Encyclopedia of Computational Neuroscience DOI 10.1007/978-1-4614-7320-6_465-3 # Springer Science+Business Media New York 2014
Sensory Input to Central Pattern Generators Wolfgang Stein* School of Biological Sciences, Illinois State University, Normal, IL, USA
Synonyms Afferent feedback to neural oscillators; Afferent input to rhythm generating networks; Proprioceptive feedback to central pattern generators; Sensory drive to rhythmic neuronal networks; Sensory modulation of central pattern generators
Definition A central pattern generator (CPG) is an assembly of neurons (neuronal network) that produces rhythmic activity without requiring phasic input signals and often drives the motor system and rhythmic muscle movements. Sensory feedback to a CPG circuit is the return signal from the sensory system in response to this rhythmic muscle movement, which conveys a continuous measurement of the output behavior to the CPG.
Detailed Description Individual neural networks, such as CPGs, can generate rhythmic output patterns even in the absence of any phasic input. They drive vital behaviors such as breathing, swallowing, and chewing, as well as locomotion and saccadic eye movements, and often continue to function even in isolated nervous system (i.e., in vitro) preparations (Marder and Calabrese 1996; Grillner et al. 2005; Dickinson 2006; Gordon and Whelan 2006; Isa and Sparks 2006; Kiehn 2006; Katz and Hooper 2007; Chevallier et al. 2008; Doi and Ramirez 2008; Berkowitz et al. 2010; El Manira et al. 2010; B€ uschges et al. 2011; Harris-Warrick 2011; Marder 2012). Given the improved accessibility for neuronal manipulations in vitro, it is not surprising that most insights into the rhythm generating mechanisms of CPGs have been derived from this condition, hence using preparations deprived of sensory information. In vivo, however, numerous additional influences have access to the CPG circuit from other CNS regions, circulating hormones, and sensory systems (Fig. 1). In particular for driving motor behaviors, CPGs receive constant feedback from peripheral sense organs, including proprioceptors. While CPGs may also contribute to cognitive functions (e.g., theta and gamma rhythms in the hippocampus; Grillner et al. 2005), this entry focuses on CPGs underlying motor pattern generation because the influence of sensory feedback is particularly obvious in these CPGs.
*Email:
[email protected] Page 1 of 11
Encyclopedia of Computational Neuroscience DOI 10.1007/978-1-4614-7320-6_465-3 # Springer Science+Business Media New York 2014
Fig. 1 Schematic overview of sensory input to CPG circuits. For simplicity, the motor neurons are not shown separately. The CPG receives input from proprioceptive and exteroceptive pathways. This input either directly affects the CPG (local feedback) and / or is mediated via upstream control neurons (long-loop feedback). Local feedback mainly determines the magnitude and timing of CPG activity, while long-loop feedback has modulatory effects, regulates intensity and coordinates CPG activity with other motor acts. CPG activity itself can modulate incoming sensory feedback, for example via presynaptic inhibition of the afferent pathways
Sensory Feedback Versus Sensory Input Sensory feedback differs from sensory input in that it provides the CPG with a continuous readout of the consequences of CPG activity on the resulting behavior. Typically, the sense organs providing sensory feedback to CPGs are proprioceptive, i.e., they continuously measure the response of the muscles to the rhythmic motor output and thus show phasic activity patterns. Exteroceptive sense organs, in contrast, typically provide sensory input, rather than feedback, about changes in external conditions. They are not necessarily related to CPG activity, although in some cases they can be. To demonstrate this difference, imagine eating an almond chocolate truffle. You take a first bite from the soft chocolate layer of the truffle, and your periodontal mechanoreceptors (peripheral receptors that signal information about tooth load, Bonte et al. 1993) will initially encounter little resistance and will thus either be inactive or continuously active at a low level and hence provide sensory input. As you encounter the almond with your second bite, this changes dramatically and the periodontal mechanoreceptors now register a strong mechanical load, and they do so whenever your teeth meet the almond, in phase with the chewing pattern, providing a signal that chewing strength needs to be increased. In other words, they now provide sensory feedback that is directly related to the motor output. In contrast, typical proprioceptors, such as muscle spindles that mainly measure the stretch of muscles, provide phasic feedback at all times, even in the unlikely event that the manufacturer of the chocolate truffle forgot to put in the almond.
Page 2 of 11
Encyclopedia of Computational Neuroscience DOI 10.1007/978-1-4614-7320-6_465-3 # Springer Science+Business Media New York 2014
Sensory input, due to its (partial) independence of CPG activity, often initiates or modulates the intensity of the motor pattern on a longer time scale, adapts CPG activity to external conditions, and coordinates it with other motor acts to maintain equilibrium during behavior (Grillner 2003; Dickinson 2006; Stein 2009; Blitz and Nusbaum 2011; Harris-Warrick 2011; Marder 2012). Many actions of sensory input pathways are mediated via intercalated interneurons that process many different sensory modalities and often exert modulatory influences on the CPG circuits.
Local Sensory Feedback The role of local sensory feedback on CPG activity has been particularly well studied. CPGs are often located close to the musculoskeletal system they control, and they receive local feedback from that system (via proprioceptive pathways, or, in some cases, directly from dendrites of CPG motor neurons, Garcia-Crescioni et al. 2010). Local sensory feedback typically modifies and adapts CPG activity on a short time scale (Pearson 2004) by altering magnitude and relative timing of muscle activity. Conceptually, local sensory feedback to a CPG serves at least three main functions: 1. Corrective input: It can provide a corrective signal to adapt CPG patterns to deal with environmental perturbations. Corrective feedback is necessary in an unpredictable environment, for instance, to reinforce CPG activities that drive fin or body movements during swimming in rough water or to adjust wing beat in a turbulent air stream. 2. Timing: It can contribute to CPG activity in an ongoing fashion, during unperturbed motor pattern production, by providing timing cues about the biomechanical state of the moving body part(s). During walking in cats and insects, for example, phase transitions are facilitated by sensory feedback that depends on the biomechanical state of the leg (B€ uschges 2005). This sensory feedback ensures that a certain phase of the movement (e.g., the swing phase) is not initiated until the appropriate biomechanical state of the leg (the posterior extreme position) has been achieved. It provides information about limb and appendage position in space and thus allows for adapting and planning of movements. 3. Stability: By ensuring that the timing and force output of a motor pattern is appropriate for the task at hand, local sensory feedback can also lead to a stabilization of the motor pattern. The tegula, a wing proprioceptor in locusts, for example, is excited during wing depression and hence provides feedback about the position of the wing and facilitates phase transitions (Wolf 1993). In addition, however, the tegula acts to stabilize the flight rhythm: If its nerve is stimulated with a relatively weak shock, activating only a few of the tegula’s sensory neurons, the flight rhythm speeds up (Ausborn et al. 2007). If it is stimulated strongly, the pattern slows. This example nicely demonstrates that proprioceptors can have a pivotal influence on the pattern-generating machinery in vivo: They work together with the CPG to ensure that the behavioral output is continuously adjusted to maintain functioning and stability of the desired pattern. Due to these essential functions of local sensory feedback, in most systems behaviorally appropriate motor pattern generation requires both CPG activity and sensory feedback, with sensory and central mechanisms being integrated to produce a robust rhythmic activity pattern that can be rapidly adjusted to cope with unpredictable environmental disturbances.
Page 3 of 11
Encyclopedia of Computational Neuroscience DOI 10.1007/978-1-4614-7320-6_465-3 # Springer Science+Business Media New York 2014
Sensory Gating and Phase-Dependent Modulation of Sensory Feedback As is likely true for all aspects of neural signaling, the effect of sensory feedback on CPG activity is modified by many influences. For example, it can vary with the phase of the motor pattern at which it is given. For example, during stick insect walking, the influence of the campaniform sensilla, which measure load of the leg, always supports phase transitions from swing to stance phase (Akay et al. 2007). Functionally, this makes sense: When load is high, this means that the leg must be on the ground and stance phase should commence. Interestingly, the effect of the load receptors on the protractor and retractor muscles that move the leg forward and backward changes in sign with a switch from forward to backward walking. During forward walking, a transition from protractors to retractors is facilitated, while during backward walking, transitions from retractor to protractor activity are elicited. Another example, also from the walking system, is the phase dependence of local sensory feedback. A perturbation that occurs during the swing phase of the leg (e.g., when the foot hits a step when climbing up a stairway) usually results in the leg being lifted to enable it to reach the next step. During the stance phase, the same stimulus causes a further descending of the leg to increase force between foot and ground, which enhances stability. Such reflex reversals have been observed in many motor systems (Skorupski and Sillar 1986; Ba¨ssler 1993; Burrows 1996), but the neuronal basis for this switch in sign is not well understood. There are two principle possibilities for how such a reflex reversal can be achieved (Hooper 2000): (1) a differential (phase-dependent) routing of sensory feedback to different CPG neurons and (2) a varying response by the CPG network to sensory feedback due to how the network integrates the input during different phases of the pattern. In locusts, for example, the sensory feedback from the femoral chordotonal organ, which measures the position of the femur-tibia joint during walking, is gated with the phase of the walking motor pattern, disabling feedback during certain phases of this pattern (Wolf and Burrows 1995). The underlying mechanism is a phasedependent presynaptic inhibition of the sensory terminals and a concomitant decrease in transmitter output. While sensory activity remains unchanged, sensory transmission to the CNS is impaired. Since presynaptic inhibition is typically strongest whenever sensory activity is high, it reduces the overall amount of sensory feedback, and, quite peculiarly, it also diminishes expected sensory feedback at certain phases of the CPG pattern. Presynaptic inhibition could thus be one mechanism that underlies the reafference principle, which tries to explain the fact that self-initiated motions do not interfere with the perception of constancy [proposed by von Holst and Mittelstaedt (1950)]. One way to achieve this goal is via an efference copy of the motor signal (in this case the CPG output) which provides the input to a forward internal model. This model is then used to generate the predicted sensory feedback that estimates the sensory consequences of the motor command. This signal is sent to the periphery, where it (presynaptically) inhibits any proprioceptor response to the CPG-generated movement which could interfere with the execution of the motor task. Differences in the actual from the predicted sensory activity are then sent back to the CPG as an error signal to adapt the motor output. While the pathways that elicit the presynaptic inhibition are still somewhat nebulous, it has been demonstrated that proprioceptors can presynaptically inhibit the terminals of neurons from other proprioceptive sense organs (Stein and Schmitz 1999). Since proprioceptors typically show phasic activity, this could contribute to the gating of sensory feedback at specific phases of the motor pattern. While CPG activity can affect transmitter release from sensory neurons (Wolf and Burrows 1995), so far there are no indications that CPGs can directly modulate sensory spike activity. However, CPGs may indirectly affect sensory spike activity via feedback onto modulatory neurons Page 4 of 11
Encyclopedia of Computational Neuroscience DOI 10.1007/978-1-4614-7320-6_465-3 # Springer Science+Business Media New York 2014
that not only control CPG activity but also release neuromodulatory substances in a paracrine fashion. Neuromodulators, in turn, affect both muscle contraction and sensory activity, which makes it possible for CPGs to interfere with sensory spike activity, at least in principle. The activity of the anterior gastric receptor (AGR) in the stomatogastric nervous system of crustaceans, for example, can switch from spiking to bursting, depending on the modulatory conditions present (Birmingham et al. 1999). While in spiking mode, AGR encodes both rapid and slow stimuli and thus reports cycle-by-cycle muscle movements that are driven by the CPG. In bursting mode, however, only persistent stimuli are detected, and thus, average levels of muscle tension are reported. This example demonstrates that the same proprioceptor can provide feedback about different qualities of the CPG output.
Context or Task-Dependent Modulation of Sensory Feedback While phase-dependent modulation affects sensory feedback on a cycle-by-cycle basis, sensory feedback can also be subject to long-term modulation, either via the actions of neuromodulators released from neurons (paracrine, as stated in the previous paragraph) or the endocrine system (hormones) that modify the sensory response itself (Birmingham 2001) or via a modulation of the response of the CPG network to sensory input. In the stomatogastric nervous system of crustaceans, for example, changes of a few Hertz in the tonic activity of the AGR muscle proprioceptor modify the response of the gastric mill (chewing) CPG network to sensory input from mechanoreceptors in the stomach (Daur et al. 2009). Magnitude and timing of the gastric mill motor pattern elicited by the mechanoreceptive input correlate with the tonic spike activity of the muscle receptor, which means that the state of the system and its response to incoming sensory input is modified.
What Drives Rhythmic Behavior In Vivo: Sensory Feedback or CPG Activity? Discussion regarding whether the CPG or the sensory feedback drives the actual behavior of an animal in vivo is as old as the discovery of CPG circuits. There is no universal answer, because their relative impact appears to differ from system to system. B€ uschges and El Manira (2011) draw the conclusion that, although clearly affected by sensory feedback, CPGs that drive locomotor movements in homogeneous media such as air (flying) or water (swimming) often continue to be rhythmically active even when isolated from sensory feedback. In contrast, CPGs that drive locomotion in heterogeneous environments, such as terrestrial locomotion, typically lack rhythmic activity in isolation from local sensory feedback. This distinction, however, does not necessarily mean that sensory feedback does not shape the rhythmic movement. In locust flight, for example, the CPG can be activated in isolation and will show a stable flight pattern, but in vivo several sense organs, including the tegula, provide feedback to the CPG and dominate the motor pattern. The frequency of the rhythm, however, increases by a factor of two, which is why in this system sensory feedback is seen as a major contributor to the functional motor pattern (Ausborn et al. 2007). While in this example there would be no flight pattern without CPG activity, theoretical approaches have proposed that sensory feedback itself could act as an oscillator if provided with the adequate properties, such as delay lines and gain (Cruse 2002). For example, a network with negative sensory feedback will produce stable oscillations if the open-loop gain is equal or larger than 1 at a frequency where the phase shift is 180 (Nyquist criterion). It should be noted though that for nonlinear systems such as neural networks, more complex stability criteria such as Lyapunov or the circle criterion might be necessary. Networks with positive feedback, on the other hand, need a high-pass filter or related mechanism that reduces the positive feedback to create stable oscillations (Ba¨ssler 1986). In both cases, the oscillations would be driven entirely by sensory feedback and might not even require a central oscillatory network. In such a case the Page 5 of 11
Encyclopedia of Computational Neuroscience DOI 10.1007/978-1-4614-7320-6_465-3 # Springer Science+Business Media New York 2014
system would be called a chain reflex (because a system that must be driven by sensory input is defined as a reflex). In fact, a chain reflex may not be distinguishable from a CPG in vivo (Ba¨ssler 1986). If we consider a relaxation oscillator as a pattern-generating circuit (e.g., a half-center circuit with reciprocal inhibition, as is found in many systems), the frequency of the oscillations would depend on the endogenous excitation of the loop and its internal characteristics (gain and time constant of the fatigue of the positive feedback). Sensory feedback could affect oscillation frequency either by affecting the intrinsic characteristics or by modifying the excitation state of the network. Even in the easiest situation of a simple addition of excitation to the oscillator neurons by the sensory feedback, the timing cues for switching between oscillation states would depend on the sensory feedback, and the CPG would only be responsible for managing the actual switch between phases. If the endogenous excitation in such a relaxation oscillator network would fall below the threshold for oscillations, the sensory feedback could substitute for the lack of endogenous excitation and elicit oscillations. Per definition, this would then be a chain reflex. Even if the CPG consists of a negative feedback loop (such as serial inhibition in a network with an odd number of neurons), sensory feedback would drive the frequency of the oscillation. In such a system, the CPG oscillation frequency depends on the amplitude and phase-frequency oscillations of the open-loop system. If sensory feedback is added to the system, the eigenfrequency stays the same, and the sensory input will be damped and superimposed on the inherent (CPG) oscillations (Cruse 2009). The resulting frequency will thus be a superposition of central and sensory oscillations. Sensory feedback, however, will entrain the central oscillations within a given range. Thus, the CPG represents a band-pass filter for the sensory oscillation. In the passband, the timing cues of the oscillations are determined by the sensory feedback. In summary, determining whether the CPG or sensory feedback dominates the behavior of the oscillatory system in vivo, although of pivotal importance for understanding the dynamics of motor systems, continues to challenge studies of CPG function.
Long-Loop Sensory Feedback and Long-Lasting Effects of Sensory Feedback While one role of sensory feedback is to allow the CPG to deal with environmental perturbations (corrective input), certain motor pattern characteristics such as phase relationships must be maintained to guarantee functional and behavioral homeostasis. Sensory changes from one part of the pattern, for example, might require compensatory changes in other parts. Thus, the effects of local sensory feedback might have widespread and possibly long-lasting effects. Sensory pathways also affect CPGs indirectly, by influencing the neurons that control the CPGs. In the locomotor systems of cats and lampreys, for example, supraspinal networks in the brainstem receive sensory input from many modalities, including proprioceptive feedback (Rossignol et al. 2006). While in general these upstream neurons are involved in pattern initiation, modulation, and selection, the uniquely identifiable reticulospinal Mauthner and M€ uller cells in lampreys display movement-related activities that appear to be elicited by ascending feedback from the CPGs (Antri et al. 2009; Buchanan 2011). This is similar to modulatory projection neurons that control the CPGs in the stomatogastric nervous system (Blitz and Nusbaum 2012). Here, ascending feedback from CPG neurons imposes a rhythmic activity pattern onto these modulatory neurons. In both cases, neurons outside of the CPG have access to the timing of the CPG, and they are affected by sensory input. In the stomatogastric nervous system, phasic sensory feedback affects the very set of projection neurons that shows CPG timing (Hedrich et al. 2009), which allows for a long-loop sensory control of CPG activity. In lobsters, for example, the phasing of CPG activity can reverse activity if the proprioceptive AGR neuron exceeds a certain threshold (Combes et al. 1999), due to
Page 6 of 11
Encyclopedia of Computational Neuroscience DOI 10.1007/978-1-4614-7320-6_465-3 # Springer Science+Business Media New York 2014
the bistable intrinsic properties of modulatory projection neurons that process sensory feedback from AGR.
Open-Loop Versus Closed-Loop Sensorimotor Interactions Even if sensory feedback only exerts short-acting effects, compensatory changes in the motor pattern may occur and induce global, long-lasting changes in the CPG pattern, because the activity changes of the neurons targeted by sensory feedback may induce changes in a chain of followers. Yet, relatively little is known about such holistic sensorimotor interactions. This is mostly due to the fact that while sensory feedback has been studied in many motor circuits, this was typically done in open-loop conditions, i.e., with emphasis on how sensory signals alter motor output (rather than interact with it), or on information flow toward that output. The dynamical components determined by the interaction of motor and sensory activities, however, can create emergent properties that govern the functional characteristics of the system. While already a standard for investigating movement or behavior in general (e.g., fly and bee flight: Dickinson 2005; Fry et al. 2008; Mronz and Lehmann 2008; Sareen et al. 2011; Srinivasan 2011; monkey motor control & vision: Nicolelis 2003), the idiosyncratic dynamics created by the sensorimotor interaction have only rarely been elucidated at the level of the nervous system. One reason for the discrepancy between closed-loop behavioral and open-loop nervous system studies is the unfortunate fact that sensory structures, and thus also sensory feedback, are typically not available in experiments performed in isolation from the body. The functional and circuit properties as well as the cellular characteristics of CPG networks, however, are only known in great detail because of their accessibility in isolated preparations. In contrast, in systems with wellcharacterized behavior and sensory structures, the underlying neural network is usually not well described. One way to circumvent this problem is to provide artificial sensory feedback that depends on the motor output, in real time, to the isolated CPG circuit. Thus far, in the few instances in which such closed-loop experiments have been performed (Ba¨ssler and Nothof 1994; Ausborn et al. 2007; Smarandache et al. 2008; Ausborn et al. 2009), it is clear that sensorimotor interactions pivotally shape the motor output. Given the fact that sensory feedback also affects the upstream neural structures that control the motor circuits, it seems reasonable to assume that sensory feedback in closed-loop conditions will have an impact on the motor pattern that goes beyond simple influences on timing and magnitude. Rather, emergent properties of sensorimotor interactions are to be expected.
Indirect and Long-Term Influences of Sensory Feedback While the immediate influences on timing and magnitude of sensory feedback are well studied, sensory feedback also has long-term influences on CPG activity: 1. Development of vital rhythmic activities. It is still unclear whether sensory feedback is needed during early nervous system development (Suster and Bate 2002). A lack of feedback, however, leads to dysplasia or coordination problems later in life. 2. Stability and robustness of the motor pattern during the life span of the animal. While motor circuits must be flexible in order to respond to changes in the environment or the body conditions, they must also generate stable patterns and be robust against perturbations. Emergent properties of sensorimotor interactions may well supply the basis for stability and robustness and may even help to select the adequate motor pattern, biasing the system toward specific patterns. In particular when environmental or neuromodulatory conditions change, robustness must be present. Failures of this control may be fatal (e.g., sudden infant death syndrome). Page 7 of 11
Encyclopedia of Computational Neuroscience DOI 10.1007/978-1-4614-7320-6_465-3 # Springer Science+Business Media New York 2014
3. Recovery from injury. Continuous CPG activity requires CNS input, and removal of this input causes a failure of the motor pattern. While there is some evidence that continuous entrainment of the motor patterns via sensory feedback supports recovery (e.g., after spinal cord injury, Rossignol and Frigon 2011; Mehrholz et al. 2012), it is far from clear what mechanisms drive recovery. Emergent properties during sensorimotor interactions might support recovery, in particular if neuromodulatory neurons (long-loop sensory influences) are involved. Sensory feedback could in this case replace the missing endogenous excitation of the CPG network. In summary, while CPGs generate rhythmic activity patterns that have similarities to the behavioral patterns, sensory feedback pivotally shapes the CPG output. While the short-term and direct actions of sensory feedback on CPG activity are well described, their long-term actions and sensorimotor interactions in closed-loop conditions are a focus of current studies.
Cross-References ▶ Central Pattern Generator ▶ Peripheral Feedback and Rhythm Generation
References Akay T, Ludwar B, Goritz ML, Schmitz J, Buschges A (2007) Segment specificity of load signal processing depends on walking direction in the stick insect leg muscle control system. J Neurosci 27:3285–3294 Antri M, Fenelon K, Dubuc R (2009) The contribution of synaptic inputs to sustained depolarizations in reticulospinal neurons. J Neurosci 29:1140–1151 Ausborn J, Stein W, Wolf H (2007) Frequency control of motor patterning by negative sensory feedback. J Neurosci 27:9319–9328 Ausborn J, Wolf H, Stein W (2009) The interaction of positive and negative sensory feedback loops in dynamic regulation of a motor pattern. J Comput Neurosci 27:245–257 ¨ Bassler U (1986) On the definition of central pattern generator and its sensory control. Biol Cybern 54:65–69 Ba¨ssler U (1993) The femur-tibia control system of stick insects–a model system for the study of the neural basis of joint control. Brain Res Brain Res Rev 18:207–226 Ba¨ssler U, Nothof U (1994) Gain control in a proprioceptive feedback loop as a prerequisite for working close to instability. J Comput Biol 175:23–33 Berkowitz A, Roberts A, Soffe SR (2010) Roles for multifunctional and specialized spinal interneurons during motor pattern generation in tadpoles, zebrafish larvae, and turtles. Front Behav Neurosci 4:36 Birmingham JT (2001) Increasing sensor flexibility through neuromodulation. Biol Bull 200:206–210 Birmingham JT, Szuts ZB, Abbott LF, Marder E (1999) Encoding of muscle movement on two time scales by a sensory neuron that switches between spiking and bursting modes. J Neurophysiol 82:2786–2797 Blitz DM, Nusbaum MP (2011) Neural circuit flexibility in a small sensorimotor system. Curr Opin Neurobiol 21:544–552 Page 8 of 11
Encyclopedia of Computational Neuroscience DOI 10.1007/978-1-4614-7320-6_465-3 # Springer Science+Business Media New York 2014
Blitz DM, Nusbaum MP (2012) Modulation of circuit feedback specifies motor circuit output. J Neurosci 32:9182–9193 Bonte B, Linden RW, Scott BJ, van Steenberghe D (1993) Role of periodontal mechanoreceptors in evoking reflexes in the jaw-closing muscles of the cat. J Physiol 465:581–594 Buchanan JT (2011) Spinal locomotor inputs to individually identified reticulospinal neurons in the lamprey. J Neurophysiol 106:2346–2357 Burrows M (1996) The neurobiology of an insect brain. Oxford University Press, New York B€ uschges A (2005) Sensory control and organization of neural networks mediating coordination of multisegmental organs for locomotion. J Neurophysiol 93:1127–1135 B€ uschges A, Scholz H, El Manira A (2011) New moves in motor control. Curr Biol 21:513–524 Chevallier S, Jan Ijspeert A, Ryczko D, Nagy F, Cabelguen JM (2008) Organisation of the spinal central pattern generators for locomotion in the salamander: biology and modelling. Brain Res Rev 57:147–161 Combes D, Meyrand P, Simmers J (1999) Dynamic restructuring of a rhythmic motor program by a single mechanoreceptor neuron in lobster. J Neurosci 19:3620–3628 Cruse H (2002) The functional sense of central oscillations in walking. Biol Cybern 86:271–280 Cruse H (2009) Neural networks as cybernetic systems, 3rd and revised edn. Brains, Minds & Media. http://www.brains-minds-media.org/archive/1990/bmm1841.pdf Daur N, Nadim F, Stein W (2009) Regulation of motor patterns by the central spike-initiation zone of a sensory neuron. Eur J Neurosci 30:808–822 Dickinson MH (2005) The initiation and control of rapid flight maneuvers in fruit flies. Integr Comp Biol 45:274 Dickinson PS (2006) Neuromodulation of central pattern generators in invertebrates and vertebrates. Curr Opin Neurobiol 16:604–614 Doi A, Ramirez JM (2008) Neuromodulation and the orchestration of the respiratory rhythm. Respir Physiol Neurobiol 164:96–104 El Manira A, Kyriakatos A, Nanou E (2010) Beyond connectivity of locomotor circuitry-ionic and modulatory mechanisms. Prog Brain Res 187:99–110 Fry SN, Rohrseitz N, Straw AD, Dickinson MH (2008) TrackFly: virtual reality for a behavioral system analysis in free-flying fruit flies. J Neurosci Methods 171:110–117 Garcia-Crescioni K, Fort TJ, Stern E, Brezina V, Miller MW (2010) Feedback from peripheral musculature to central pattern generator in the neurogenic heart of the crab Callinectes sapidus: role of mechanosensitive dendrites. J Neurophysiol 103:83–96 Gordon IT, Whelan PJ (2006) Deciphering the organization and modulation of spinal locomotor central pattern generators. J Exp Biol 209:2007–2014 Grillner S (2003) The motor infrastructure: from ion channels to neuronal networks. Nat Rev Neurosci 4:573–586 Grillner S, Markram H, De Schutter E, Silberberg G, LeBeau FE (2005) Microcircuits in action–from CPGs to neocortex. Trends Neurosci 28:525–533 Harris-Warrick RM (2011) Neuromodulation and flexibility in central pattern generator networks. Curr Opin Neurobiol 21:685–692 Hedrich UB, Smarandache CR, Stein W (2009) Differential activation of projection neurons by two sensory pathways contributes to motor pattern selection. J Neurophysiol 102:2866–2879 Hooper SL (2000) Central pattern generators. Curr Biol 10:R176
Page 9 of 11
Encyclopedia of Computational Neuroscience DOI 10.1007/978-1-4614-7320-6_465-3 # Springer Science+Business Media New York 2014
Isa T, Sparks DL (2006) Microcircuit of the superior colliculus. A neuronal machine that determines timing and endpoint of saccadic eye movements. In: Grillner S, Graybiel AM (eds) Microcircuits: the interface between neurons and global brain function. MIT Press, Cambridge, MA, pp 5–34 Katz PS, Hooper SL (2007) Invertebrate central pattern generators. Cold Spring Harb Monogr Ser 49:251 Kiehn O (2006) Locomotor circuits in the mammalian spinal cord. Annu Rev Neurosci 29:279–306 Marder E (2012) Neuromodulation of neuronal circuits: back to the future. Neuron 76:1–11 Marder E, Calabrese RL (1996) Principles of rhythmic motor pattern generation. Physiol Rev 76:687–717 Mehrholz J, Kugler J, Pohl M (2012) Locomotor training for walking after spinal cord injury. Cochrane Database Syst Rev 11, CD006676 Mronz M, Lehmann FO (2008) The free-flight response of Drosophila to motion of the visual environment. J Exp Biol 211:2026–2045 Nicolelis MA (2003) Brain-machine interfaces to restore motor function and probe neural circuits. Nat Rev Neurosci 4:417–422 Pearson KG (2004) Generating the walking gait: role of sensory feedback. Prog Brain Res 143:123–129 Rossignol S, Frigon A (2011) Recovery of locomotion after spinal cord injury: some facts and mechanisms. Annu Rev Neurosci 34:413–440 Rossignol S, Dubuc R, Gossard JP (2006) Dynamic sensorimotor interactions in locomotion. Physiol Rev 86:89–154 Sareen P, Wolf R, Heisenberg M (2011) Attracting the attention of a fly. Proc Natl Acad Sci USA 108:7230–7235 Skorupski P, Sillar KS (1986) Phase-dependent reversal of reflexes mediated by the thoracocoxal muscle receptor organ in the crayfish, Pacifastacus leniusculus. J Neurophysiol 55:689–695 Smarandache CR, Daur N, Hedrich UB, Stein W (2008) Regulation of motor pattern frequency by reversals in proprioceptive feedback. Eur J Neurosci 28:460–474 Srinivasan MV (2011) Honeybees as a model for the study of visually guided flight, navigation, and biologically inspired robotics. Physiol Rev 91:413–460 Stein W (2009) Modulation of stomatogastric rhythms. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 195:989–1009 Stein W, Schmitz J (1999) Multimodal convergence of presynaptic afferent inhibition in insect proprioceptors. J Neurophysiol 82:512–514 Suster ML, Bate M (2002) Embryonic assembly of a central pattern generator without sensory input. Nature 416:174–178 von Holst E, Mittelsta¨dt H (1950) The reafference principle. Interaction between the central nervous system and the periphery. In: Selected papers of Erich von Holst: the behavioural physiology of animals and man. Methuen, London, pp 39–73 Wolf H (1993) The locust tegula: significance for flight rhythm generation, wing movement control and aerodynamic force production. J Exp Biol 182:229–253 Wolf H, Burrows M (1995) Proprioceptive sensory neurons of a locust leg receive rhythmic presynaptic inhibition during walking. J Neurosci 15:5623–5636
Further Reading Scholarpedia articles on Central Pattern Generators and Sensory Feedback Motor coordination. http://www.scholarpedia.org/article/Motor_coordination Page 10 of 11
Encyclopedia of Computational Neuroscience DOI 10.1007/978-1-4614-7320-6_465-3 # Springer Science+Business Media New York 2014
Periodic Orbits and Dynamical Systems. http://www.scholarpedia.org/article/Periodic_orbit The Spinal Cord. http://www.scholarpedia.org/article/Spinal_cord The Stomatogastric Ganglion. http://www.scholarpedia.org/article/Stomatogastric_ganglion The Tritonia Swim Network. http://www.scholarpedia.org/article/Tritonia_swim_network Facebook pages on Central pattern generators https://www.facebook.com/pages/Central-pattern-generator/138633789494277?nr Wikipedia articles on Central Pattern Generators and Feedback Central Pattern Generation. http://en.wikipedia.org/wiki/Central_pattern_generator The Efference Copy. http://en.wikipedia.org/wiki/Efference_copy Feedback. http://en.wikipedia.org/wiki/Feedback Locomotion and the Spinal Cord. http://en.wikipedia.org/wiki/Spinal_Locomotion Neural Oscillations. http://en.wikipedia.org/wiki/Neural_oscillation The Scratch Reflex. http://en.wikipedia.org/wiki/Scratch_reflex
Page 11 of 11
