Cortical sensory processing is modulated by behavioral and cognitive states. How this modulation is achieved by changing synaptic circuits remains largely unknown. In awake mouse auditory cortex, we found that sensory-evoked spike responses of layer 2/3 (L2/3) excitatory cells were scaled down with preserved sensory tuning when mice transitioned from quiescence to active behaviors, including locomotion, whereas L4 and thalamic responses were unchanged. Whole-cell voltage-clamp recordings revealed that tone-evoked synaptic excitation and inhibition exhibited a robust functional balance. The change to active states caused scaling down of excitation and inhibition at approximately equal levels in L2/3 cells, but resulted in no synaptic changes in L4 cells. This lamina-specific gain control could be attributed to an enhancement of L1-mediated inhibitory tone, with L2/3 parvalbumin inhibitory neurons also being suppressed. Thus, L2/3 circuits can adjust the salience of output in accordance with momentary behavioral demands while maintaining the sensitivity and quality of sensory processing.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Nature Communications Open Access 15 February 2021
Increasing endogenous activity of NMDARs on GABAergic neurons increases inhibition, alters sensory processing and prevents noise-induced tinnitus
Scientific Reports Open Access 20 July 2020
Vagus nerve stimulation (VNS)-induced layer-specific modulation of evoked responses in the sensory cortex of rats
Scientific Reports Open Access 02 June 2020
Subscribe to Journal
Get full journal access for 1 year
only $6.58 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Oswald, A.M., Schiff, M.L. & Reyes, A.D. Synaptic mechanisms underlying auditory processing. Curr. Opin. Neurobiol. 16, 371–376 (2006).
Wu, G.K., Tao, H.W. & Zhang, L.I. From elementary synaptic circuits to information processing in primary auditory cortex. Neurosci. Biobehav. Rev. 35, 2094–2104 (2011).
Petersen, C.C. & Crochet, S. Synaptic computation and sensory processing in neocortical layer 2/3. Neuron 78, 28–48 (2013).
Callaway, E.M. Local circuits in primary visual cortex of the macaque monkey. Annu. Rev. Neurosci. 21, 47–74 (1998).
Douglas, R.J. & Martin, K.A. Neuronal circuits of the neocortex. Annu. Rev. Neurosci. 27, 419–451 (2004).
Fanselow, E.E. & Nicolelis, M.A. Behavioral modulation of tactile responses in the rat somatosensory system. J. Neurosci. 19, 7603–7616 (1999).
Reynolds, J.H. & Chelazzi, L. Attentional modulation of visual processing. Annu. Rev. Neurosci. 27, 611–647 (2004).
Ferezou, I. et al. Spatiotemporal dynamics of cortical sensorimotor integration in behaving mice. Neuron 56, 907–923 (2007).
Lee, S., Carvell, G.E. & Simons, D.J. Motor modulation of afferent somatosensory circuits. Nat. Neurosci. 11, 1430–1438 (2008).
Otazu, G.H., Tai, L.H., Yang, Y. & Zador, A.M. Engaging in an auditory task suppresses responses in auditory cortex. Nat. Neurosci. 12, 646–654 (2009).
Niell, C.M. & Stryker, M.P. Modulation of visual responses by behavioral state in mouse visual cortex. Neuron 65, 472–479 (2010).
Crochet, S. & Petersen, C.C. Correlating whisker behavior with membrane potential in barrel cortex of awake mice. Nat. Neurosci. 9, 608–610 (2006).
Poulet, J.F. & Petersen, C.C. Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice. Nature 454, 881–885 (2008).
Goard, M. & Dan, Y. Basal forebrain activation enhances cortical coding of natural scenes. Nat. Neurosci. 12, 1444–1449 (2009).
Constantinople, C.M. & Bruno, R.M. Effects and mechanisms of wakefulness on local cortical networks. Neuron 69, 1061–1068 (2011).
Gentet, L.J., Avermann, M., Matyas, F., Staiger, J.F. & Petersen, C.C. Membrane potential dynamics of GABAergic neurons in the barrel cortex of behaving mice. Neuron 65, 422–435 (2010).
Zagha, E., Casale, A.E., Sachdev, R.N., McGinley, M.J. & McCormick, D.A. Motor cortex feedback influences sensory processing by modulating network state. Neuron 79, 567–578 (2013).
Polack, P.O., Friedman, J. & Golshani, P. Cellular mechanisms of brain state–dependent gain modulation in visual cortex. Nat. Neurosci. 16, 1331–1339 (2013).
Bennett, C., Arroyo, S. & Hestrin, S. Subthreshold mechanisms underlying state-dependent modulation of visual responses. Neuron 80, 350–357 (2013).
Wehr, M. & Zador, A.M. Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex. Nature 426, 442–446 (2003).
Zhang, L.I., Tan, A.Y.Y., Schreiner, C.E. & Merzenich, M.M. Topography and synaptic shaping of direction selectivity in primary auditory cortex. Nature 424, 201–205 (2003).
Tan, A.Y., Zhang, L.I., Merzenich, M.M. & Schreiner, C.E. Tone-evoked excitatory and inhibitory synaptic conductances of primary auditory cortex neurons. J. Neurophysiol. 92, 630–643 (2004).
Wu, G.K., Arbuckle, R., Liu, B.H., Tao, H.W. & Zhang, L.I. Lateral sharpening of cortical frequency tuning by approximately balanced inhibition. Neuron 58, 132–143 (2008).
Li, L.Y. et al. Differential receptive field properties of parvalbumin and somatostatin inhibitory neurons in mouse auditory cortex. Cereb. Cortex published online, 10.1093/cercor/bht417 (14 January 2014).
Sakata, S. & Harris, K.D. Laminar-dependent effects of cortical state on auditory cortical spontaneous activity. Front. Neural Circuits 6, 109 (2012).
Zhou, Y. et al. Preceding inhibition silences layer 6 neurons in auditory cortex. Neuron 65, 706–717 (2010).
Zhou, Y. et al. Generation of spike latency tuning by thalamocortical circuits in auditory cortex. J. Neurosci. 32, 9969–9980 (2012).
Wu, G.K., Li, P., Tao, H.W. & Zhang, L.I. Nonmonotonic synaptic excitation and imbalanced inhibition underlying cortical intensity tuning. Neuron 52, 705–715 (2006).
Sun, Y.J. et al. Fine-tuning of pre-balanced excitation and inhibition during auditory cortical development. Nature 465, 927–928 (2010).
Dantzker, J.L. & Callaway, E.M. Laminar sources of synaptic input to cortical inhibitory interneurons and pyramidal neurons. Nat. Neurosci. 3, 701–707 (2000).
Pfeffer, C.K., Xue, M., He, M., Huang, Z.J. & Scanziani, M. Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons. Nat. Neurosci. 16, 1068–1076 (2013).
Lima, S.Q., Hromadka, T., Znamenskiy, P. & Zador, A.M. PINP: a new method of tagging neuronal populations for identification during in vivo electrophysiological recording. PLoS ONE 4, e6099 (2009).
Ma, W.P. et al. Visual representations by cortical somatostatin inhibitory neurons—selective, but with weak and delayed responses. J. Neurosci. 30, 14371–14379 (2010).
Jiang, X., Wang, G., Lee, A.J., Stornetta, R.L. & Zhu, J.J. The organization of two new cortical interneuronal circuits. Nat. Neurosci. 16, 210–218 (2013).
Winer, J.A. & Larue, D.T. Populations of GABAergic neurons and axons in layer I of rat auditory cortex. Neuroscience 33, 499–515 (1989).
Hestrin, S. & Armstrong, W.E. Morphology and physiology of cortical neurons in layer I. J. Neurosci. 16, 5290–5300 (1996).
Shlosberg, D., Amitai, Y. & Azouz, R. Time-dependent, layer-specific modulation of sensory responses mediated by neocortical layer 1. J. Neurophysiol. 96, 3170–3182 (2006).
Wozny, C. & Williams, S.R. Specificity of synaptic connectivity between layer 1 inhibitory interneurons and layer 2/3 pyramidal neurons in the rat neocortex. Cereb. Cortex 21, 1818–1826 (2011).
Glickfeld, L.L., Histed, M.H. & Maunsell, J.H. Mouse primary visual cortex is used to detect both orientation and contrast changes. J. Neurosci. 33, 19416–19422 (2013).
Destexhe, A., Rudolph, M. & Pare, D. The high-conductance state of neocortical neurons in vivo. Nat. Rev. Neurosci. 4, 739–751 (2003).
Haider, B., Hausser, M. & Carandini, M. Inhibition dominates sensory responses in the awake cortex. Nature 493, 97–100 (2013).
Liu, B.H. et al. Broad inhibition sharpens orientation selectivity by expanding input dynamic range in mouse simple cells. Neuron 71, 542–554 (2011).
Xiong, X.R. et al. Interaural level difference-dependent gain control and synaptic scaling underlying binaural computation. Neuron 79, 738–753 (2013).
Harris, K.D. Top-down control of cortical state. Neuron 79, 408–410 (2013).
Poulet, J.F., Fernandez, L.M., Crochet, S. & Petersen, C.C. Thalamic control of cortical states. Nat. Neurosci. 15, 370–372 (2012).
Nelson, A. et al. A circuit for motor cortical modulation of auditory cortical activity. J. Neurosci. 33, 14342–14353 (2013).
Felleman, D.J. & Van Essen, D.C. Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1, 1–47 (1991).
Cauller, L.J., Clancy, B. & Connors, B.W. Backward cortical projections to primary somatosensory cortex in rats extend long horizontal axons in layer I. J. Comp. Neurol. 390, 297–310 (1998).
Gonchar, Y. & Burkhalter, A. Distinct GABAergic targets of feedforward and feedback connections between lower and higher areas of rat visual cortex. J. Neurosci. 23, 10904–10912 (2003).
Petreanu, L., Mao, T., Sternson, S.M. & Svoboda, K. The subcellular organization of neocortical excitatory connections. Nature 457, 1142–1145 (2009).
Olsen, S.R., Bortone, D.S., Adesnik, H. & Scanziani, M. Gain control by layer six in cortical circuits of vision. Nature 483, 47–52 (2012).
Guo, W. et al. Robustness of cortical topography across fields, laminae, anesthetic states, and neurophysiological signal types. J. Neurosci. 32, 9159–9172 (2012).
Li, Y.T., Ibrahim, L.A., Liu, B.H., Zhang, L.I. & Tao, H.W. Linear transformation of thalamocortical input by intracortical excitation. Nat. Neurosci. 16, 1324–1330 (2013).
Li, L.Y., Li, Y.T., Zhou, M., Tao, H.W. & Zhang, L.I. Intracortical multiplication of thalamocortical signals in mouse auditory cortex. Nat. Neurosci. 16, 1179–1181 (2013).
Borg-Graham, L.J., Monier, C. & Fregnac, Y. Visual input evokes transient and strong shunting inhibition in visual cortical neurons. Nature 393, 369–373 (1998).
Anderson, J.S., Carandini, M. & Ferster, D. Orientation tuning of input conductance, excitation, and inhibition in cat primary visual cortex. J. Neurophysiol. 84, 909–926 (2000).
We thank B. Zingg and L.Y. Li for the help on viral injection and Nissl staining. This work was supported by grants to L.I.Z. from the US National Institutes of Health (R01DC008983) and the David and Lucile Packard Foundation (Packard Fellowships for Science and Engineering). H.W.T. was supported by US National Institutes of Health grant R01EY019049. Z.X., L.I.Z. and F.L. were supported by grants from the National Natural Science Foundation of China (U1301225, 31228013, 31200831) and a 973 program (2014CB943002).
The authors declare no competing financial interests.
Integrated supplementary information
Supplementary Figure 1 Superimposed power spectra of LFP recorded during three different behavioral states.
The power spectra were smoothed by averaging pixel values using a sliding window.
(a) From left to right, confocal image of brain sections of A1 showing the Nissl staining, the fluorescence expression pattern in a Scnn1a-Tg3-Cre X Ai14 mouse (Nissl staining in blue), the distribution of thalamocortical axons (overexposed to reveal detailed structures) in a wildtype mouse injected in the MGBv with a non-floxed AAV virus encoding EYFP, reconstructed morphology of two example pyramidal neurons recorded in L2/3 and L4, and recovered depths of another seven recorded pyramidal cells (L2/3 cells are labeled as black and L4 cells are labeled as red). Cortical layers are labeled. (b) Comparison of the reconstructed cell depth and corrected cortical depth based on micromanipulator readings. The unity line is shown.
The horizontal dashed line marks scaling factor of 1. The vertical line marks the boundary between L2/3 and L4, i.e. 360 ± 10 μm below the pia surface. N = 32 cells. Within L2/3, there is no correlation between scaling factor and cell depth (r = -0.26).
Supplementary Figure 4 The slope of linear regression for “A” versus “Q” response levels as measured by charge transfer.
Data are presented similarly as in Fig. 5o. Solid symbol represents mean ± s.d. Red dash line indicates slope of 1. There is no significant difference between excitation and inhibition in L2/3 cells (P = 0.2242, t = 1.304, unpaired t-test, n = 11, 7)
(a) BF-tone evoked synaptic currents (averaged from 8 repeats for “quiet” state and 5 repeats for “active” state) of an example cell recorded under different clamping voltages. Vertical arrow marks tone onset. Note that baseline currents have been subtracted. Scale: 100 pA and 20 ms. (b) Current-voltage (I-V) relationship under different behavioral states plotted for the same cell in (a). The current value was averaged within a 1 ms window at 25 ms post tone onset (around the peak of the inward current, marked by dash line “I” in (a)). Bar = s.d. Note that the slope became shallower in active states, indicating that there was a reduction of total evoked synaptic conductance. (c) I-V relationship for currents averaged within a 1 ms window at 54 ms post tone onset (marked by the dash line “II” in (a)). Note that the reversal potential was more negative than in (b) due to a lower instantaneous E/I ratio.
(a) Series resistance was measured by injecting negative square pulses (50 ms, -5 mV) in an example voltage-clamp recording experiment. Rs was calculated from the transient current at the onset of the testing pulse. Rs values before, during (red) and after a running epoch are shown. Averaged response traces to test pulses are shown on the top. Simultaneously recorded speed is shown at the bottom. (b) Summary of average Rs values during epochs of quiescence and the following epochs of locomotion. N = 8 cells.
Supplementary Figure 7 Comparison of synaptic amplitudes during the whole recording session and before, during and after epochs of active behaviors.
(a) Top, time course of behavioral states in a typical 6.25-minutes recording session. Epochs of quiescence and active states are marked by black and red lines respectively. Note that quiescence and active states are intermingled in the course of a typical experiment. Middle, trace of recorded plate rotating speed is shown. Arrow indicates speed = 0. Scale: 10 cm s-1, 20 s. Bottom, evoked peak excitatory conductance in response to 70 dB BF tones during Q and A states. The Rs was also monitored for this recording session. (b) Top, enlarged speed trace within the time window marked by two dash lines in (a). Bottom, overlapped raw traces of recorded excitatory currents in response to 70 dB BF tones before, during and after this epoch of locomotion. Thick bar indicates tone duration (50 ms). (c,d) Summary of average peak amplitude of evoked excitatory (c) or inhibitory (d) conductance in response to 70 dB BF tones before, during and after an epoch of locomotion. N = 11 cells in (c), 7 cells in (d). Solid symbol represents mean ± s.e.m. *P < 0.05, ***P < 0.001, Wilcoxon signed rank test. (e) Average peak amplitudes of evoked excitatory conductances in response to 70 dB BF tones at the start and end of a recording session. There is no significant difference (P = 0.1475, Z = 1.467, n = 11, Wilcoxon signed rank test).
About this article
Cite this article
Zhou, M., Liang, F., Xiong, X. et al. Scaling down of balanced excitation and inhibition by active behavioral states in auditory cortex. Nat Neurosci 17, 841–850 (2014). https://doi.org/10.1038/nn.3701
This article is cited by
Machine Intelligence Research (2022)
Nature Communications (2021)
The Journal of Mathematical Neuroscience (2020)
Nature Protocols (2020)
Nature Communications (2020)