of TG cells to repetitive stimulation adapt much less than cortical responses. Since no synapses are involved in the sensory processing at the TG stage, we expect no difference in the adaptation patterns evoked by the two stimulus conditions. The peak response adaptation ratio pointed toward slightly less adaptation for the weak stimulation (Figure 2B). This difference proved statistically significant when comparing the steady-state response (taken to be the average response to the last five stimuli) in the two conditions (p < 0.01, n = 13, two-sided two-sample Wilcoxon signed-rank test). The spike count adaptation ratio showed no difference between the two conditions (Figure 2D). Since more spikes are evoked per whisker deflection by the high-intensity stimulus and given that no major difference was found in the adaptation pattern for the two intensities, neurons downstream to the trigeminal nucleus should adapt more in response to high-intensity stimulation, due to synaptic depression. The lack of adaptation in first-order TG cells and the clear discrepancy between the predictions of the classical short-term synaptic plasticity model and the adaptation patterns observed in cortex led us to investigate the direct feed-forward thalamic input to cortical layer 4 in order to shed light on the source of these unexpected adaptation patterns. Previous studies showed that the VPM provides the major subcortical input to layer 4 cells in the barrel cortex (Bruno and Sakmann, 2006Bruno R.M. Sakmann B. Cortex is driven by weak but synchronously active thalamocortical synapses.Science. 2006; 312: 1622-1627Crossref PubMed Scopus (488) Google Scholar, Gil et al., 1999Gil Z. Connors B.W. Amitai Y. Efficacy of thalamocortical and intracortical synaptic connections: quanta, innervation, and reliability.Neuron. 1999; 23: 385-397Abstract Full Text Full Text PDF PubMed Scopus (272) Google Scholar, Pierret et al., 2000Pierret T. Lavallée P. Deschênes M. Parallel streams for the relay of vibrissal information through thalamic barreloids.J. Neurosci. 2000; 20: 7455-7462PubMed Google Scholar). Similar to the cortex, adaptation of VPM responses is believed to result from synaptic depression (Deschênes et al., 2003Deschênes M. Timofeeva E. Lavallée P. The relay of high-frequency sensory signals in the Whisker-to-barreloid pathway.J. Neurosci. 2003; 23: 6778-6787PubMed Google Scholar). Therefore, as previously explained, we expected that thalamic cells will adapt more in response to high-intensity stimulation, compared to the low-intensity condition. Using high-impedance glass electrodes (see Experimental Procedures), we recorded extracellularly the spikes of single VPM neurons responding to strong and weak repetitive stimulation (n = 32). Clearly, and even more pronounced than in cortex, the responses to high-intensity stimulation adapted significantly less than the responses in the low-intensity condition (Figures 3A–3D , p < 10−10 for the steady-state response of both PSTH peak and spike-count ratio, one-sided two-sample Wilcoxon signed-rank test). Considering the sensory responses of TG neurons, these results seem to contradict the assumption that adaptation of VPM responses is purely a result of synaptic depression. Intensity-dependent adaptation may result from rapid feed-forward mechanisms that control the gain of the adaptation process instantaneously, or it may involve much slower timescales. To test this, we applied a recovery protocol to a subset of recorded thalamic cells (n = 7). As before, we delivered a train of 20 stimuli at 18 Hz, but in addition we delivered a test stimulus of the same intensity as the train, 1, 2.3, or 5 s after the end of the train (Figure 4A ). Recovery was quantified in a similar way to adaptation, by measuring either the ratio of the PSTH peak or the spike count for the test response and the first response. Similarly to high-frequency stimulus trains, the test responses to high-intensity stimuli exhibited significantly better recovery (i.e., less adaptation) on timescales of up to 5 s. Responses to high-intensity stimuli fully recovered and even facilitated after 1 s (peak recovery ratio 1.11 ± 0.16), whereas responses to low-intensity stimuli remained significantly depressed even after 5 s (peak recovery ratio 0.84 ± 0.08, p < 0.01, one-sided one-sample Wilcoxon signed-rank test, n = 7). Significant differences between the two conditions were evident at all timescales for the peak recovery ratio (Figure 4B) and after 1 s in the spike-count recovery ratio as well (Figure 4C, p < 0.05, one-sided two-sample Wilcoxon signed-rank test, n = 7). Slow recovery from stimulus trains and adaptation to high-frequency stimulation display the same dependency on stimulus intensity: low-intensity stimuli adapt more and recover more slowly. This suggests that these two phenomena may share common mechanisms with slow kinetic components, on timescales of seconds. Neurons in the reticular thalamic nucleus (RT) receive excitatory input from the VPM and cortex, and in turn, send feedback inhibition to the VPM. The RT constitutes the only known source of inhibitory input to VPM cells (Pinault, 2004Pinault D. The thalamic reticular nucleus: structure, function and concept.Brain Res. Brain Res. Rev. 2004; 46: 1-31Crossref PubMed Scopus (391) Google Scholar, Shosaku et al., 1989Shosaku A. Kayama Y. Sumitomo I. Sugitani M. Iwama K. Analysis of recurrent inhibitory circuit in rat thalamus: neurophysiology of the thalamic reticular nucleus.Prog. Neurobiol. 1989; 32: 77-102Crossref PubMed Scopus (94) Google Scholar). Previous studies report that RT responses adapt much more than thalamic responses in response to high-frequency repetitive stimulation (Hartings et al., 2003Hartings J.A. Temereanca S. Simons D.J. Processing of periodic whisker deflections by neurons in the ventroposterior medial and thalamic reticular nuclei.J. Neurophysiol. 2003; 90: 3087-3094Crossref PubMed Scopus (36) Google Scholar, Yu et al., 2009Yu X.J. Xu X.X. Chen X. He S. He J. Slow recovery from excitation of thalamic reticular nucleus neurons.J. Neurophysiol. 2009; 101: 980-987Crossref PubMed Scopus (23) Google Scholar). It is possible therefore that reduced firing of RT neurons due to adaptation during repetitive stimulation may disinhibit the VPM, causing a recovery of VPM responses and thus may explain the unexpected adaptation pattern of thalamic cells. Simultaneous paired whisker-aligned recordings of VPM single units and multiunit activity in the RT (n = 5) supported this conjecture. We observed that while VPM responses exhibit some recovery after about the tenth stimulus, RT responses recover much less, if at all (Figure 5A ). We therefore speculated that RT responses to high-intensity stimuli will depress more profoundly than the responses to low-intensity stimuli and thus disinhibit the VPM, resulting in less adaptation to stronger stimuli. Though RT neurons receive input from the VPM, adaptation of multiunit activity in the RT (n = 11) did not display the same dependence on stimulus intensity observed in the VPM. The PSTH peak response adaptation ratio was smaller for the steady-state response to the high-intensity stimulus (Figures 5C and 5D p < 10−10, n = 11, one-sided two-sample Wilcoxon signed-rank test), indicating greater adaptation to the stronger stimulus, as predicted by our initial hypothesis, while the spike-count adaptation ratio showed no significant difference between the two conditions (Figures 5E and 5F). This result can, in theory, provide an explanation to our surprising observations in VPM and cortex, but it is not sufficient to conclude that feedback inhibition is the cause. Our next goal was to examine if indeed thalamic feed-back mechanisms are the cause of the intensity-dependent adaptation we observed, or whether it is a property of feed-forward inputs from the brainstem. One approach would be to somehow manipulate or block inhibition (Castro-Alamancos, 2002aCastro-Alamancos M.A. Different temporal processing of sensory inputs in the rat thalamus during quiescent and information processing states in vivo.J. Physiol. 2002; 539: 567-578Crossref PubMed Scopus (102) Google Scholar, Cotillon-Williams et al., 2008Cotillon-Williams N. Huetz C. Hennevin E. Edeline J.M. Tonotopic control of auditory thalamus frequency tuning by reticular thalamic neurons.J. Neurophysiol. 2008; 99: 1137-1151Crossref PubMed Scopus (40) Google Scholar), but this runs the risk of inadvertently affecting network behavior. To address this question, we proceeded by performing intracellular recordings of VPM neurons. Using sharp electrodes, we recorded the membrane potential of VPM neurons responding to both stimulus conditions (n = 13). In agreement with previous studies, we observed large EPSPs in our recordings (Figure 6A ), which are thought to reflect inputs from few brainstem fibers (Castro-Alamancos, 2002bCastro-Alamancos M.A. Properties of primary sensory (lemniscal) synapses in the ventrobasal thalamus and the relay of high-frequency sensor" @default.
• W2002246101 created "2016-06-24" @default.
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• W2002246101 date "2010-04-01" @default.
• W2002246101 modified "2023-10-14" @default.
• W2002246101 title "Intensity-Dependent Adaptation of Cortical and Thalamic Neurons Is Controlled by Brainstem Circuits of the Sensory Pathway" @default.
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• W2002246101 doi "https://doi.org/10.1016/j.neuron.2010.03.032" @default.
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