Neuron
Previews Kirby, L.G., Pernar, L., Valentino, R.J., and Beck, S.G. (2003). Neuroscience 116, 669–683.
Vandermaelen, C.P., and Aghajanian, G.K. (1983). Brain Res. 289, 109–119.
Lucki, I. (1998). Biol. Psychiatry 44, 151–162.
Varga, V., Sze´kely, A.D., Csillag, A., Sharp, T., and Hajo´s, M. (2001). Neuroscience 106, 783–792.
Nakamura, K. (2013). Front. Integr. Neurosci. 7, 60. Ogawa, S.K., Cohen, J.Y., Hwang, D., Uchida, N., and Watabe-Uchida, M. (2014). Cell Rep. Published online August 6, 2014. http://dx.doi.org/10. 1016/j.celrep.2014.06.042.
Wall, N.R., De La Parra, M., Callaway, E.M., and Kreitzer, A.C. (2013). Neuron 79, 347–360. Warden, M.R., Selimbeyoglu, A., Mirzabekov, J.J., Lo, M., Thompson, K.R., Kim, S.Y., Adhikari, A., Tye, K.M., Frank, L.M., and Deisseroth, K. (2012). Nature 492, 428–432.
Watabe-Uchida, M., Zhu, L., Ogawa, S.K., Vamanrao, A., and Uchida, N. (2012). Neuron 74, 858–873. Weissbourd, B., Ren, J., DeLoach, K.E., Guenthner, C.J., Miyamichi, K., and Luo, L. (2014). Neuron 83, this issue, 645–662. Wickersham, I.R., Lyon, D.C., Barnard, R.J., Mori, T., Finke, S., Conzelmann, K.K., Young, J.A., and Callaway, E.M. (2007). Neuron 53, 639–647.
Motion with Direction and Balance Abdel El Manira1 and Sten Grillner1,* 1Department of Neuroscience, Karolinska Institute, 17177 Stockholm, Sweden *Correspondence: [email protected] http://dx.doi.org/10.1016/j.neuron.2014.07.021
In this issue of Neuron, three papers, Severi et al. (2014), Thiele et al. (2014), and Wang and McLean (2014), examine the descending circuitry involved in coordinating locomotor behavior in larval zebrafish. Visually evoked locomotor and steering movements in zebrafish are mediated by neurons in the nucleus of the medial longitudinal fascicle (nMLF) that project directly to spinal motoneurons. The final motor output induced by nMLF will involve integration in the hindbrain and premotor circuits in the spinal cord. Animals navigate through their environment with extreme agility by grading the speed and strength of their locomotor movements while at the same time controlling their balance (posture). The generation and execution of the locomotion stems from neuronal circuits in the spinal cord (CPG) that transform descending commands from the brain into coordinated motor patterns. While there is substantial knowledge on the organization of the spinal CPG (Arber, 2012; Bu¨schges et al., 2011; Grillner and Jessell, 2009), our understanding of how descending commands are encoded and decoded is still incomplete. In this issue of Neuron, three complementary papers, Severi et al. (2014), Thiele et al. (2014), and Wang and McLean (2014), have addressed the origin of the descending commands and how they are decoded by the spinal motoneurons to regulate steering and posture in larval zebrafish. The 5- to 6-day-old zebrafish larva has a long way to go until it matures to its adult version, but it is nevertheless an astounding neuronal machine that is
able to generate a variety of complex patterns of behavior. When it is swimming around, it is at the same time hunting for food, mostly in the form of swimming amoebae. It cannot only identify these moving targets but also steer toward them to ingest them. This preparation allows for a great variety of experimental approaches and has yielded important insights into the operation of the motor system. The Baier (Thiele et al., 2014), the Engert (Severi et al., 2014), and the McLean (Wang and McLean, 2014) laboratories report here on visually evoked swimming in the larval zebrafish. In focus is the midbrain nucleus of the medial longitudinal nucleus (nMLF) that is activated by visual stimuli, eliciting forward locomotion (Gahtan et al., 2005; Orger et al., 2008). This nucleus contains a limited number of neurons that project to the hindbrain and spinal cord; in addition, their dendrites ramify in an area in which afferents from the retina terminate (Thiele et al., 2014; Wang and McLean, 2014). Of particular interest here is that the locomo-
tor activity can be induced by natural visual input in a controlled way. Gratings of points moving in a direction from tail to head induces swimming movements, as if the larvae are trying to catch up with the moving points. In this way, bouts of swimming can be triggered, and by increasing the speed of the gratings, the larvae can be made to swim progressively faster. Severi et al. (2014) show that the larvae have two preferred modes of locomotion, slow and fast swimming. Many of the experiments are performed in a preparation with the head fixed in agar, in which visual stimuli can still trigger bouts of fictive locomotor activity, however, only in the slow speed range. Neurons in nMLF are activated when visually evoked swimming is triggered, and lesions of a limited number of nMLF neurons reduces the speed of evoked swimming, indicating that these neurons indeed contribute to driving the locomotor circuitry. Conversely, if nMLF is activated by direct electrical stimulation, locomotor activity can be triggered, indicating that this nucleus indeed can be an important
Neuron 83, August 6, 2014 ª2014 Elsevier Inc. 515
Neuron
Previews contributor for driving locomotor activity. It is interesting to note there is a discrepancy of the electrical versus optogenetic activation of nMLF on the swimming frequency and duration. Increasing the frequency of electrical stimulus pulses increases both the frequency and duration of swimming (Severi et al., 2014). By contrast, no change in the frequency of swimming or duration of the bouts is induced by optogenetic stimulation of nMLF with increasing frequencies (Thiele et al., 2014). If the larval fish shall be able to catch the moving unicellular organisms, it needs to identify their locations and be able to steer the movements toward the prey. Thiele et al. (2014) show in an elegant way that an activation of nMLF neurons on one side can lead to an isolated bending movement of the body through an activation of a special muscle group (posterior hypaxial muscles). If this occurs during swimming, it will result in steering movements toward the activated side. They also show that a graded activation of the nMLF neurons results in a progressive change of the amplitude of the bending movements. That this is important for steering is shown by the fact that an ablation of some nMLF neurons results in swimming in circles, with an increased bending on the intact side, whereas a bilateral activation of neurons in nMLF results in forward locomotion. Similarly, unilateral optogenetic activation of nMLF results in steering toward the enhanced side. The degree of asymmetric activation would seem to control the amplitude of the turning movement, indicating that the nMLF produces a graded control of turning movements and hence regulates steering and swimming direction. How the retinal input contributes to this with possible direct input to nMLF (the dendrites of nMLF neurons are in the termination field of retinal afferents) is still unclear. Previous studies show (Gahtan et al., 2005) that the retinal projections to tectum (superior colliculus in mammals) are also important for the steering control, effects probably mediated via nMLF. Since nMLF has extensive collaterals at the brainstem level, it opens up for the possibility that other descending pathways may also be activated from nMLF. The next step in the analyses is to understand how the different types of
nMLF neurons influence the spinal cord networks. Some axons extend throughout the spinal cord and others only for a few segments. They have collaterals along their axonal trajectory both in the hindbrain and spinal cord. Since nMLF can elicit locomotor activity, they will have to project directly or indirectly via other reticulospinal pathways to both locomotor network interneurons and motoneurons in the spinal cord. Wang and McLean (2014) focus here on the connectivity to motoneurons. They represent the final stage of the processing of the descending inputs from nMLF that transform a tonic drive into graded movements. The main focus is the mechanisms leading to a sequential recruitment of motoneurons by increasing levels of nMLF inputs. They do so by comparing the strength of synaptic connections between an identified nMLF neurons (MeLc) and two types of motoneurons (early-born primary [pMN] and late-born secondary [sMN]). Both MN types receive mixed electrical and excitatory postsynaptic potentials (EPSPs) from MeLc neuron. The chemical EPSPs had similar amplitude in both types of MNs, but its occurrence was less reliable in sMNs (10%) than in pMNs (40%). Despite this, the high-frequency (>200 Hz) tonic input from Melc neuron was more efficient in driving firing in sMNs than in pMNs. This seems to be due to a difference in intrinsic properties between these two MNs, with sMNs having a longer time constant and input resistance than the pMNs. Such a difference in the time constant allows for a more efficient summation of EPSPs in sMNs and hence an earlier recruitment than pMNs. Overall, this study shows that although pMNs and sMNs received equal excitatory inputs from nMLF, only sMNs are preferentially activated based on their effective temporal summation. Such a direct control of specific MN types can contribute to changes in steering during swimming and thus provide a basis for the observations of Baier and Engert labs. The interesting recruitment mechanisms revealed in the larval zebrafish are, however, not maintained, in the adult zebrafish. In the latter case, with a more elaborate motor unit differentiation, the order of activation of motoneurons and interneurons is unrelated to input resistance and is instead the result of a combi-
516 Neuron 83, August 6, 2014 ª2014 Elsevier Inc.
nation of a graded strength of synaptic drive, subthreshold conductance, and action potential threshold (Ampatzis et al., 2013; Ausborn et al., 2012; Gabriel et al., 2011). A recent study has elegantly demonstrated that optogenetic activation of a molecularly identified class of hindbrain V2a neurons drive swimming in zebrafish (Kimura et al., 2013). In addition, optogenetic activation of spinal V2a interneurons generates coordinated swimming pattern (Ljunggren et al., 2014). Whether these interneurons are the target of nMLF axons or indirectly activated descending systems remains an open question. Finally, while the direct inputs from nMLF to MNs could contribute to steering and direction control, the pattern of activation of sMNs during swimming must involve activation from premotor spinal circuits. Therefore, the final output and the graded activation of these MNs should include integration at the level of the hindbrain as well as at the spinal premotor interneuron circuit and how they convey the drive to sMNs and pMNs. Despite some remaining unsolved issues, these studies provide a series of elegant and complementary experiments on visually evoked locomotor activity. They add substantially to our understanding of how descending commands are integrated at the level of motoneurons to control visually evoked swimming and steering. Spontaneous locomotor activity that does not depend on visual input appears not to depend on nMLF (Gahtan et al., 2005) but most likely does on the other reticulospinal systems and the upstream locomotor command systems, as in other vertebrate species (Grillner, 2006). Understanding the origin of the command inputs underlying locomotion and postural adjustment (Deliagina et al., 2008) has a long history beginning with the identification of the mesencephalic locomotor region (MLR) (Shik et al., 1966). Later studies, especially in lamprey, have revealed how MLR commands are integrated in the hindbrain nuclei before they are channeled to the spinal CPG (Dubuc et al., 2008; Ryczko and Dubuc, 2013). These three studies in zebrafish have provided a novel analysis of the nature of the physiological initiation of the commands for visually evoked locomotion and steering together with the underlying synaptic mechanisms that
Neuron
Previews produce graded movements with a correct balance.
Dubuc, R., Brocard, F., Antri, M., Fe´nelon, K., Garie´py, J.F., Smetana, R., Me´nard, A., Le Ray, D., Viana Di Prisco, G., Pearlstein, E., et al. (2008). Brain Res. Brain Res. Rev. 57, 172–182.
REFERENCES
Gabriel, J.P., Ausborn, J., Ampatzis, K., Mahmood, R., Eklo¨f-Ljunggren, E., and El Manira, A. (2011). Nat. Neurosci. 14, 93–99.
Ampatzis, K., Song, J., Ausborn, J., and El Manira, A. (2013). J. Neurosci. 33, 10875–10886. Arber, S. (2012). Neuron 74, 975–989.
Gahtan, E., Tanger, P., and Baier, H. (2005). J. Neurosci. 25, 9294–9303.
Ausborn, J., Mahmood, R., and El Manira, A. (2012). Proc. Natl. Acad. Sci. USA 109, E3631– E3639.
Grillner, S. (2006). Neuron 52, 751–766.
Bu¨schges, A., Scholz, H., and El Manira, A. (2011). Curr. Biol. 21, R513–R524. Deliagina, T.G., Beloozerova, I.N., Zelenin, P.V., and Orlovsky, G.N. (2008). Brain Res. Brain Res. Rev. 57, 212–221.
Grillner, S., and Jessell, T.M. (2009). Curr. Opin. Neurobiol. 19, 572–586. Kimura, Y., Satou, C., Fujioka, S., Shoji, W., Umeda, K., Ishizuka, T., Yawo, H., and Higashijima, S. (2013). Curr. Biol. 23, 843–849.
Ljunggren, E.E., Haupt, S., Ausborn, J., Ampatzis, K., and El Manira, A. (2014). J. Neurosci. 34, 134–139. Orger, M.B., Kampff, A.R., Severi, K.E., Bollmann, J.H., and Engert, F. (2008). Nat. Neurosci. 11, 327–333. Ryczko, D., and Dubuc, R. (2013). Curr. Pharm. Des. 19, 4448–4470. Severi, K.E., Portugues, R., Marques, J.C., O’Malley, D.M., Orger, M.B., and Engert, F. (2014). Neuron 83, this issue, 692–707. Shik, M.L., Severin, F.V., and Orlovskiĭ, G.N. (1966). Biofizika 11, 659–666. Thiele, T.R., Donovan, J.C., and Baier, H. (2014). Neuron 83, this issue, 679–691. Wang, W.-C., and McLean, D.L. (2014). Neuron 83, this issue, 708–721.
Neuron 83, August 6, 2014 ª2014 Elsevier Inc. 517
Previews Kirby, L.G., Pernar, L., Valentino, R.J., and Beck, S.G. (2003). Neuroscience 116, 669–683.
Vandermaelen, C.P., and Aghajanian, G.K. (1983). Brain Res. 289, 109–119.
Lucki, I. (1998). Biol. Psychiatry 44, 151–162.
Varga, V., Sze´kely, A.D., Csillag, A., Sharp, T., and Hajo´s, M. (2001). Neuroscience 106, 783–792.
Nakamura, K. (2013). Front. Integr. Neurosci. 7, 60. Ogawa, S.K., Cohen, J.Y., Hwang, D., Uchida, N., and Watabe-Uchida, M. (2014). Cell Rep. Published online August 6, 2014. http://dx.doi.org/10. 1016/j.celrep.2014.06.042.
Wall, N.R., De La Parra, M., Callaway, E.M., and Kreitzer, A.C. (2013). Neuron 79, 347–360. Warden, M.R., Selimbeyoglu, A., Mirzabekov, J.J., Lo, M., Thompson, K.R., Kim, S.Y., Adhikari, A., Tye, K.M., Frank, L.M., and Deisseroth, K. (2012). Nature 492, 428–432.
Watabe-Uchida, M., Zhu, L., Ogawa, S.K., Vamanrao, A., and Uchida, N. (2012). Neuron 74, 858–873. Weissbourd, B., Ren, J., DeLoach, K.E., Guenthner, C.J., Miyamichi, K., and Luo, L. (2014). Neuron 83, this issue, 645–662. Wickersham, I.R., Lyon, D.C., Barnard, R.J., Mori, T., Finke, S., Conzelmann, K.K., Young, J.A., and Callaway, E.M. (2007). Neuron 53, 639–647.
Motion with Direction and Balance Abdel El Manira1 and Sten Grillner1,* 1Department of Neuroscience, Karolinska Institute, 17177 Stockholm, Sweden *Correspondence: [email protected] http://dx.doi.org/10.1016/j.neuron.2014.07.021
In this issue of Neuron, three papers, Severi et al. (2014), Thiele et al. (2014), and Wang and McLean (2014), examine the descending circuitry involved in coordinating locomotor behavior in larval zebrafish. Visually evoked locomotor and steering movements in zebrafish are mediated by neurons in the nucleus of the medial longitudinal fascicle (nMLF) that project directly to spinal motoneurons. The final motor output induced by nMLF will involve integration in the hindbrain and premotor circuits in the spinal cord. Animals navigate through their environment with extreme agility by grading the speed and strength of their locomotor movements while at the same time controlling their balance (posture). The generation and execution of the locomotion stems from neuronal circuits in the spinal cord (CPG) that transform descending commands from the brain into coordinated motor patterns. While there is substantial knowledge on the organization of the spinal CPG (Arber, 2012; Bu¨schges et al., 2011; Grillner and Jessell, 2009), our understanding of how descending commands are encoded and decoded is still incomplete. In this issue of Neuron, three complementary papers, Severi et al. (2014), Thiele et al. (2014), and Wang and McLean (2014), have addressed the origin of the descending commands and how they are decoded by the spinal motoneurons to regulate steering and posture in larval zebrafish. The 5- to 6-day-old zebrafish larva has a long way to go until it matures to its adult version, but it is nevertheless an astounding neuronal machine that is
able to generate a variety of complex patterns of behavior. When it is swimming around, it is at the same time hunting for food, mostly in the form of swimming amoebae. It cannot only identify these moving targets but also steer toward them to ingest them. This preparation allows for a great variety of experimental approaches and has yielded important insights into the operation of the motor system. The Baier (Thiele et al., 2014), the Engert (Severi et al., 2014), and the McLean (Wang and McLean, 2014) laboratories report here on visually evoked swimming in the larval zebrafish. In focus is the midbrain nucleus of the medial longitudinal nucleus (nMLF) that is activated by visual stimuli, eliciting forward locomotion (Gahtan et al., 2005; Orger et al., 2008). This nucleus contains a limited number of neurons that project to the hindbrain and spinal cord; in addition, their dendrites ramify in an area in which afferents from the retina terminate (Thiele et al., 2014; Wang and McLean, 2014). Of particular interest here is that the locomo-
tor activity can be induced by natural visual input in a controlled way. Gratings of points moving in a direction from tail to head induces swimming movements, as if the larvae are trying to catch up with the moving points. In this way, bouts of swimming can be triggered, and by increasing the speed of the gratings, the larvae can be made to swim progressively faster. Severi et al. (2014) show that the larvae have two preferred modes of locomotion, slow and fast swimming. Many of the experiments are performed in a preparation with the head fixed in agar, in which visual stimuli can still trigger bouts of fictive locomotor activity, however, only in the slow speed range. Neurons in nMLF are activated when visually evoked swimming is triggered, and lesions of a limited number of nMLF neurons reduces the speed of evoked swimming, indicating that these neurons indeed contribute to driving the locomotor circuitry. Conversely, if nMLF is activated by direct electrical stimulation, locomotor activity can be triggered, indicating that this nucleus indeed can be an important
Neuron 83, August 6, 2014 ª2014 Elsevier Inc. 515
Neuron
Previews contributor for driving locomotor activity. It is interesting to note there is a discrepancy of the electrical versus optogenetic activation of nMLF on the swimming frequency and duration. Increasing the frequency of electrical stimulus pulses increases both the frequency and duration of swimming (Severi et al., 2014). By contrast, no change in the frequency of swimming or duration of the bouts is induced by optogenetic stimulation of nMLF with increasing frequencies (Thiele et al., 2014). If the larval fish shall be able to catch the moving unicellular organisms, it needs to identify their locations and be able to steer the movements toward the prey. Thiele et al. (2014) show in an elegant way that an activation of nMLF neurons on one side can lead to an isolated bending movement of the body through an activation of a special muscle group (posterior hypaxial muscles). If this occurs during swimming, it will result in steering movements toward the activated side. They also show that a graded activation of the nMLF neurons results in a progressive change of the amplitude of the bending movements. That this is important for steering is shown by the fact that an ablation of some nMLF neurons results in swimming in circles, with an increased bending on the intact side, whereas a bilateral activation of neurons in nMLF results in forward locomotion. Similarly, unilateral optogenetic activation of nMLF results in steering toward the enhanced side. The degree of asymmetric activation would seem to control the amplitude of the turning movement, indicating that the nMLF produces a graded control of turning movements and hence regulates steering and swimming direction. How the retinal input contributes to this with possible direct input to nMLF (the dendrites of nMLF neurons are in the termination field of retinal afferents) is still unclear. Previous studies show (Gahtan et al., 2005) that the retinal projections to tectum (superior colliculus in mammals) are also important for the steering control, effects probably mediated via nMLF. Since nMLF has extensive collaterals at the brainstem level, it opens up for the possibility that other descending pathways may also be activated from nMLF. The next step in the analyses is to understand how the different types of
nMLF neurons influence the spinal cord networks. Some axons extend throughout the spinal cord and others only for a few segments. They have collaterals along their axonal trajectory both in the hindbrain and spinal cord. Since nMLF can elicit locomotor activity, they will have to project directly or indirectly via other reticulospinal pathways to both locomotor network interneurons and motoneurons in the spinal cord. Wang and McLean (2014) focus here on the connectivity to motoneurons. They represent the final stage of the processing of the descending inputs from nMLF that transform a tonic drive into graded movements. The main focus is the mechanisms leading to a sequential recruitment of motoneurons by increasing levels of nMLF inputs. They do so by comparing the strength of synaptic connections between an identified nMLF neurons (MeLc) and two types of motoneurons (early-born primary [pMN] and late-born secondary [sMN]). Both MN types receive mixed electrical and excitatory postsynaptic potentials (EPSPs) from MeLc neuron. The chemical EPSPs had similar amplitude in both types of MNs, but its occurrence was less reliable in sMNs (10%) than in pMNs (40%). Despite this, the high-frequency (>200 Hz) tonic input from Melc neuron was more efficient in driving firing in sMNs than in pMNs. This seems to be due to a difference in intrinsic properties between these two MNs, with sMNs having a longer time constant and input resistance than the pMNs. Such a difference in the time constant allows for a more efficient summation of EPSPs in sMNs and hence an earlier recruitment than pMNs. Overall, this study shows that although pMNs and sMNs received equal excitatory inputs from nMLF, only sMNs are preferentially activated based on their effective temporal summation. Such a direct control of specific MN types can contribute to changes in steering during swimming and thus provide a basis for the observations of Baier and Engert labs. The interesting recruitment mechanisms revealed in the larval zebrafish are, however, not maintained, in the adult zebrafish. In the latter case, with a more elaborate motor unit differentiation, the order of activation of motoneurons and interneurons is unrelated to input resistance and is instead the result of a combi-
516 Neuron 83, August 6, 2014 ª2014 Elsevier Inc.
nation of a graded strength of synaptic drive, subthreshold conductance, and action potential threshold (Ampatzis et al., 2013; Ausborn et al., 2012; Gabriel et al., 2011). A recent study has elegantly demonstrated that optogenetic activation of a molecularly identified class of hindbrain V2a neurons drive swimming in zebrafish (Kimura et al., 2013). In addition, optogenetic activation of spinal V2a interneurons generates coordinated swimming pattern (Ljunggren et al., 2014). Whether these interneurons are the target of nMLF axons or indirectly activated descending systems remains an open question. Finally, while the direct inputs from nMLF to MNs could contribute to steering and direction control, the pattern of activation of sMNs during swimming must involve activation from premotor spinal circuits. Therefore, the final output and the graded activation of these MNs should include integration at the level of the hindbrain as well as at the spinal premotor interneuron circuit and how they convey the drive to sMNs and pMNs. Despite some remaining unsolved issues, these studies provide a series of elegant and complementary experiments on visually evoked locomotor activity. They add substantially to our understanding of how descending commands are integrated at the level of motoneurons to control visually evoked swimming and steering. Spontaneous locomotor activity that does not depend on visual input appears not to depend on nMLF (Gahtan et al., 2005) but most likely does on the other reticulospinal systems and the upstream locomotor command systems, as in other vertebrate species (Grillner, 2006). Understanding the origin of the command inputs underlying locomotion and postural adjustment (Deliagina et al., 2008) has a long history beginning with the identification of the mesencephalic locomotor region (MLR) (Shik et al., 1966). Later studies, especially in lamprey, have revealed how MLR commands are integrated in the hindbrain nuclei before they are channeled to the spinal CPG (Dubuc et al., 2008; Ryczko and Dubuc, 2013). These three studies in zebrafish have provided a novel analysis of the nature of the physiological initiation of the commands for visually evoked locomotion and steering together with the underlying synaptic mechanisms that
Neuron
Previews produce graded movements with a correct balance.
Dubuc, R., Brocard, F., Antri, M., Fe´nelon, K., Garie´py, J.F., Smetana, R., Me´nard, A., Le Ray, D., Viana Di Prisco, G., Pearlstein, E., et al. (2008). Brain Res. Brain Res. Rev. 57, 172–182.
REFERENCES
Gabriel, J.P., Ausborn, J., Ampatzis, K., Mahmood, R., Eklo¨f-Ljunggren, E., and El Manira, A. (2011). Nat. Neurosci. 14, 93–99.
Ampatzis, K., Song, J., Ausborn, J., and El Manira, A. (2013). J. Neurosci. 33, 10875–10886. Arber, S. (2012). Neuron 74, 975–989.
Gahtan, E., Tanger, P., and Baier, H. (2005). J. Neurosci. 25, 9294–9303.
Ausborn, J., Mahmood, R., and El Manira, A. (2012). Proc. Natl. Acad. Sci. USA 109, E3631– E3639.
Grillner, S. (2006). Neuron 52, 751–766.
Bu¨schges, A., Scholz, H., and El Manira, A. (2011). Curr. Biol. 21, R513–R524. Deliagina, T.G., Beloozerova, I.N., Zelenin, P.V., and Orlovsky, G.N. (2008). Brain Res. Brain Res. Rev. 57, 212–221.
Grillner, S., and Jessell, T.M. (2009). Curr. Opin. Neurobiol. 19, 572–586. Kimura, Y., Satou, C., Fujioka, S., Shoji, W., Umeda, K., Ishizuka, T., Yawo, H., and Higashijima, S. (2013). Curr. Biol. 23, 843–849.
Ljunggren, E.E., Haupt, S., Ausborn, J., Ampatzis, K., and El Manira, A. (2014). J. Neurosci. 34, 134–139. Orger, M.B., Kampff, A.R., Severi, K.E., Bollmann, J.H., and Engert, F. (2008). Nat. Neurosci. 11, 327–333. Ryczko, D., and Dubuc, R. (2013). Curr. Pharm. Des. 19, 4448–4470. Severi, K.E., Portugues, R., Marques, J.C., O’Malley, D.M., Orger, M.B., and Engert, F. (2014). Neuron 83, this issue, 692–707. Shik, M.L., Severin, F.V., and Orlovskiĭ, G.N. (1966). Biofizika 11, 659–666. Thiele, T.R., Donovan, J.C., and Baier, H. (2014). Neuron 83, this issue, 679–691. Wang, W.-C., and McLean, D.L. (2014). Neuron 83, this issue, 708–721.
Neuron 83, August 6, 2014 ª2014 Elsevier Inc. 517
