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NEWS AND VIEWS stream of NMDA receptor activation, NO production and release, and cGMP production. By what mechanism might PIP2 speed up endocytosis? One possibility is that uptake is regulated through PIP2 stimulation of dynamin-mediated fission of vesicles from the presynaptic membrane11. In the Micheva et al. study, immuno-electron microscopy revealed that PIP2 was primarily localized to the plasma membrane in resting neurons, but upon stimulation, it was concentrated in the cytoplasm where it was often associated with vesicles. Because dynamin may mediate both slower clathrin-dependent and faster clathrin-independent endocytosis12,, activity-dependent recruitment of PIP2 to recently exocytosed vesicles could enhance local dynamin activity and accelerate both forms of vesicle retrieval. Another possibility is that elevating PIP2 changes the relative frequency of different modes of endocytosis. Three distinct forms of vesicle release and retrieval have been identified at mammalian CNS synapses: fast (20 s), in which vesicles that have fused with the membrane are left stranded on the surface until a subsequent action potential triggers their retrieval13. Because vesicles that cycle through the kiss-and-run mode may return to the readily releasable pool much faster than those vesicles that fully fuse with the plasma membrane14, the NO/PIP2 pathway could speed up both endocytosis and recycling by promoting kiss-and-run.
Consistent with this notion, PIP2 appears to increase the rate at which endocytosed vesicles become competent for re-release, as authors found that expression of the PIP2 scavenger protein slowed down the rate at which vesicles that had already been retrieved were returned to the readily releasable pool. The combined effects of PIP2 on vesicle retrieval and recycling should help neurons sustain transmitter release during long trains of action potentials—and, indeed, the authors found that it took more time for neurons expressing the PIP2 scavenger protein to recover from long pool-depleting trains of action potentials. An intriguing implication of these findings is that NO might preferentially enhance the synaptic strength of coincidentally active neurons. Although it is not clear how long the NO signal lasts, or how far it can travel, NO production triggered by the firing of a train of action potentials from a single neuron could enhance the vesicle retrieval rate at adjacent presynaptic terminals of other neurons that are active at some point during the train (i.e., those that are retrieving vesicles), even if the activity at those other synapses would not have triggered NO production on its own. This heterosynaptic signaling mechanism could speed up the rate at which all the active neurons return spent vesicles to their readily releasable pools, while having no effect on neurons that are not active during the train (i.e., those that are not retrieving vesicles). This would selectively improve the ability of temporally co-active neurons to support sustained release of neurotransmitter. Many questions remain. For example, how long does the NO-mediated enhancement of
endocytosis last? What is the minimal stimulation required to trigger effective NO release, and how do different stimulation parameters, such as train rate and duration, affect NO production? How does this retrograde signal interact with other signals, such as levels of presynaptic activity and intracellular calcium, that may also regulate rates of endocytosis15? Are inhibitory inputs modulated along with excitatory ones?? It is clear, however, that the results of the Micheva et al. study have opened a broad avenue of inquiry into modulators of presynaptic vesicle recycling and have shown that, like a good host who ensures there are enough glasses for the guests, the NO/PIP2 pathway can help to keep the synaptic party going all night long. 1. Harata, N. et al. Trends Neurosci. 24, 637–643 (2001). 2. Murthy, V.N., Sejnowski, T.J. & Stevens, C.F. Neuron 18, 599–612 (1997). 3. Micheva, K.D., Buchanan, J., Holz, R.W. & Smith, S.J. Nat. Neurosci. 6, 925–932 (2003). 4. Micheva, K.D., Holz, R.W. & Smith, S.J. J. Cell Biol. 154, 355–368 (2001). 5. Martin, T.F.J. Curr. Opin. Cell Biol. 13, 493–499 (2001). 6. Osborne, S.L., Meunier, F.A. & Schiavo, G. Neuron 32, 9–12 (2001). 7. Miesenbock, G., De Angelis, D.A. & Rothman, J.E. Nature 394, 192–195 (1998). 8. Li, Z. & Murthy, V.N. Neuron 31, 593–606 (2001). 9. Ozkan, E.D. & Ueda, T. Jpn. J. Pharmacol. 77, 1–10 (1998). 10. Holz, R.W. et al. J. Biol. Chem. 275, 17878–17885 (2000). 11. Lin, H.C. & Gilman, A.G. J. Biol. Chem. 271, 27979–27982 (1996). 12. Henkel, A.W. & Almers, W. Curr. Opin. Neurobiol. 6, 350–357 (1996). 13. Gandhi, S.P. & Stevens, C.F. Nature 423, 607–613 (2003). 14. Pyle, J.L., Kavalali, E.T., Piedras-Renteria, E.S. & Tsien, R.W. Neuron 28, 221–231 (2000). 16. Sankaranarayanan, S. & Ryan, T.A. Nat. Neurosci. 4, 129–36 (2001).
Integrating over time with dendritic wave-fronts Samuel S-H Wang & Guy Major Temporal integration, in which transient inputs shift neurons between stable firing rates, is thought to require neural networks. A new modeling study now proposes that single neurons could perform this calculation via intracellular calcium release dynamics. How can some neural circuits hold information over a time scale of many seconds, when their biophysical building blocks cannot? In
Samuel S-H. Wang is in the Department of Molecular Biology and Program in Neuroscience, and Guy Major is in the Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544, USA. e-mail:
[email protected]
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this issue, Loewenstein and Sompolinsky1 address this question by proposing a model of neural integration over time that is based on a new kind of dendritic computation. Their model is mathematically appealing, although its biological plausibility remains to be determined. A hallmark of temporal integration is multi-level persistent neural activity: neurons shifting rapidly from one stable firing rate to another in response to transient inputs. If the
change in firing rate is proportional to the input, this operation is equivalent to integration over time2. Depending on the system, firing rate may be correlated with a behavioral output such as eye position3 or a variable being held in working memory4. Such neural systems can integrate inputs that are both positive and negative, leading to increases and decreases in firing rates, respectively. What are the mechanisms that convert brief inputs into changes that last for many
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Figure 1 A dendritic cable with tuned bistability at every point can support a wave-front moving with velocity proportional to the synaptic input. The position of the wave-front gives the time integral of the inputs.
seconds? Synaptic feedback may be important in some neural integrators; for instance, in the oculomotor system, manipulations that disrupt parts of the horizontal integrator (or communication between parts of it) reduce the persistence times of eye position5 and the remaining neurons (Aksay, E. et al. Soc. Neurosci. Abstr. 27, 852.8, 2001). This is consistent with the idea that persistent neural activity is at least partly dependent on network interactions. However, if one assumes that neurons simply add up fast excitatory and inhibitory inputs, making a recurrent neural circuit multistable poses a significant practical challenge, as multistability requires precise tuning of synaptic feedback2,6. Visual feedback can slowly tune the stability of persistent firing (Major, G. et al. Soc. Neurosci. Abstr. 26, 73.14, 2000). Nonetheless, brains must also operate well under a variety of conditions, including damage to the system, developmental changes and (at least in cold-blooded animals) rapid variations in temperature. Thus, even with slow tuning, the robust performance of real biological neural integrators may not be fully accounted for by recurrent feedback models. A further place to look for mechanisms contributing to neural integration is at the level of single neurons. How would this help? One way is to imagine that a neuron is not a single point but instead is made up of many autonomous compartments. Furthermore, suppose that each compartment can have two stable voltage states: depolarized and hyperpolarized (Up and Down, respectively, in Fig. 1a). Coupling multiple bistable compartments together allows the state of one compartment to influence the states of the others, which can give rise to multiple stable states in the neuron as a whole. Neural integration could be accomplished by using many different bistable compartments. In this issue, Loewenstein and Sompolinsky1 have implemented a version of this idea based on calcium rather than voltage bistability, by making use of an intracellular chemical signaling pathway—calcium release from internal stores. Their model is composed of four key parts. First, in each short segment of dendrite, calcium-induced calcium release (CICR) and calcium clearance are tuned to generate local bistability of intracellular calcium concentration. Each segment has stable states of low and high calcium (Fig. 1b), with shifts between states if the calcium concentration is pushed
across some threshold. Second, calcium can diffuse along the dendrite from one compartment to the next. Under certain balanced conditions, a model dendrite can be made to divide into high-calcium and low-calcium zones (Fig. 1c). With careful tuning, the boundary between the zones is stationary. Third, synaptic inputs are converted to changes in calcium concentration. The wavefront moves in one direction if the input is excitatory, and in the other direction if it is inhibitory (Fig. 1c, arrows). Fourth, the readout mechanism is current generated by a calcium-activated channel such as nonspecific cationic depolarizing conductance. This current drives the firing rate. A key feature of the model is the idea that calcium release, a ubiquitous phenomenon in eukaryotic cells, including neurons, is selfamplifying7. Elevating cytoplasmic calcium increases the open probability of IP3 or ryanodine receptor release channels, enabling calcium release to enhance further release. In the case of ryanodine receptors, calcium alone is sufficient to activate the channel. In the model (Fig. 1b), at low calcium levels, little calcium release is active and the concentration is set by mechanisms of calcium extrusion and reuptake. At high levels of calcium, conditions exist under which the level of calcium release is exactly sufficient to counteract extrusion/reuptake, allowing calcium to be ‘latched’ at a high level. The beauty of the wave-front model is that it achieves analog temporal integration by using a single extended dendritic cable with local bistability at every point, and it does this without synaptic feedback. Like synaptic feedback-based attempts at modeling temporal integration (see discussion in ref. 8), this model still requires fine tuning of global parameters, in this case calcium release, clearance and readout. This sensitivity to parameters is unsurprising given that the core mechanism is positive feedback. Readout errors may become a particular problem in dendrites of realistic lengths3 (∼1000 µm as opposed to ∼30 µm in the model). The model can be made more robust with larger compartments, but at the price of losing precise temporal integration. Some help comes from the fact that the wave-front model can be elaborated to include multiple and branching dendrites, the outputs of which can be combined. This confers at least some degree of robustness to local parameter variations and random noise.
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a Up state
Down state Extracellular Closed
Open
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Hyperpolarized
b Down state
Up state
Endoplasmic reticulum Open
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c Up
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Wavefront A more serious problem in the model is the absence of an explicit mechanism for converting synaptic input to changes in calcium concentration. Even if a kick of calcium did drive a dendritic compartment into a highcalcium state, how could inhibition drive calcium down again? Speculatively, we suggest that there could be background steady-state calcium entry through voltage-gated channels, balanced by calcium pumping. An excitatory input could open more channels, causing an increase in calcium. An inhibitory input could close calcium channels, causing a dip in calcium. Another omission is the failure to consider two known features of calcium release: gating by second messengers such as IP3 and channel inactivation. Calcium release often requires IP3, which is produced by extracellular activation of neurotransmitter or neuromodulator receptors. This provides a means for synaptic activity to trigger calcium release. But release duration is also limited by IP3 degradation and diffusion, suggesting that a brief input would elevate calcium for less than a second9. Therefore, IP3-based calcium release is unlikely to evoke a persistent increase in calcium concentration. Nevertheless, it might be
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NEWS AND VIEWS very well suited to be a trigger mechanism for upward transitions. This leaves as a candidate mechanism true CICR via ryanodine receptors. But ryanodine receptors (and for that matter IP3 receptors) usually show at least some calcium-dependent inactivation (and adaptation). These phenomena would lead to problems such as unwanted drifts in the wave-front between inputs, integration errors, and even termination of the signal. Indeed, to our knowledge, CICR-based waves are experimentally observed to propagate10 and are not ‘balanced’ to give a nonmoving front. However, ryanodine receptor inactivation is heterogeneous; the (rare) RyR3 subtype is steeply calcium-activated and is only weakly inactivating11, providing a possible substrate for the model. Some of the problems outlined above can be circumvented by returning to voltagebased mechanisms. At the mathematical level there is no difference between the cable equation (which describes the spread of charge) and the reaction-diffusion equation (which describes the spread of ions and molecules). Furthermore, there is no real mathematical difference between voltage6 and calcium bistability (Fig. 1a,b). The idea that spatial activation patterns in bistable dendrites could contribute a single-cell mechanism to multistable persistent neural activity is also being explored in other integrator models12. A natural extension would be to implement the wave-front model by
combining local voltage bistability and the cable equation. Mechanisms based on voltage multistability have several advantages. The voltage-dependent conductances underlying such a scheme could include persistent sodium, long-lasting calcium and NMDA conductances, all of which have an established track record of involvement in bistability and indeed multistability in many different neuron types13. In particular, NMDA conductance is abundant in candidate integrator neurons (Wang, S.S.-H. et al. Soc. Neurosci. Abstr. 24, 602.5, 1998) and is necessary for normal integration in vivo14. Because these conductances are voltage-gated, inputs could drive both upward and downward transitions. A voltage-based mechanism also removes the need for a calcium-to-firing conversion step. One weakness is that the dendritic cable would be much more compact electrically than for calcium diffusion, but if membrane conductance is sufficiently high, as may occur in vivo, dendrites may be sufficiently ‘long’ in electrical terms to support plateau potential wave-fronts. Cable nonuniformities or heterogeneity12 may be required for correct readout. Another weakness of the voltage wave-front idea is that it does not explain why transient intracellular current injections in integrator neurons do not evoke persistent changes in firing1,15. However, this discrepancy can also be explained if current injections affect additional conductances that mask changes in dendritic state15. In short, a voltage-based proposal would be mathemati-
cally similar to the existing calcium-based model, and may be more plausible biologically. Models that have aimed to explain how neural circuits generate persistent activity have suggested various underlying mechanisms, including tuned recurrent feedback, cellular or dendritic bistability, and now calcium release wave-fronts. Neural circuits may even use some combination of these to improve robustness12. The definitive tests, however, will be experimental. To paraphrase the embryologist Viktor Hamburger,“Our real teacher is the nervous system, who is, incidentally, the only teacher who is always right.” 1. Loewenstein, Y. & Sompolinsky, H. Nat. Neurosci. 6, 961–967 (2003). 2. Robinson, D.A. Annu. Rev. Neurosci. 12, 33–45 (1989). 3. Aksay, E., Baker, R., Seung, H.S. & Tank, D.W. J. Neurophysiol. 84, 1035–1049 (2000). 4. Goldman-Rakic, P.S. Neuron 14, 477–485 (1995). 5. Arnold, D.B. & Robinson, D.A. Exp. Brain Res. 113, 57–74 (1997). 6. Koulakov, A.A., Raghavachari, S., Kepecs, A. & Lisman, J.E. Nat. Neurosci. 5, 775–782 (2002). 7. Berridge, M.J., Lipp, P. & Bootman, M.D. Nat. Rev. Mol. Cell Biol. 1, 11–21 (2000). 8. Seung, H.S., Lee, D.D., Reis, B.Y. & Tank, D.W. J. Comp. Neurosci. 9, 171–185 (2000). 9. Khodakhah, K. & Ogden, D. J. Physiol. 487, 343–358 (1995). 10. Jaffe, L.F. BioEssays 21, 657–667 (1999). 11. Fill, M. & Copello, J.A. Physiol. Rev. 82, 893–922 (2002). 12. Goldman, M.S., Levine, J.H., Major, G., Tank, D.W. & Seung, H.S. Cereb. Cortex (in press). 13. Simon, M., Perrier, J.F. & Hounsgaard, J. Eur. J. Neurosci. 18, 258–266 (2003). 14. Mettens, P., Cheron, G. & Godaux, E. Neuroreport 5, 1333–1336 (1994). 15. Aksay, E., Gamkrelidze, G., Seung, H.S., Baker, R. & Tank, D.W. Nat. Neurosci. 4, 184–193 (2001).
You can’t teach a middle-aged ganglion new tricks Winfried Denk Repeated imaging of the same individual neuron for over a year in mice allows the authors of a new study in this issue to show that presynaptic axon terminals become progressively more stable as the animals age, changing little after 6 to 12 months. A typical neuron has 10,000 neighbors, as compared to only tens or at most hundreds for other cells of the body. It matters, of course, not only how many neighbors you have but also which of your neighbors you talk to and who talks back to you. Furthermore, the complexity of the fully developed mammalian nervous system is
Winfried Denk is in the Department of Biomedical Optics, Max-Planck Institute for Medical Research, Jahn Strasse 29, Heidelberg D-69120, Germany. e-mail:
[email protected]
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many orders of magnitude larger than what could be possibly genetically determined. It is all the more important, therefore, to understand the rules governing neural (self-) organization, particularly during development, repair and adult plasticity. We have known how neurons look, down to the finest axonal and dendritic branches, since Golgi discovered the eponymous staining method1 and highly corrected microscope optics were perfected. More recently, electron microscopy refined this picture and allowed, in particular, the unambiguous identification of synaptic connections. All
these histological methods, however well they resolve fine details, work only on fixed tissue, thus completely missing information on neural activity and, even more important in the present context, being unable to detect the dynamics of individual morphological features. To assay such dynamics requires repeated imaging of the very same neuron in the very same animal, which to date only optical microscopy can deliver. But imaging in intact tissue or intact animals still faces substantial obstacles. In a study reported in this issue, Gan and colleagues have overcome these obsta-
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