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of the mice only during the acquisition or retrieval phases of the various tasks. Additionally, experiments in older animals may reveal age-dependent deficits resulting from reduced VAChT expression (Paban et al., 2005). The work also suggests a role for cholinergic transmission in behaviors involving social preference, independent of learning, and it will be interesting to explore this further. Limitations of the knockdown mutation are that ACh release is not completely eliminated, and that the mutation affects release at all cholinergic synapses. Although acetylcholine has a role in behavior mediated by the hippocampus, it also acts in cortex and striatum, and its role in these locations is less well understood. It is thus important to note that the construct used by Prado et al. contains loxP sites flanking exon 1 (Prado et al., 2006), which should enable conditional deletion of the gene in specific central cholinergic populations. To understand how the presynaptic regulaton of quantal size contributes to synaptic transmission, we must first understand the mechanisms responsible. If synaptic vesicles fill to an equilibrium dictated by the ionic coupling of the transporter, then having one transport protein per vesicle should produce the same quantal size as several transporters (Daniels et al., 2006). On the other hand, high rates of recycling may limit the time available for filling, making quantal size dependent on the number of transporters. Increased expression may also serve to offset a nonspecific leak through the vesicle membrane (the ‘‘leaky bathtub’’ model) (Williams, 1997). In addition, it is important to note that all postsynaptic measurements of quantal size involve spontaneously released vesicles, and that these may differ from vesicles capable of evoked release, particularly at central synapses (Sara et al., 2005). Further, there are no measurements of quantal size during high-frequency stimulation of neurons, so we still understand little about how vesicle filling contributes to synaptic depression. Nonetheless, Prado et al. demonstrate the potential for presynaptic regulation of quantal size to alter synaptic physiology in a behaviorally relevant context (Prado et al., 2006). Thomas S. Hnasko1,2 and Robert H. Edwards1,2 1 Department of Neurology 2 Department of Physiology School of Medicine University of California, San Francisco 600 16th Street, GH-N272B San Francisco, California 94158 Selected Reading Coyle, J.T., Price, D.L., and DeLong, M.R. (1983). Science 219, 1184– 1190. Croft, B.G., Fortin, G.D., Corera, A.T., Edwards, R.H., Beaudet, A., Trudeau, L.E., and Fon, E.A. (2005). Mol. Biol. Cell 16, 306–315. Daniels, R.W., Collins, C.A., Chen, K., Gelfand, M.V., Featherstone, D.E., and Diantonio, A. (2006). Neuron 49, 11–16. Fon, E.A., Pothos, E.N., Sun, B.-C., Killeen, N., Sulzer, D., and Edwards, R.H. (1997). Neuron 19, 1271–1283. Mainen, Z.F., Malinow, R., and Svoboda, K. (1999). Nature 399, 151–155. Paban, V., Jaffard, M., Chambon, C., Malafosse, M., and AlescioLautier, B. (2005). Neuroscience 132, 13–32.

Parsons, R.L., Calupca, M.A., Merriam, L.A., and Prior, C. (1999). J. Neurophysiol. 81, 2696–2700. Patel, J., Mooslehner, K.A., Chan, P.M., Emson, P.C., and Stamford, J.A. (2003). J. Neurochem. 85, 898–910. Pothos, E.N., Larsen, K.E., Krantz, D.E., Liu, Y.-J., Haycock, J.W., Setlik, W., Gershon, M.E., Edwards, R.H., and Sulzer, D. (2000). J. Neurosci. 20, 7297–7306. Prado, V.F., Martins-Silva, C., de Castro, B.M., Lima, R.F., Barros, D.M., Amaral, E., Ramsey, A.J., Sotnikova, T.D., Ramirez, M.R., Kim, H.-G., et al. (2006). Neuron 51, this issue, 601–612. Sara, Y., Virmani, T., Deak, F., Liu, X., and Kavalali, E.T. (2005). Neuron 45, 563–573. Song, H.-j., Ming, G.-l., Fon, E., Bellocchio, E., Edwards, R.H., and Poo, M.-m. (1997). Neuron 18, 815–826. Takahashi, N., Miner, L.L., Sora, I., Ujike, H., Revay, R.S., Kostic, V., Jackson-Lewis, V., Przedborski, S., and Uhl, G.R. (1997). Proc. Natl. Acad. Sci. USA 94, 9938–9943. Van der Kloot, W. (1990). Prog. Neurobiol. 36, 93–130. Wang, Y.-M., Gainetdinov, R.R., Fumagalli, F., Xu, F., Jones, S.R., Bock, C.B., Miller, G.W., Wightman, R.M., and Caron, M.G. (1997). Neuron 19, 1285–1296. Williams, J. (1997). Neuron 18, 683–686. Zhou, Q., Petersen, C.C.H., and Nicoll, R.A. (2000). J. Physiol. 525, 195–206. DOI 10.1016/j.neuron.2006.08.019

Subplate Neurons Foster Inhibition Previous work demonstrates an essential role of subplate neurons during ocular dominance (OD) column formation in the developing visual cortex. While inhibitory circuitry has also been shown to play an essential role in OD plasticity, the relationship between subplate neurons and the development of inhibitory circuits has been unclear. In this issue of Neuron, Kanold and Shatz provide evidence that maturation of inhibitory circuitry requires subplate neurons in the developing cortex. Visual cortex is the first stage of the mammalian visual pathway where information from the two eyes, relayed through the thalamus, is combined. During brain development, thalamic axons from the two eyes are initially overlapped in layer 4 of the developing visual cortex, but during subsequent development, they are segregated into eye-specific patches (OD columns) (Figure 1A; Hubel et al., 1977; Levay et al., 1980). Prolonged monocular deprivation (occlusion of one eye) can cause a shift in ocular dominance, in which cortical neurons become responsive exclusively to the open eye (Wiesel and Hubel, 1963). In the visual cortex of the monocular deprived animal, the thalamic axons driven by the open (active) eye occupy a larger territory, while the territory occupied by the axons driven by the occluded (less active) eye shrink (Hubel et al., 1977; Levay et al., 1980). The OD shift can be induced only during a short period after the time of natural eye opening, called the critical period (Hubel and Wiesel, 1970). Thus, the OD plasticity is specific to a restricted period in early cortical development.

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Figure 1. Involvement of Subplate Neurons in Ocular Dominance Plasticity (A) Ocular dominance column development. At an early stage of cortical development, thalamic axons from the two eyes are initially overlapped in layer 4 of the developing visual cortex (left panel). Subsequently, they segregate into eye-specific patches (OD columns, right panel). (B) The STDP model proposed by Kanold and Shatz. (Top left panel) During OD segregation, subplate neurons provide strong excitatory inputs to layer 4 neurons at the same time that thalamic afferents innervate both subplate neurons and layer 4 neurons. (Bottom left panel) Weak thalamic inputs onto layer 4 neurons are immediately followed by strong depolarization (postsynaptic activity) elicited by the subplate neurons that are concurrently driven by the thalamic inputs. Occurrence of postsynaptic activity is slightly delayed relative to the thalamic inputs due to the disynaptic transmission of the excitation from the thalamic axons. Thalamic inputs preceding the postsynaptic activity are well within a short time window for long-term potentiation (LTP) (green box), resulting in synaptic potentiation that occurs between the thalamic afferent and the layer 4 neuron. (Top right panel) In the subplate-ablated cortex, spontaneous activity is high in layer 4 neurons due to decreased inhibition. (Bottom right panel) Arrival time of the thalamic inputs relative to postsynaptic activity is random in layer 4 neurons. Since most of the thalamic inputs are within a broader time window for long term depression (LTD) (red box), synapses between the thalamic afferent and the layer 4 neuron undergo depression.

Subplate neurons are the first postmitotic neurons to receive synaptic inputs from thalamic axons in the developing neocortex (for reviews, see Allendoerfer and Shatz, 1994; McAllister, 1999). Subplate neurons first send their axons to the cortical plate, and subsequently, while maintaining contact with the subplate neurons, thalamic axons invade the cortical plate. During OD segregation of thalamic axons, layer 4 cells are dually innervated by both subplate and thalamic axons (Figure 1B, left panel). Because of the intimate contact of layer 4 cells with both subplate and thalamic neurons, the absence of OD columns in the cortex after early ablation of subplate neurons (Ghosh and Shatz, 1992) has sug-

gested an essential role of the subplate neurons during OD column formation. The absence of OD columns after ablation of subplate neurons has been accounted for by the overexpression of BDNF, a neurotrophic factor (Lein et al., 1999). The presence of increased BDNF levels in the subplate-ablated cortex eliminates competition between the left/right eye-driven thalamic axons, which occurs in the normal cortex because of limited amounts of BDNF. In this issue of Neuron, Kanold and Shatz (2006) provide a different and fresh view of the role of subplate neurons in visual cortical development. They report that in the subplate-ablated cortex, maturation of inhibitory synapses fails to occur. The gene expression pattern of a subset of GABAA receptor subunits in the subplate-ablated cortex is reminiscent of the immature cortex. Furthermore, the expression of the K+-Cl2 cotransporter KCC2 fails to be upregulated in layer 4 neurons, which causes continued depolarization of neurons by GABA, rather than inducing the switch to hyperpolarization that normally occurs. Thus, the GABAergic maturation in the layer 4 neurons is prevented without subplate neurons. In addition, without subplate neurons, they report a paradoxical shift of OD toward the occluded eye rather than the open eye after monocular deprivation. Surprisingly, the less active afferents representing the occluded eye occupy a much larger territory in the subplateablated cortex. Kanold and Shatz propose a model based upon the spike timing-dependent plasticity (STDP) rule, originally described in Markram et al. (1997), to explain this paradoxical OD shift. In the STDP rule, synapses are potentiated or depressed according to the relative timing of pre- and postsynaptic activity: synaptic depression occurs when presynaptic activity follows postsynaptic spikes, while synaptic potentiation occurs when presynaptic activity precedes postsynaptic spikes, as during normal synaptic transmission (for reviews, see Abbott and Nelson, 2000; Dan and Poo, 2004). An important component of their model, which is based on experimental observations, is that, in the normal cortex, subplate neurons already receive strong thalamic input during the time that synapses between thalamic axons and layer 4 neurons are just developing. Additionally, subplate neurons also provide strong excitatory input to layer 4 neurons at this time (Figure 1B, top left panel). Thus, while thalamic inputs alone cannot strongly depolarize layer 4 neurons, this activity will be immediately followed by strong depolarization elicited by the subplate neurons that are concurrently driven by the thalamic inputs (Figure 1B, bottom left panel). This correlated input activity will lead to the strengthening of the synapse between thalamic axons and layer 4 neurons. Without subplate neurons, however, thalamic inputs cannot reliably depolarize layer 4 neurons. Furthermore, layer 4 neurons exhibit higher spontaneous activity due to decreased inhibition, which is not correlated to the thalamic inputs. Therefore, thalamic inputs occur at random relative to postsynaptic activity (Figure 1B, bottom right panel). In their model, the STDP time window for synaptic depression (red stippled box in Figure 1B) is much broader than for potentiation (green stippled box in Figure 1B). Because of this asymmetry, there is an imposed bias toward synaptic depression. Thus, more active inputs from the open eye

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undergo increased synaptic depression relative to those of the less active closed eye. While the asymmetric STDP rule explains the paradoxical OD shift in the subplate-ablated cortex, it may also be possible to explain this reverse OD shift with other synaptic plasticity mechanisms. Consider a situation in which a presynaptic neuron cannot effectively depolarize the postsynaptic neuron, which is what is believed to occur between thalamic inputs to layer 4 neurons in the subplate-ablated cortex. In this case, the synapse should simply undergo a weakening according to the well-known mechanism of long-term depression (LTD) (Artola, et al., 1990; Dudek and Bear, 1992; Mulkey and Malenka, 1992). Thus, the more active inputs from the open eye undergo more severe synaptic depression than the less active inputs of the closed eye. This form of homosynaptic LTD also explains the paradoxical reverse OD shift observed in the pharmacologically inhibited cortex (Hata and Stryker, 1994; Hata et al., 1999). In the inhibited cortex, achieved by the infusion of the GABAA receptor agonist muscimol that strongly hyperpolarizes postsynaptic neurons, thalamic afferents are unable to depolarize layer 4 neurons, which is the similar situation that is found in the subplate-ablated cortex. In separate studies, it has been previously shown that inhibition (Hensch, et al., 1998; Huang, et al., 1999) and subplate neurons (Ghosh and Shatz, 1992) both play an essential role in OD plasticity. While these studies hinted at a linkage between subplate neurons and developing inhibitory circuits, this was never directly demonstrated. The present report provides the first strong evidence that subplate neurons are required for the normal development of inhibitory circuitry. While additional studies will also be needed to more fully establish the causal relationship between the paradoxical OD shift and the absence of inhibition in the subplate-ablated cortex, the proposal of Kanold and Shatz is compelling. Tomokazu Ohshiro1 and Michael Weliky1 1 Department of Brain and Cognitive Sciences University of Rochester Rochester, New York 14627 Selected Reading Abbott, L.F., and Nelson, S.B. (2000). Nat. Neurosci. Suppl. 3, 1178– 1183. Allendoerfer, K.L., and Shatz, C.J. (1994). Annu. Rev. Neurosci. 17, 185–218. Artola, A., Brocher, S., and Singer, W. (1990). Nature 347, 69–72. Dan, Y., and Poo, M.-m. (2004). Neuron 44, 23–30. Dudek, S.M., and Bear, M.F. (1992). Proc. Natl. Acad. Sci. USA 89, 4363–4367. Ghosh, A., and Shatz, C.J. (1992). Science 255, 1441–1443. Hata, Y., and Stryker, M.P. (1994). Science 265, 1732–1735. Hata, Y., Tsumoto, T., and Stryker, M.P. (1999). Neuron 22, 375–381. Hensch, T.K., Fagiolini, M., Mataga, N., Stryker, M.P., Baekkeskov, S., and Kash, S.F. (1998). Science 282, 1504–1508. Huang, Z.J., Kirkwood, A., Pizzorusso, T., Porciatti, V., Morales, B., Bear, M.F., Maffei, L., and Tonegawa, S. (1999). Cell 98, 739–755. Hubel, D.H., and Wiesel, T.N. (1970). J. Physiol. 206, 419–436. Hubel, D.H., Wiesel, T.N., and Levay, S. (1977). Philos. Trans. R. Soc. Lond. B Biol. Sci. 278, 377–409.

Kanold, P.O., and Shatz, C.J. (2006). Neuron 51, this issue, 627–638. Lein, E.S., Finney, E.M., McQuillen, P.S., and Shatz, C.J. (1999). Proc. Natl. Acad. Sci. USA 96, 13491–13495. Levay, S., Wiesel, T.N., and Hubel, D.H. (1980). J. Comp. Neurol. 191, 1–51. Markram, H., Lubke, J., Frotscher, M., and Sakmann, B. (1997). Science 275, 213–215. McAllister, A.M. (1999). Proc. Natl. Acad. Sci. USA 96, 13600–13602. Mulkey, R.M., and Malenka, R.C. (1992). Neuron 9, 967–975. Wiesel, T.N., and Hubel, D.H. (1963). J. Neurophysiol. 26, 1003–1017. DOI 10.1016/j.neuron.2006.08.022