Neuralarchitecturesfor adaptivebehavior D. W. M o r t o n and H. J. Chiel
How do animals use the same peripheral structures to generate different behavioral responses? Three different neuronal architectures have been proposed to mediate this task: dedicated circuitry; distributed circuitry; and reorganizing circuitry. This review will critically examine the evidence for these different architectures in invertebrate circuits, and then examine the evidence for them in more complex vertebrate circuits. The evidence suggests that these different architectures are unlikely to be found in pure form in most neural circuits, but are useful for guiding the experimental analysis of circuitry. Animals often use the same peripheral structures for a variety of motor acts. For example, an animal might use the same limb for locomotion or scratching, or might use its feeding apparatus for biting, chewing, swallowing or rejecting food. What are the neural mechanisms that enable animals to generate the different motor patterns that mediate these behavioral responses? Focussing on invertebrate preparations, in which the circuitry is more completely characterized, we will review the different neural architectures that have been proposed to accomplish this task, and the experimental evidence for them. The utility of these architectural categories for guiding the analysis of vertebrate circuits whose cellular properties have not yet been fully described will be examined. A more general review of the common principles of motor control in vertebrates and invertebrates has recently been published 1.
In distributed circuitry, the response to any input is distributed over a population of neurons, with each neuron contributing a small amount to the final behavioral response. Changing activity in the neural population generates different responses (Fig. 1C). An implication of this circuit design is that elements of the circuit receive sensory inputs from many areas of the periphery and contribute to motor outputs that affect many of these areas z. These broad motor outputs enable the entire circuit to have access to the periphery during all responses. In contrast to the other two architectures, which seem to subserve behaviors composed of a finite number of discrete responses, this architecture is used for behaviors composed of a continuously varying range of responses. However, since the periphery does not enable these responses to be expressed simultaneously, they can be distinguished from one another. For example, an animal cannot move an arm to the left and to the fight at the same time (Table I). How well do these categories describe circuits that have been analyzed experimentally? How useful are they for the analysis of a novel circuit?
D. W. Morton and H. J. Chielare at the Deptsof Neuroscienceand Biology, Case Western Reserve University, Cleveland, 0H44106, USA.
Dedicated circuitry
Recent studies provide compelling evidence that the expression of different, but related, behaviors can be mediated by separate, dedicated circuits. An example of dedicated circuitry is observed in the pteropod mollusk Clione limacina. Clione swims continuously to maintain positive buoyancy (see Ref. 5 for a recent review of the swim central pattern Architectures for adaptive behavior Three distinct neural architectures have been generator). To do this, the animal's swim wings must described in the literature for the generation of be extended. Touching one of the animal's wings, different behavioral responses: dedicated circuitry; however, causes a rapid wing retraction and an distributed circuitry; and reorganizing circuitry. immediate interruption of the swimming motor Evidence suggests that a given neural circuit can have pattern. Huang and Satterlie have shown that two features of one, two, or all three of these architec- separate neural circuits generate and co-ordinate the tures. However, for clarity, we will describe each movements associated with these two behaviors 6 architecture separately. (Figs 2A and 3A). A tactile stimulus to the swimming In dedicated circuitry, each behavioral response is wing activates the wing retraction circuit, which generated by a separate, dedicated neural circuit. In consists of a number of mechanoafferent neurons that response to a particular input, one of the dedicated activate wing-retraction interneurons. In turn, these circuits exerts control over the periphery, producing a interneurons cause rapid wing retraction by directly specific response, while other circuits are suppressed activating wing-retraction motor neurons. Simul(Fig. 1A). Since only one circuit can control the taneously, retraction interneurons and motor neurons periphery at any time, no conflicts between different inhibit both the swim pattern generator and swim uses of the periphery can arise. An implication of this motor neurons. Consequently, swimming stops until circuit design is that the elements of the circuit activity in the retraction circuitry has ceased. Thus, receive the sensory inputs, and provide the motor only one circuit has control of the periphery at a time, outputs that are necessary only for the behavioral and it appears that different neurons are used for response that they generate z (Table I). generating different responses. In reorganizing circuitry, different responses are Though the swim circuit comprises a dedicated produced by altering the circuit. This might be caused circuit for swimming responses, by the criteria of by the addition or removal of neurons, changes in the Table I, this circuit might also be subject to reintrinsic properties of neurons, or a change in the organization in order to produce two types of effective synaptic connections between them (Fig. swimming responses. 5-HT, which mimics many of 1B). This architecture is similar to the polymorphic the effects of a strong touch to the animal's tail, might network concept described by Getting and Dekin 3, or act to reconfigure the swimming circuit from slow to to the different configurations concept described by fast (escape) swimming5, suggesting that a dedicated Weimann and colleagues4 (Table I). architecture does not exclude reorganization. TINS, Vol. 17, No. 10, 1994
© 1994,Elsevier Science Ltd
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Fig. 1. Diagrams of three circuit architectures for generating different behavioral responses. Excitatory synaptic connections are indicated using a straight bar," inhibitory synaptic connections are indicated using a sofid bali. (A) Dedicated circuitry. In response to Input 1, switching circuitry turns on Circuit 1 and suppresses Circuit 2, activating motor neurons that generate Response 1. In response to Input 2, Circuit 2 is active and Circuit 1 is suppressed, and Response 2 is generated. (B) Reorganizing circuitry. Input 1 activates part of the neural population, leading to Response 1. Input 2 causes changes in the neural circuit (that is, in the connectivity or the intrinsic properties of neurons), leading to Response 2. (C) Distributed circuitry. (Left.) A schematic representation of a distributed circuit. Inputs and outputs of intemeurons are broadly distributed. For simplicity, no feedback connections have been shown. (Right.) A schematic representation of the computation within this circuit. In response to Input 1, broadly tuned members of the neural population respond more or less strongly (represented by thin arrows), leading to an overall population response (represented by bold arrow), and the motor neurons are activated so as to generate Response 1. A different input leads to a different pattern of activation of the population, and thus generates Response 2.
Another example of dedicated circuitry is found in the locust (Locusta migratoria). Two forms of locomotion, flying and walking, appear to be mediated by separate pattern generators that only weakly interact 9. This hypothesis is supported by three lines of evidence. First, Ramirez and Pearson 9 have shown that, in a semi-intact preparation, both the walking and the flight motor patterns could be observed at the same time, but with different frequencies, in motor neurons innervating bifunctional muscles (that is, muscles that can move a wing and a leg and, thus, participate in both behaviors). Second, flight interneurons were only active during the flight pattern, 414
whereas walking interneurons were only active during the walking pattern. Third, interneurons for flight had processes that were found primarily in different regions of the ganglion than were those for walking, suggesting that the circuits subserving these two behaviors were physically separated wittfin the ganglion. Since the presence or absence of tarsal contact can cause an animal to select either walking or flying 1°, it is likely that these two patterns do not occur simultaneously in intact animals. Thus, it appears that separate interneurons mediate flight and walking pattern generation, have specific motor outputs appropriate for each behavior, and that their TINS, Vol. 17, No. 10, 1994
TABLE I. Circuit architectures Architecture
Neural circuit mediating different responses*
Pattern of synaptic connectivity
Control over periphery
Number of responses
Dedicated
Different set of neurons for each response
Specific sensory inputs and specific motor outputs
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A few discrete responses
Reorganizing
Circuit changes. Cells might enter or leave, effective connections may change, and/or intrinsic properties might change
State dependent, since some neurons participate in multiple responses
Reorganized circuit has control of periphery
A few discrete responses
Distributed
A single population of neurons is used for all responses
Broad sensory inputs and broad motor outputs
All parts of circuit have access to periphery
Continuously varying over a range
* Motor neurons might be included in the neural circuit.
outputs might be gated so that only one set has control of the periphery during each behavior. It should be noted that these circuits might share local interneurons that are responsible for co-ordinating the activity of many motor neurons u.
Reorganizing circuitry Evidence that reorganizing circuitry might mediate the generation of different behavioral responses using the same peripheral structures has been found in several different systems. The strongest evidence comes from studies of the crustacean stomatogastric nervous system, which contains relatively small numbers of identified cells whose properties and interconnections have been extensively characterized. A comprehensive review of this system has recently been published 12. The stomatogastric nervous system can undergo extensive reorganization in response to sensory inputs and modulatory substances. In their recent review, Dickinson and Moulins describe four kinds of reorganization that have been observedl:~: (1) Single neurons can switch from one pattern to another7,14 ]6 (2) Multiple neurons can switch from one pattern to another 4,17. (3) Two pattern generators can fuse to form a different pattern generatoP s. (4) Many of the neurons from three distinct pattern generators can fuse to form a different pattern generator 19.
applied to its tail cause it to generate an escape swim by alternately contracting its dorsal and ventral musculature. Getting and Dekin 3 hypothesized that light touch activates the dorsal swim interneurons (DSIs), which polysynaptically inhibit one another, causing a withdrawal response and preventing an escape swim. The stimuli that induce an escape swim activate cerebral neuron C2, which inhibits a hypothesized inhibitory interneuron between the DSIs. This change in effective connectivity enables the DSIs to excite one another, and causes the circuit to generate the rhythmic bursting output of escape swimming.
Distributed circuitry Experimental evidence suggests that different behavioral responses might also be generated by distributed circuitry. For example, in the medicinal leech, a local bending reflex enables the animal to flex its body away from the site of a skin touch. The bending direction appears to be mediated by a population of dorsal local bending interneurons (dorsal LBIs). It has been proposed that the computation of the withdrawal direction is distributed over the population of the dorsal LBIs, with each one contributing a small amount to the bending directions. Experimental evidence supports this hypothesis: first, each dorsal LBI integrates inputs from ventral as well as dorsal mechanoreceptors; second, hyperpolarizing any of the dorsal LBIs causes a small decrease in the motor output of the reflex, suggesting that all of the LBIs contribute to it; and third, each of the dorsal LBIs has widespread outputs, connecting to both dorsal and ventral motor neurons. Examples of this are shown for LBIs 125 and 115 (see Figs 2C and 3C). A computer model of this circuitry has demonstrated that these results are consistent with a distributed computation2°. Though the dorsal LBIs comprise a distributed circuit subserving the local bending reflex by the criteria of Table I, they might also be subject to reorganization when the animal carries out other behavioral responses. For example, they might join other circuits. Dorsal LBI 125 is also involved in shortening; dorsal LBI 115 is involved in shortening and swimming~1'22.
An example of the switching of a neuron from one pattern to another is the change in the activity of the ventral dilator (VD) neuron in the lobster Palinurus vulgaris 7. Activation of the cardiac sac pattern causes the VD neuron to leave the pyloric network and to join the cardiac sac network (Figs 2B and 3B). When the cardiac sac network is active, neurons in that network drive VD neurons with large EPSPs. At the same time, the pyloric network loses its ability to drive VD neurons because their intrinsic membrane properties change. Specifically, VD neurons lose their ability to generate plateau potentials ~5. An example of reorganization in a molluscan nervous system is seen in the circuit generating defensive responses in Tritonia. Light touch to the animal causes it to withdraw, that is, to simultaneously Architectures of vertebrate circuits contract its ventral and dorsal musculature. Contact Vertebrates appear to possess circuitry that with a predatory starfish or high salt concentrations satisfy the criteria for dedicated, distributed, or TIN& VoL 17, No. 10, 1994
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reorganizing neural architectures. An example of a vertebrate circuit that is dedicated but might also have distributed characteristics is provided by the rapid escape response mediated by the Mauthner cells and by Mauthner-like cels 2z. In teleosts, the Mauthner cells receive specific sensory inputs, and the axons of these cells cross the midline, and synapse on spinal cord motor neurons that control trunk musculature, suggesting that, when they are activated, they can cause the body to contract away from the stimulus that induced them to fire. In freely moving animals, 416
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the onset of firing in these cells is correlated with the onset of rapid escape responses. Intracellular stimulation of the Mauthner cells activates musculature associated with the escape response, and hyperpolarization of these cells prevents this muscular response to stimuli that initiate escape23. Thus, these cells possess very specific sensory inputs and motor outputs, and appear to be involved only in escape behavior. Neuroanatomical studies indicate that the Mauthner cells are part of a larger population of Mauthner-like TINS, VoL 17, No. 10, 1994
Fig. 2. (Left.)Experimental examples of the three different circuit architectures. Excitatory synaptic connections are indicated using a straight bar, inhibitory synaptic connections are indicated using a solid ball, and electrical connections are indicated by a resistor symbol. (A) Dedicated circuits in the mollusk Clione. (Left.) The swim central pattern generator (swim CPG) in Clione is active continuously, and drives swim motor neurons (SAd) and wing muscles, inducing a swim. Elements of the dedicated swim circuit are shaded. (Right.) A touch to the wing activates mechanoreceptors (S), which activate retraction intemeurons (RI) that turn on retraction motor neurons (RM), causing the wing to retract. The RI turn off the swim circuit by inhibiting cells in the swim CPG and by inhibiting 5M. In addition, RA4 polysynaptically inhibits SM (indicated by the gray line). Elements of the dedicated retraction circuit are shaded. Diagram modified from Ref. 6. (B) Reorganizing circuit in the lobster stomatogastric nervous system. (Left.) When the cardiac sac network is inactive (indicated by the gray outlines), the ventricular dilator (VD) is a member of the pyloric network. (Right.) When the cardiac sac network is active, VD joins the cardiac sac network. It is strongly excited by the inferior ventricular (IV) neurons of the cardiac sac network. Furthermore, the cell loses the ability to generate plateau potentials, and so becomes less responsive to synaptic outputs from the pyloric network. Abbreviations: IC, inferior cardiac; PY, pyloric; PD, pyloric dilator; AB, anterior burster; LP, lateral pyloric; CD1 and CD2, cardiac sac dilator I and 2. Diagram modified from Ref. 7. (C) Distributed circuit in the leech, Input and output connections are shown for two dorsal local bending intemeurons (125 and 115). The contralateral (contra) 125 or 115 is also indicated, which has a symmetrical set of connections to the motor and sensory neurons (connections not shown). Other local bending intemeurons, which receive inputs from these sensory neurons and provide outputs to these motor neurons, are symbolically indicated by dots. Neuron 125 receives sensory input from pressure (P) cells on both the dorsal and ventral sides of the body (PD and PV, respectively), both from the side ipsilateral (ipsi) and contralateral to the soma of the interneuron. Neuron 115 receives similarly broad sensory inputs from both ipsilateral P cells and one contralateral P cell. In addition, the interneurons have outputs to both excitatory (E) and inhibitory (I) motor neurons on the dorsal (D) and ventral (V) sides of the body, both ipsilateral and contralateral to their somata. Since sensory inputs on one side of the animal will activate both ipsilateral and contralateral interneurons 125 and 115, interneuronal inputs to these motor neurons would lead to 'inappropriate' excitation or inhibition of motor neurons. For example, dorsal touch (activating a PD neuron) will cause input to motor neurons from 125 that is appropriate to induce a dorsal bend (for example, activation of an ipsilateral dorsal excitor motor neuron), as well as activation of motor neurons that are inappropriate for inducing a dorsal bend (for example, the dorsal inhibitory motor neurons will receive inhibition from the ipsilatera1125, but excitation from the contralateral 125). Other interneurons in this system show similar broad sensory inputs and apparently inappropriate motor outputs. Diagram modified from Ref. 8.
cells, and that these Mauthner-like cells contribute to the initiation of rapid escape responses 23. Do these cells form a distributed population of neurons that trigger escape? The criteria of Table I suggest that it is important to determine whether most of these neurons are active in generating different responses, and whether they activate a number of stereotyped outputs or a continuous range of outputs. Recent studies of the kinematics of escape in goldfish suggest that a wide range of escape responses is observed, and that this is due to changes in the magnitude and in the timing of activation of trunk musculature ipsilateral and contralateral to the stimulus inducing the escape response ~4. A model of this phenomenon involving differential activation of the Mauthner and Mauthnerlike neurons has also been proposed. Thus, this might be an example of a distributed population that is dedicated to escape behavior. That is, during an escape response, only this population of nerve cells is active; but computations within this population might occur in a distributed manner. Other vertebrate circuits might reorganize. For example, in frog embryos, brief mechanical stimuli to the skin can induce swimming, whereas longer and more intense mechanical stimuli can induce struggling~5. 'Fictive' versions of these behaviors can be induced in a reduced preparation using the same two mechanical stimuli, or by using either brief or sustained electrical activation of the skin which, in turn, activates the Rohon-Beard sensory neurons 26. Intracellular recordings from motor and premotor neurons indicate that the same classes of neurons mediate both behaviors zT, and that the pattern of synaptic drive during both behaviors is similar. Two lines of evidence suggest that this circuit reorganizes. Recordings from motor and premotor neurons indicate that up to 30% more cells are TINS, Vol. 17, No. 10, 1994
involved in struggling; that is, cells are added to the circuit. In addition, during struggling, depolarization is more intense, and the cycle duration is longer. These changes suggest that the increased sensory input could cause changes in the synaptic or intrinsic properties of the neurons in the circuit27. Experiments to determine whether the neurons participating in struggling are distinct from those that induce swimming, and whether the intrinsic membrane properties or synaptic connectivity change when the same neurons are activated in swimming or struggling, could elucidate whether the circuit does, in fact, reorganize. Another vertebrate circuit that appears to undergo reorganization is that which controls scratching in the turtle. The isolated spinal cord is capable of generating the three forms of the scratch reflex observed in the intact animal: rostral scratch, pocket scratch and caudal scratch 2s. In all three behavioral responses, hip protraction (flexion) alternates with hip retraction (extension). However, the timing of knee extension varies in the different responses, and can be used to distinguish one from the other. Recordings from spinal motoneurons indicate that synaptic inputs to motor neurons responsible for hip protraction and retraction are similar in all three types of scratch but that synaptic inputs change for the motor neurons responsible for knee extension 29. Thus, it is possible that the circuit reorganizes. Because a large number of interueurons are activated in response to cutaneous stimulation that can induce a scratch response 3°, it will be important to characterize the extent to which the changes in motor output are a result of distributed activity in this interneuronal population, or whether there are some interneurons that are shared among all the responses, in addition to some that are uniquely activated for each type of scratch. 417
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Fig. 3. (Left.) Experimental evidence for different circuit architectures. This figure shows some of the neurophysiological data on which the circuit diagrams in Fig. 2 are based. (A) Effect of dedicated retraction circuit on swim circuit in Clione. The data show the inhibitory effects of retraction intemeurons on swim interneurons. 1. At the arrow, a swimming wing is touched, inducing a powerful burst in the retraction interneuron (bottom trace), which strongly inhibits a swim intemeuron during a swim (top trace). 2. Depolarizing current injected into a retraction intemeuron (bottom trace) strongly inhibits the firing of a swim interneuron (top trace). Data taken, with permission, from Ref. 6. (B) Experimental evidence for reorganization in the stomatogastric nervous system ofPanulirus vulgaris. Top three traces: activity of the ventricular dilator (VD) neuron (top trace) during rhythmic activity in the pyloric network [whose activity is monitored in the pyloric dilator (PD) neuron, second trace]. The cardiac sac network [whose activity is monitored in the cardiac sac dilator 2 (CD2) neuron, third trace] is quiescent. Bottom three traces: when the cardiac set network is active (third trace), the VD neuron fires with that network (top trace), not the pyloric network (second trace). Data taken, with permission, from Ref. 7. (C) Evidence for distributed interneuronal connections in the leech bending circuit. The top two traces show synaptic inputs to motor neurons [the ipsilateral dorsal excitors, DE (i)] in response to intraceflular activation of the dorsal pressure neuron (PD). The middle trace shows the synaptic input to the motor neuron as the same sensory neuron is activated while intemeuron 125 is hyperpolarized. The bottom trace shows the difference between the top and middle traces. Hyperpolarizing the intemeuron does not block the response but does slightly reduce it, suggesting that the synaptic inputs to the motor neurons are the result of the activity of many local bending intemeurons. Similar results were obtained in response to hyperpolarizing other local bending interneurons. Data taken, with permission, from Ref. 8.
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Evidence for distributed circuitry is found in primate nervous systems. For example, Schwartz and his colleagues 31 have shown, using sequential recordings, that many neurons in the motor cortex of the rhesus monkey have broadly tuned directional responses. Each neuron is maximally activated during arm movements in its preferred direction, and these 418
preferred directions are uniformly distributed throughout three-dimensional space. Georgopolous and colleagues 32 have proposed that the direction of arm reaching is computed by this population, with each neuron contributing a small amount to the resulting arm movement. A three-dimensional population vector calculated from the responses of these directionaLly tuned neurons could accurately predict the direction and trajectory of ann movements before the movements begin. Others have shown that motor cortical cells, projecting through the corticospinal tract, are likely to activate the appropriate motor neuronal pools in the spinal cord (see discussion in Ref. 31). This suggests that the activity of a population of motor cortical cells could activate weighted combinations of appropriate muscle groups in order to generate the correct movement. Georgopoulos and colleagues 33 have gone on to show that this population computation is more closely related to the direction of the force than to the movement itself. It is interesting to note that Sparks and colleagues 34 have presented data showing that a distributed computation can also occur in the superior colliculus, for generating appropriate saccadic eye movements. However, the computation might be because of a weighted average of the vector contributions of each active neuron, rather than a vector sum of all of the neuron activities34. TINS, Vol. 17, No. 10, 1994
Concluding r e m a r k s If it was possible to identify and classify distinct neural architectures, it could have great value for simplifying the experimental analysis of neural circuitry. A parallel can be drawn to the analysis of the biophysics of nerve cells. Because many conductances have been identified, and their effects on the current and frequency properties of nerve cells have been described, it is possible to rapidly assess these properties of a new neuron, and then to focus the analysis based on the conductances that are most likely to be present in that cell. Of course, one cannot be sure which conductances are actually present without careful voltage-clamp and pharmacological study. Similarly, were there well-defined classes of neural architecture, they could guide the analysis of a novel circuit whose input-output characteristics have been described. How distinct are these different architectural categories? This review suggests that there are examples of each kind in relatively 'pure' form but that these architectures can coexist with one another. In the first example of dedicated circuitry (Clione swimming and wing withdrawal), it is clear that part of a dedicated circuit (that is, for swimming) might also be capable of reorganizing (from slow to fast swimming). Similarly, the example of distributed circuitry (leech bending) suggests that the interneurons involved in a distributed circuit might also reorganize to contribute to different behaviors, such as swimming or shortening. Even within a given behavior, it is unlikely that all neurons in a distributed circuit are active in all responses (for example, leech interneuron 115 does not receive input from the contralateral ventral pressure neuron, and so will not be activated by all touches to the body wallS). Finally, there might be circuits that are only partially distributed. For example, a circuit could produce distributed motor output but receive very localized sensory input, or receive distributed inputs but produce localized motor outputs. There may also be 'gray' areas where the definitions might overlap. In the second example of dedicated circuitry (locust walking and flying), it is clear that each interneuronal circuit might not only share motor neurons but may also share local interneurons that act to group motor neurons into specific patterns of activity. It could be argued that this is a form of reorganization, and that interneurons for walking join the circuit during walking, whereas interneurons for flying join the circuit during flying. Given these overlaps in the definitions of the different architectures, one possibility is that they reflect the stage of the analysis of a circuit, rather than genuinely distinct categories. Thus, a circuit might appear to be dedicated when it is studied initially, but as more elements are analyzed, it might clearly have distributed aspects. Does this render these different categories useless for the analysis of novel neural circuits? We think not. In the analysis of the leech bending circuit, Lockery and K r i s t a n 2 first established that the input-output relationships for the circuit as a whole could be a result of the activation of dedicated interneurons, or of a distributed circuit, and this suggested specific, testable hypotheses about the characteristics of the interneurons. Similarly, the hypothesis that neurons of the superior colliculus TINS, Vol. 17, No. 10, 1994
compute eye movements through a distributed population computation generated testable hypotheses for the effects of selectively inactivating different parts of this population 34. Thus, even if no 'pure' examples of the different architectures exist, the conceptual framework that we have discussed can greatly aid in the design of experiments to probe the nature of the circuitry. A critical analysis of these different examples has clarified several general aspects of neural architectures. First, it suggests that reorganization is a fundamental property of many different neuronal architectures, and is likely to be very widespread. Second, it provides additional criteria for distinguishing a dedicated circuit from a distributed circuit. Not only must the specificity of inputs or outputs to the system be examined, but the control that different circuits have over the periphery during different behaviors must be determined. Finally, it suggests the likelihood that many biological neural networks possess features of more than one of these different architectures, simultaneously showing characteristics of dedicated, distributed, or reorganizing circuitry. Our discussion of the analysis of vertebrate circuits also suggests that many of these issues cannot be resolved without a detailed cellular analysis of the circuit, but that such analyses can be guided by experiments suggested by these different architectures. Developing and improving techniques to monitor and manipulate the activity of many neurons simultaneously might be essential for testing these hypotheses, for example, the roles of a neuronal population in a distributed circuit. Selected references 1 Pearson, K. G. (1993) Annu. Rev. Neurosci. 16, 265-297 2 Lockery, S. R. and Kristan, W. B., Jr (1990) J. Neurosci. 10, 1811-1815 3 Getting, P. A. and Dekin, M. S. (1985) in Model Neural Networks and Behavior (SeIverston, A. I., ed.), pp. 3-20, Plenum Press 4 Weimann, J. M., Meyrand, P. and Marder, E. (1991) J. Neurophysiol. 65, 111-122 5 Arshavsky, Y. I., Orlovsky, G. N., Panchin, Y. V., Roberts, A. and Soffe, S. R. (1993) Trends Neurosci. 16, 227-233 6 Huang, Z. and Satterlie, R. A. (1990) J. Comp. Physiol. A 166, 875-887 7 Hooper, S. L. and Moulins, M. (1989) Science 244, 1587-1589 8 Lockery, S. R. and Kristan, W. B., Jr (1990) J. Neuroscl. 10, 1816-1829 9 Ramirez, J. M. and Pearson, K. G. (1988) J. NeurobioL 19, 257-282 10 Kr~mer, K. and Markl, H. (1978) J. Insect Physiol. 24, 577-586 11 Burrows, M. (1989) J. Exp. Biol. 146, 209-227 12 Harris-Warrick, R. M., Marder, E., Selverston, A. I. and Moulins, M., eds (1992) Dynamic Biological Networks: The Stomatogastric Nervous System, MIT Press 13 Dickinson, P. S. and Moulins, M. (1992) in Dynamic
Biological Networks: The Stomatogastric Nervous System 14 15 16 17 18 19
(Harris-Warrick, R. M., Marder, E., Selverston, A. I. and Moulins, M., eds), pp. 139-160, MIT Press Dickinson, P. S. and Marder, E. (1989) J. Neurophysiol. 61, 833-844 Hooper, S. L. and Moulins, M. (1990) J. Neurophysiol. 64, 1574-1589 Hooper, S. L., Moulins, M. and Nonnotte, L. (1990) J. NeurophysioL 64, 1555-1573 Katz, P. S. and Harris-Warrick, R. M. (1991)J. NeurophysioL 65, 1442-1451 Dickinson, P. S., Mecsas, C. and Marder, E. (1990) Nature 344, 155-158 Meyrand, P., Simmers, J. and Moulins, M. (1991) Nature
Acknowledgements WethankRoy Ritzmann,Randall Beer, ThomasDick and threeanonymous reviewersfor their commentson an earlierdraftof this review. Theauthors acknowledgesupport by NSFgrants IBN-8810757and IBN-9309691, by NIH programprojectgrant HL-25830-11A i, and by NIH traininggrant 5 T32 5M07250 dufing the preparationof this review. 419
351, 60-63 20 Lockery, S. R., Fang, Y. and Sejnowski, T. J. (1990) Neural Comput. 2, 274-282 21 Friesen, W. O. (1989) J. Comp. PhysioL A 166, 205-215 22 Wittenberg, G. and Kristan, W. B., Jr (1992) J. Neurophysiol. 68, 1693-1707 23 Eaton, R. C. and Hackett, J. T. (1984) in NeuralNlechanisms of Startle Behavior(Eaton, R. C., ed.), pp. 213-266, Plenum Press 24 Foreman, M. 13. and Eaton, R. C. (1993) J. Neurosci. 13, 4101-4113 25 Kahn, J. A. and Roberts, A. (1982) J. Exp. Biol. 99, 197-205 26 Soffe, S. R. (1991) Proc. R. Soc. Lond., Ser. B 197-203 27 Soffe, S. R. (1993) J. Neurosci. 13, 4456-4469
28 Mortin, L. I., Keifer, J. and Stein, P. S. G. (1985) J. Neurophysiol. 53, 1501-1516 29 Robertson, G. A. and Stein, P. S. G. (1988) J. Physiol. 404, 101-128 30 Berkowitz, A. and Stein, P. S. G. (1991) Soc. Neurosci. Abstr. 17, 123 31 Schwartz, A. B., Kettner, R. E. and Georgopoulos, A. P. (1988) .I. Neurosci. 8, 2913-2927 32 Georgopoulos, A. P., Kettner, R. E. and Schwartz, A. B. (1988) J. Neurosci. 8, 2928-2937 33 Georgopoulos, A. P., Ashe, J., Smyrnis, N. and Taira, M. (1992) Science 256, 1692-1695 34 Lee, C., Rohrer, W. H. and Sparks, D. L. (1988) Nature 332, 357-360
Signalling via A TPin the nervoussystem Herbert Zimmermann HerbertZimmermann is at the Biozentrum derJ. W. GoetheUniversit~t, AK Neurochemie, MarieCurie-Str. 9, D-60439 Frankfurtam Main, Germany.
Strong evidence has been provided that A T P can act as a transmitter not only in smooth muscle but also in peripheral ganglia and in brain. The cloning and molecular identification of two putative A TP receptors supports the previously established pharmacological receptor classifications. This review places into perspective the evidence for A T P as a neural signalling substance by examining sites of storage, release and hydrolysis, as well as potential actions and targets. The action of A T P is related to that of the nucleoside adenosine, and the potential of additional nucleotides to function as neural messenger is examined briefly.
released, it is clear that nucleotides are stored in the millimolar range. Concentrations are highest for ATP but the vesicular contents of other nucleotides, such as GTP, UTP (so far demonstrated only for chromaffin granules), ADP and the diadenosine polyphosphates AP4A and A P ~ (Box 1), are still in the millimolar range and might also be of functional importance. In view of the ligand specificities of some of the P2 purinoceptors (see below), a detailed search for additional vesicular nucleotides might be rewarding. It appears probable that neurones other than cholinergic and adrenergic neurones store ATP. Pure cholinergic synaptic vesicles from brain have a molecular ratio of ACh to ATP of 7:1, but the total of electron-lucent synaptic vesicles isolated from cerebral cortex has a molecular ratio of 1:1 (Ref. 6). As in cortex, the contribution of cholinergic nerve terminals is presumably no more than 10% and it can be concluded that ATP is stored to a considerable amount also in non-cholinergic electron-lucent vesicles. The vesicular transporter for ATP has not yet been identified in molecular terms 7. It is assumed to provide specificity for purinergic function similar to the vesicular glutamate or glycine transporters that enable packaging of the ubiquitous amino acids for regulated release in particular nerve cellss.
This brief overview of ATP's functions in neural tissues does not permit in-depth referencing of individual fields but rather provides a general view of the various aspects involved. It includes vesicular storage and release from neurones, as well as release from activated target cells. In nervous tissue, ATP can act via a variety of receptors, some of which are ligand-gated ion channels, whereas others are coupled to trimeric G proteins. ATP is hydrolyzed extracellularly to adenosine which itself is a potent signalling substance. In the periphery, targets of either ATP or its final hydrolysis product adenosine include neurones, muscle and endothelial cells and, in brain, include neurones, glia and blood vessels. There is evidence that extraceUular ATP can serve as a co-substrate for surface-located protein kinases. ATP is r e l e a s e d from n e r v e t e r m i n a l s Additional nucleotides might act as neural signalling There is much evidence for a CaZ÷-dependent substances. These include ADP, UTP and the di- neurogenic release of ATP. This has been demonadenosine polyphosphates. strated directly for synaptosomes isolated from the electric organ of the electric ray and mammalian brain, V e s i c l e s c a n s t o r e A T P and o t h e r n u c l e o t i d e s and for peripheral tissues including the taenia coli, The recently established evidence for ATP and bladder, vas deferens and the rat neuromuscular other nucleotides as neural messengers calls for a junction9'1°. Molecular ratios of released ACh and close examination of nucleotide contents of vesicles. ATP close to those of synaptic-vesicle storage have Co-storage of ACh and ATP in vesicles was demon- been observed for synaptosomes from the electric strated for a variety of peripheral and central synaptic organ of the electric ray, and purely cholinergic vesicles (Table I). Similarly, noradrenaline and ATP synaptosomes from rat caudate nucleus 1. Synaptoare co-stored in vesicles of sympathetic nerve ter- somal ATP release induced by K ÷ was suggested to minals, and in the related granules from chromaffin be quantal in nature n. Less-congruent results were cells. In either case, vesicular nucleotides are out- obtained in a number of studies using less-pure numbered by their co-transmitter. While there is fractions of brain synaptosomes. Furthermore, some uncertainty concerning the exact number of certain neurotoxins appear to dissociate release of the transmitter substances inside vesicles or in quanta co-transmitters by selectively blocking release of 420
© 1994, ElsevierScience Ltd
TINS, VoL 17, No. 10, 1994