- Email: [email protected]
Parvalbumin Interneurons: All Forest, No Trees
Neuron
Previews nearby synapses will have an important role to play.
Govindarajan, A., Kelleher, R.J., and Tonegawa, S. (2006). Nat. Rev. Neurosci. 7, 575–583.
REFERENCES
Grienberger, C., Chen, X., and Konnerth, A. (2015). Trends Neurosci. 38, 45–54.
Ackman, J.B., Burbridge, T.J., and Crair, M.C. (2012). Nature 490, 219–225.
Harvey, C.D., and Svoboda, K. (2007). Nature 450, 1195–1200.
Druckmann, S., Feng, L., Lee, B., Yook, C., Zhao, T., Magee, J.C., and Kim, J. (2014). Neuron 81, 629–640.
Khazipov, R., and Luhmann, H.J. (2006). Trends Neurosci. 29, 414–418.
Fu, M., Yu, X., Lu, J., and Zuo, Y. (2012). Nature 483, 92–95.
Kleindienst, T., Winnubst, J., Roth-Alpermann, C., Bonhoeffer, T., and Lohmann, C. (2011). Neuron 72, 1012–1024.
Gambino, F., Page`s, S., Kehayas, V., Baptista, D., Tatti, R., Carleton, A., and Holtmaat, A. (2014). Nature 515, 116–119.
Major, G., Larkum, M.E., and Schiller, J. (2013). Annu. Rev. Neurosci. 36, 1–24.
Makino, H., and Malinow, R. (2011). Neuron 72, 1001–1011. Oh, W.C., Parajuli, L.K., and Zito, K. (2015). Cell Rep. 10, 162–169. Poirazi, P., and Mel, B.W. (2001). Neuron 29, 779–796. Takahashi, N., Kitamura, K., Matsuo, N., Mayford, M., Kano, M., Matsuki, N., and Ikegaya, Y. (2012). Science 335, 353–356. Winnubst, J., Cheyne, J.E., Niculescu, D., and Lohmann, C. (2015). Neuron 87, this issue, 399–410.
Parvalbumin Interneurons: All Forest, No Trees Joshua T. Trachtenberg1,* 1Department of Neurobiology, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA 90095, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.neuron.2015.06.041
There has been a surge of interest in how inhibitory neurons influence the output of local circuits in the brain. In this issue of Neuron, Scholl et al. (2015) provide a compelling argument for what one class of inhibitory neurons actually does. What is cortical inhibition good for? Recently, the answer to this question is remarkably similar to one of those questions on ‘‘Family Feud,’’ where there’s a survey of opinions and the top 10 answers are all correct. Fortunately, the results from Scholl et al. (2015) in this issue of Neuron add enough new data to tip the scales in favor of one simple answer. In the neocortex, inhibitory neurons are a fairly small minority, comprising roughly 20% of all cortical neurons. Historically, this has made it difficult to find these cells and to record from them in the intact brain. Even more maddening, this small population is subdivided, very roughly, into three groups (and more likely a dozen), based on their interaction with excitatory neurons (Kawaguchi and Kubota, 1997). Parvalbumin (PV)-expressing interneurons fire rapid barrages of action potentials, and are accordingly named ‘‘fast-spiking’’ interneurons. These innervate and inhibit the cell bodies of excitatory neurons. Somatostatin (SOM)-ex-
pressing interneurons have firing rates that are more on par with the local excitatory neurons, and are thus often referred to as ‘‘regular-spiking.’’ These innervate and inhibit the dendrites of pyramidal neurons. In the primary visual cortex, where these cells have been most extensively studied, both groups receive strong excitatory input. The final group of inhibitory neurons is characterized by their expression of vasoactive intestinal polypeptide (VIP). These cells appear to inhibit other inhibitory neurons and to receive neuromodulatory input from the brainstem, and are thought to regulate brain states during arousal (Hangya et al., 2014; Pfeffer et al., 2013). Over the past half-decade or so, a number of mouse lines have been developed in which expression of the gene encoding the bacteriophage tyrosine recombinase enzyme, Cre recombinase, is directed by PV, SOM, or VIP promoter/ enhancer elements (Pfeffer et al., 2013). These mice have given us the ability to
finally visualize and manipulate each inhibitory class. Scholl et al. (2015) provide new data supporting a view that the computational heavy lifting in the cortex is done by the excitatory neurons, whereas PV cells seem to leave a lot of potentially very useful information on the table. Rather than integrating specific cortical inputs to create complex receptive fields that extract higher-order information from the visual scene, as excitatory neurons do (Cossell et al., 2015), PV cells simply integrate inputs from the local network without specificity (Figure 1). Being uniformly connected to the local excitatory neurons makes PV cells well suited to a different role—monitoring and regulating the total activity of the local network, also known as gain control. To reach this conclusion, they imaged the activity of large numbers of excitatory and PV neurons in mouse primary visual cortex using two-photon excitation of the calcium indicator Oregon Green
Neuron 87, July 15, 2015 ª2015 Elsevier Inc. 247
Neuron
Previews
BAPTA. PV cells in these mice classes. The emergence of selectively expressed the red transgenic animals, coupled fluorescent protein tdTomato, with the ability to express prowhich was used to identify teins in specific cell types, is these cells and separate their revealing the underlying rules signals. Many earlier studies of circuit connectivity, both found that PV cells are less at the functional and anatomFigure 1. PV Cell-Mediated Gain Control and Learning interested in the particular ical levels. Instead of playing (Left) Pyramidal neurons nonlinearly integrate broad synaptic input to proorientation of a visual stimulus a game of Jeopardy, where duce sharply tuned spiking output. (Middle) PV interneurons simply sum their inputs linearly. (Right) The resulting broad inhibition acts to alter firing rates of than are excitatory neurons. answers are questions, we pyramidal neurons, which underlies learning. This response promiscuity are moving toward Wheel of could be restricted to orientaFortune, where the individual tion selectivity, or it could be a general that by simply integrating information letters, or cortical motifs, are being property of PV cells. Scholl et al. (2015) from nearby neurons, PV cells are taking uncovered. examined this by measuring ocular domi- a moment-by-moment pulse of network nance and binocular disparity in PV and activity and acting on this information to REFERENCES excitatory neurons. Because excitatory modulate cortical response gain (Atallah neurons in the binocular zone of visual et al., 2012; Wilson et al., 2012). That is, Anderson, J.S., Carandini, M., and Ferster, D. (2000). J. Neurophysiol. 84, 909–926. cortex receive inputs from both eyes, PV cells are not altering the computations they can be sensitive to binocular dispar- performed by local excitatory neurons, Atallah, B.V., Bruns, W., Carandini, M., and Scanziani, M. (2012). Neuron 73, 159–170. ities and respond optimally when the vi- but altering their firing rates. sual stimuli delivered to each eye are Gain control is not simply there to pre- Bock, D.D., Lee, W.C., Kerlin, A.M., Andermann, slightly out of phase. This binocular vent seizures. An emergent model, based M.L., Hood, G., Wetzel, A.W., Yurgenson, S., Soucy, E.R., Kim, H.S., and Reid, R.C. (2011). disparity gives rise to a perception of the on results from many labs, is that PV-cell- Nature 471, 177–182. world in three dimensions. mediated gain control is tremendously L., Iacaruso, M.F., Muir, D.R., Houlton, R., What they found is quite remarkable, important for, perhaps, the most inter- Cossell, Sader, E.N., Ko, H., Hofer, S.B., and Mrsic-Flogel, while at the same time quite consistent esting aspect of cortical processing: T.D. (2015). Nature 518, 399–403. with other investigators. Despite having learning (Figure 1). In juvenile cortex, a Courtin, J., Chaudun, F., Rozeske, R.R., Karalis, far stronger responses to binocular stimu- prolonged drop in sensory input to cortex N., Gonzalez-Campo, C., Wurtz, H., Abdi, A., Baulation than excitatory neurons, PV cells drops excitatory firing rates. The most freton, J., Bienvenu, T.C., and Herry, C. (2014). Naappear to discard the binocular disparity rapid anatomical and physiological plas- ture 505, 92–96. information that comes with it. That is, ticity that occurs to compensate is a loss Hangya, B., Pi, H.J., Kvitsiani, D., Ranade, S.P., they don’t seem to participate much in of feedforward input to PV cells (Kuhlman and Kepecs, A. (2014). Curr. Opin. Neurobiol. 26, 117–124. the process of extracting depth informa- et al., 2013). This severe reduction in PV tion. Instead, these inhibitory cells simply cell responsiveness in turn drops local in- Kawaguchi, Y., and Kubota, Y. (1997). Cereb. sum all of the information from the sur- hibition of excitatory networks, enabling Cortex 7, 476–486. rounding network of neurons residing them to be more responsive to whatever Kerlin, A.M., Andermann, M.L., Berezovskii, V.K., within 100 microns or so of their position. sensory input remains. This form of expe- and Reid, R.C. (2010). Neuron 67, 858–871. If this same local and linear integration rience-dependent plasticity is simply gain Kuhlman, S.J., Olivas, N.D., Tring, E., Ikrar, T., Xu, model applies to carnivore and primate control by another name. By working to X., and Trachtenberg, J.T. (2013). Nature 501, visual cortex, where neurons with similar re-establish normal excitatory firing rates, 543–546. orientation preference are clustered into PV cells set the conditions necessary for Lee, S.H., Kwan, A.C., Zhang, S., Phoumthipphacolumns of orientation and ocular domi- slower, more permanent changes in the vong, V., Flannery, J.G., Masmanidis, S.C., Taniguchi, H., Huang, Z.J., Zhang, F., Boyden, E.S., et al. nance, then, as the authors point out, PV excitatory wiring of local networks. cells in these brains would find themIn adults, too, PV cell-mediated gain (2012). Nature 488, 379–383. selves surrounded by excitatory neurons control is a central part of learning Letzkus, J.J., Wolff, S.B., Meyer, E.M., Tovote, P., of like tuning (Anderson et al., 2000). (Hangya et al., 2014). In mice trained to Courtin, J., Herry, C., and Lu¨thi, A. (2011). Nature These PV cells would then be expected associate a tone with a foot shock, PV 480, 331–335. to show similarly sharp orientation and cells in primary auditory (Letzkus et al., Liu, B.H., Li, Y.T., Ma, W.P., Pan, C.J., Zhang, L.I., ocular dominance tuning, in stark contrast 2011) and dorsomedial prefrontal cortex and Tao, H.W. (2011). Neuron 71, 542–554. to what has been reported in mice where (Courtin et al., 2014) are phasically inPfeffer, C.K., Xue, M., He, M., Huang, Z.J., and such columns do not exist. Should this hibited during fear expression, whereas Scanziani, M. (2013). Nat. Neurosci. 16, 1068– be true, it would call into question the PV cells are phasically activated when 1076. idea that broad tuning seen in PV cells mice transition out of a reward zone Scholl, B., Pattadkal, J.J., Dilly, G.A., Priebe, N.J., acts to sharpen excitatory tuning (Lee (Hangya et al., 2014). and Zemelman, B.V. (2015). Neuron 87, this issue, For years our understanding of inhibi- 424–436. et al., 2012; Liu et al., 2011). Instead, the authors argue, as others have done tory neocortical circuitry has been limited Wilson, N.R., Runyan, C.A., Wang, F.L., and Sur, (Bock et al., 2011; Kerlin et al., 2010), by our ability to access specific cell sub- M. (2012). Nature 488, 343–348. 248 Neuron 87, July 15, 2015 ª2015 Elsevier Inc.
Previews nearby synapses will have an important role to play.
Govindarajan, A., Kelleher, R.J., and Tonegawa, S. (2006). Nat. Rev. Neurosci. 7, 575–583.
REFERENCES
Grienberger, C., Chen, X., and Konnerth, A. (2015). Trends Neurosci. 38, 45–54.
Ackman, J.B., Burbridge, T.J., and Crair, M.C. (2012). Nature 490, 219–225.
Harvey, C.D., and Svoboda, K. (2007). Nature 450, 1195–1200.
Druckmann, S., Feng, L., Lee, B., Yook, C., Zhao, T., Magee, J.C., and Kim, J. (2014). Neuron 81, 629–640.
Khazipov, R., and Luhmann, H.J. (2006). Trends Neurosci. 29, 414–418.
Fu, M., Yu, X., Lu, J., and Zuo, Y. (2012). Nature 483, 92–95.
Kleindienst, T., Winnubst, J., Roth-Alpermann, C., Bonhoeffer, T., and Lohmann, C. (2011). Neuron 72, 1012–1024.
Gambino, F., Page`s, S., Kehayas, V., Baptista, D., Tatti, R., Carleton, A., and Holtmaat, A. (2014). Nature 515, 116–119.
Major, G., Larkum, M.E., and Schiller, J. (2013). Annu. Rev. Neurosci. 36, 1–24.
Makino, H., and Malinow, R. (2011). Neuron 72, 1001–1011. Oh, W.C., Parajuli, L.K., and Zito, K. (2015). Cell Rep. 10, 162–169. Poirazi, P., and Mel, B.W. (2001). Neuron 29, 779–796. Takahashi, N., Kitamura, K., Matsuo, N., Mayford, M., Kano, M., Matsuki, N., and Ikegaya, Y. (2012). Science 335, 353–356. Winnubst, J., Cheyne, J.E., Niculescu, D., and Lohmann, C. (2015). Neuron 87, this issue, 399–410.
Parvalbumin Interneurons: All Forest, No Trees Joshua T. Trachtenberg1,* 1Department of Neurobiology, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA 90095, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.neuron.2015.06.041
There has been a surge of interest in how inhibitory neurons influence the output of local circuits in the brain. In this issue of Neuron, Scholl et al. (2015) provide a compelling argument for what one class of inhibitory neurons actually does. What is cortical inhibition good for? Recently, the answer to this question is remarkably similar to one of those questions on ‘‘Family Feud,’’ where there’s a survey of opinions and the top 10 answers are all correct. Fortunately, the results from Scholl et al. (2015) in this issue of Neuron add enough new data to tip the scales in favor of one simple answer. In the neocortex, inhibitory neurons are a fairly small minority, comprising roughly 20% of all cortical neurons. Historically, this has made it difficult to find these cells and to record from them in the intact brain. Even more maddening, this small population is subdivided, very roughly, into three groups (and more likely a dozen), based on their interaction with excitatory neurons (Kawaguchi and Kubota, 1997). Parvalbumin (PV)-expressing interneurons fire rapid barrages of action potentials, and are accordingly named ‘‘fast-spiking’’ interneurons. These innervate and inhibit the cell bodies of excitatory neurons. Somatostatin (SOM)-ex-
pressing interneurons have firing rates that are more on par with the local excitatory neurons, and are thus often referred to as ‘‘regular-spiking.’’ These innervate and inhibit the dendrites of pyramidal neurons. In the primary visual cortex, where these cells have been most extensively studied, both groups receive strong excitatory input. The final group of inhibitory neurons is characterized by their expression of vasoactive intestinal polypeptide (VIP). These cells appear to inhibit other inhibitory neurons and to receive neuromodulatory input from the brainstem, and are thought to regulate brain states during arousal (Hangya et al., 2014; Pfeffer et al., 2013). Over the past half-decade or so, a number of mouse lines have been developed in which expression of the gene encoding the bacteriophage tyrosine recombinase enzyme, Cre recombinase, is directed by PV, SOM, or VIP promoter/ enhancer elements (Pfeffer et al., 2013). These mice have given us the ability to
finally visualize and manipulate each inhibitory class. Scholl et al. (2015) provide new data supporting a view that the computational heavy lifting in the cortex is done by the excitatory neurons, whereas PV cells seem to leave a lot of potentially very useful information on the table. Rather than integrating specific cortical inputs to create complex receptive fields that extract higher-order information from the visual scene, as excitatory neurons do (Cossell et al., 2015), PV cells simply integrate inputs from the local network without specificity (Figure 1). Being uniformly connected to the local excitatory neurons makes PV cells well suited to a different role—monitoring and regulating the total activity of the local network, also known as gain control. To reach this conclusion, they imaged the activity of large numbers of excitatory and PV neurons in mouse primary visual cortex using two-photon excitation of the calcium indicator Oregon Green
Neuron 87, July 15, 2015 ª2015 Elsevier Inc. 247
Neuron
Previews
BAPTA. PV cells in these mice classes. The emergence of selectively expressed the red transgenic animals, coupled fluorescent protein tdTomato, with the ability to express prowhich was used to identify teins in specific cell types, is these cells and separate their revealing the underlying rules signals. Many earlier studies of circuit connectivity, both found that PV cells are less at the functional and anatomFigure 1. PV Cell-Mediated Gain Control and Learning interested in the particular ical levels. Instead of playing (Left) Pyramidal neurons nonlinearly integrate broad synaptic input to proorientation of a visual stimulus a game of Jeopardy, where duce sharply tuned spiking output. (Middle) PV interneurons simply sum their inputs linearly. (Right) The resulting broad inhibition acts to alter firing rates of than are excitatory neurons. answers are questions, we pyramidal neurons, which underlies learning. This response promiscuity are moving toward Wheel of could be restricted to orientaFortune, where the individual tion selectivity, or it could be a general that by simply integrating information letters, or cortical motifs, are being property of PV cells. Scholl et al. (2015) from nearby neurons, PV cells are taking uncovered. examined this by measuring ocular domi- a moment-by-moment pulse of network nance and binocular disparity in PV and activity and acting on this information to REFERENCES excitatory neurons. Because excitatory modulate cortical response gain (Atallah neurons in the binocular zone of visual et al., 2012; Wilson et al., 2012). That is, Anderson, J.S., Carandini, M., and Ferster, D. (2000). J. Neurophysiol. 84, 909–926. cortex receive inputs from both eyes, PV cells are not altering the computations they can be sensitive to binocular dispar- performed by local excitatory neurons, Atallah, B.V., Bruns, W., Carandini, M., and Scanziani, M. (2012). Neuron 73, 159–170. ities and respond optimally when the vi- but altering their firing rates. sual stimuli delivered to each eye are Gain control is not simply there to pre- Bock, D.D., Lee, W.C., Kerlin, A.M., Andermann, slightly out of phase. This binocular vent seizures. An emergent model, based M.L., Hood, G., Wetzel, A.W., Yurgenson, S., Soucy, E.R., Kim, H.S., and Reid, R.C. (2011). disparity gives rise to a perception of the on results from many labs, is that PV-cell- Nature 471, 177–182. world in three dimensions. mediated gain control is tremendously L., Iacaruso, M.F., Muir, D.R., Houlton, R., What they found is quite remarkable, important for, perhaps, the most inter- Cossell, Sader, E.N., Ko, H., Hofer, S.B., and Mrsic-Flogel, while at the same time quite consistent esting aspect of cortical processing: T.D. (2015). Nature 518, 399–403. with other investigators. Despite having learning (Figure 1). In juvenile cortex, a Courtin, J., Chaudun, F., Rozeske, R.R., Karalis, far stronger responses to binocular stimu- prolonged drop in sensory input to cortex N., Gonzalez-Campo, C., Wurtz, H., Abdi, A., Baulation than excitatory neurons, PV cells drops excitatory firing rates. The most freton, J., Bienvenu, T.C., and Herry, C. (2014). Naappear to discard the binocular disparity rapid anatomical and physiological plas- ture 505, 92–96. information that comes with it. That is, ticity that occurs to compensate is a loss Hangya, B., Pi, H.J., Kvitsiani, D., Ranade, S.P., they don’t seem to participate much in of feedforward input to PV cells (Kuhlman and Kepecs, A. (2014). Curr. Opin. Neurobiol. 26, 117–124. the process of extracting depth informa- et al., 2013). This severe reduction in PV tion. Instead, these inhibitory cells simply cell responsiveness in turn drops local in- Kawaguchi, Y., and Kubota, Y. (1997). Cereb. sum all of the information from the sur- hibition of excitatory networks, enabling Cortex 7, 476–486. rounding network of neurons residing them to be more responsive to whatever Kerlin, A.M., Andermann, M.L., Berezovskii, V.K., within 100 microns or so of their position. sensory input remains. This form of expe- and Reid, R.C. (2010). Neuron 67, 858–871. If this same local and linear integration rience-dependent plasticity is simply gain Kuhlman, S.J., Olivas, N.D., Tring, E., Ikrar, T., Xu, model applies to carnivore and primate control by another name. By working to X., and Trachtenberg, J.T. (2013). Nature 501, visual cortex, where neurons with similar re-establish normal excitatory firing rates, 543–546. orientation preference are clustered into PV cells set the conditions necessary for Lee, S.H., Kwan, A.C., Zhang, S., Phoumthipphacolumns of orientation and ocular domi- slower, more permanent changes in the vong, V., Flannery, J.G., Masmanidis, S.C., Taniguchi, H., Huang, Z.J., Zhang, F., Boyden, E.S., et al. nance, then, as the authors point out, PV excitatory wiring of local networks. cells in these brains would find themIn adults, too, PV cell-mediated gain (2012). Nature 488, 379–383. selves surrounded by excitatory neurons control is a central part of learning Letzkus, J.J., Wolff, S.B., Meyer, E.M., Tovote, P., of like tuning (Anderson et al., 2000). (Hangya et al., 2014). In mice trained to Courtin, J., Herry, C., and Lu¨thi, A. (2011). Nature These PV cells would then be expected associate a tone with a foot shock, PV 480, 331–335. to show similarly sharp orientation and cells in primary auditory (Letzkus et al., Liu, B.H., Li, Y.T., Ma, W.P., Pan, C.J., Zhang, L.I., ocular dominance tuning, in stark contrast 2011) and dorsomedial prefrontal cortex and Tao, H.W. (2011). Neuron 71, 542–554. to what has been reported in mice where (Courtin et al., 2014) are phasically inPfeffer, C.K., Xue, M., He, M., Huang, Z.J., and such columns do not exist. Should this hibited during fear expression, whereas Scanziani, M. (2013). Nat. Neurosci. 16, 1068– be true, it would call into question the PV cells are phasically activated when 1076. idea that broad tuning seen in PV cells mice transition out of a reward zone Scholl, B., Pattadkal, J.J., Dilly, G.A., Priebe, N.J., acts to sharpen excitatory tuning (Lee (Hangya et al., 2014). and Zemelman, B.V. (2015). Neuron 87, this issue, For years our understanding of inhibi- 424–436. et al., 2012; Liu et al., 2011). Instead, the authors argue, as others have done tory neocortical circuitry has been limited Wilson, N.R., Runyan, C.A., Wang, F.L., and Sur, (Bock et al., 2011; Kerlin et al., 2010), by our ability to access specific cell sub- M. (2012). Nature 488, 343–348. 248 Neuron 87, July 15, 2015 ª2015 Elsevier Inc.