The Hooper Lab
Central pattern generation and motor expression
Neuroscience Program
Department of Biological Sciences
Ohio University
Listen to the lvn or the mvn
Rhythmic neuronal activity is widespread in nervous systems, and plays
a central role in certain types of sensory processing, in motor pattern
production, and possibly (in vertebrates) in attention. These rhythms are
generated endogenously (i.e., they can continue without rhythmic sensory
input) by central neural networks, and hence are an example of the nervous
system's ability to spontaneously create patterns. Network rhythmicity
has been extensively studied in several invertebrate preparations and in
the lamprey, and we now have a good general understanding of the mechanisms
underlying it. However, we under
underlying it. However, we understand relatively little about its dynamic
regulation (e.g., how rhythmic pattern frequency and phasing are controlled),
and, in the case of motor systems, how these neural networks interact with
their peripheral effectors (the musculoskeletal system) so as to continually
generate behaviorally relevant, functional motor outputs.
Our lab studies these issues in the pyloric neuromuscular
system of the lobster, Panulirus interruptus (above right), (Maynard and Dando,
1974; Selverston et al., 1976; Selverston and Moulins, 1987; Harris-Warrick
et al., 1992). The pyloric neural network produces an approximately 3
phase rhythmic neural output, and is composed of 14-16 neurons divided
into 7 neuronal types. We have shown that this network maintains phase (e.g., neuronal burst lengths and interneuronal
delays proportionally change with changes in period) when overall cycle
frequency is varied over a 3 to 5 fold range (Hooper, 1997a,b). In this
system burst lengths and interneuronal delays at least partially depend
on the intrinsic membrane properties of the neurons themselves (Russell
and Hartline, 1979), and thus phase maintenance implies that these properties
precisely alter as cycle period changes so as to always keep neuronal activity
and cycle period in proportion.
A major research area in the lab is to understand how this intrinsic phase maintenance occurs. Pyloric neurons respond to hyperpolarization with a delayed rebound depolarization and firing, and changes in this rebound delay are part of the mechanism underlying phase maintenance in the intact network. We have studied this process in one of the pyloric cell types (the PY neurons) by isolating the neurons from the network, inducing them to fire by injecting rhythmic trains of hyperpolarizing current pulses into the neurons, and measuring the delay to rebound firing as we change current pulse cycle period and duty cycle (interpulse interval divided by cycle period). This work has shown that rebound delay in these neurons depends on both cycle period and duty cycle, and that each PY neuron responds to changes in these two parameters in different ways. This differing response implied that comparison among the rebound delays of several PY neurons would allow nervous systems to uniquely distinguish among different rhythmic patterns, and we provided a simple, neurobiologically plausible implementation of this process (Hooper, 1998; Kristan, 1998; Golowasch et al., 1999).
The mechanism underlying intrinsic phase-maintaining properties of isolated Pyloric (PY) neurons is being investigated by a postdoctoral fellow who has recently joined the lab, Einat Buchman. Her initial work suggests that these properties are the result of the combined interaction of the hyperpolarization activated current Ih; the depolarization activated, rapidly inactivating K current IA; and the calcium activated K current. This work is exciting because it makes a series of predictions that we can test experimentally, work being performed by Jeff Thuma, Scott Hooper, and Adam Weaver.
Although it plays a major role in phase maintenance in the intact network, intrinsic phase maintenance by individual neurons is only part of the story; the rest of this process occurs as a result of network-based interactions among the network's neurons. 
Adam Weaver has been investigating this question in a series of experiments in which he removes the Lateral Pyloric (LP) and Ventricular Dilator (VD) neurons from the network by hyperpolarization, and tests the effect this treatment has on the network's response to cycle period alterations. This work has revealed that these neurons function as cycle period governor neurons. The LP neuron serves to slow the network when period is decreased by current injection into the network pacemaker, but has no effect when period is increased. Conversely, the VD neuron serves to speed up the network when period is increased by current injection in the pacemaker, but has no effect when period is decreased. These two neurons thus play complimentary roles in period maintenance in the network. To gain more insight as to how network interactions shape pyloric activity, Adam has also been entraining the network by rhythmic current pulse injection into the LP and VD neurons, and has found that the primary entrainment mode is non-integer (2:3, 3:5, etc.). Although these results have been predicted theoretically, to our knowledge this is the first experimental data examining this phenomenon in any detail. Given the experimental and modeling advantages of the pyloric network (the ability to delete neurons from the network, existing neuron models) we are excited about following up this work, and believe it might materially advance our understanding of how the network functions.
The second major interest of the lab is to determine what effect on muscle activity these changes in neuron output have (considerable evidence suggests that pyloric muscles in a related species are not faithful followers of their input (Meyrand and Moulins, 1986; Meyrand and Marder, 1991)). This work was performed
by Lee Morris and several undergraduate researchers, who showed that many pyloric muscles were much too slow to accurately follow the rapid pyloric pattern, and that the primary motor output of the muscles when driven by an unvarying pyloric input would be a temporally summated, tonic contraction (Morris and Hooper, 1997 & 1998; Harness, et al., 1998; Koehnle, et al., 1997; Ellis, et al., 1996). However, my research associate, Jeff Thuma, has shown that in fact the pyloric output is not unvarying, but is instead slightly but significantly modulated by the activity of other, much slower, stomatogastric networks (Thuma & Hooper, 2002, 2003). Experiments by Jeff and my former graduate student, Lee Morris, have shown that the slow pyloric muscles extract this modulation, and therefore primarily contract in phase with neural networks none of whose neurons innervate the muscles in question (Morris et al., 2000; Thuma et al., 2003). These data completely change the field's working hypothesis of what function the pyloric network evolved to serve, and what function the pylorus performs. We are therefore now beginning a major new effort aimed at more fully defining the details of pyloric muscle response to neural input, and at creating a dynamic, three-dimensional model of the stomach in which we can use our muscle data to predict stomach motions.
Literature cited above
For a complete list of all STG researchers visit the Hartline STG web page
Important publications:
SL Hooper, JB Thuma, KH Hobbs (2008) Invertebrate muscles: thin and thick filament structure; molecular basis of contraction and its regulation, catch and asynchronous muscle. Progress in Neurobiology, in press
KH Hobbs, SL Hooper (2008) Using complicated, wide dynamic range, driving to develop models of single neurons in single recording sessions. J Neurophysiology, in press
JB Thuma, PI Harness, TJ Koehnle, LG Morris, and SL Hooper (2007) Muscle anatomy is a primary determinant of muscle relaxation dynamics in the lobster (Panulirus interruptus) stomatogastric system. J Comparative Physiology A 193:1101-1113.
SL Hooper, C Guschlbauer, G von Uckermann, A Buschges (2007) Slow Temporal Filtering May Largely Explain the Transformation of Stick Insect (Carausius morosus) Extensor Motor Neuron Activity Into Muscle Movement. J Neurophysiology 98:1718-1732.
SL Hooper, C Guschlbauer, G von Uckermann, A Buschges (2007) Different motor neuron spike patterns produce contractions with very similar rises in graded slow muscles. J Neurophysiology 97:1428-1444.
SL Hooper, C Guschlbauer, G von Uckermann, and A Buschges (2006) Natural Neural Output that Produces Highly Variable Locomotory Movements. J Neurophysiology 96:2072-2088.
SL Hooper and RA DiCaprio (2004) Crustacean motor pattern generator networks. Neurosignals 13:50-69.
JB Thuma, LG Morris, AL Weaver, and SL Hooper (2003) Lobster (Panulirus interruptus) pyloric muscles express the motor patterns of three neural networks, only one of which innervates the muscles. J Neuroscience, 23:8911-8920.
AL Weaver and SL Hooper (2003) Relating Network Synaptic Connectivity and Network Activity in the Lobster (Panulirus interruptus) pyloric network. J Neurophysiology, 90:2378-2386.
AL Weaver and SL Hooper (2003) Follower Neurons in Lobster (Panulirus interruptus) Pyloric Network Neurons that Regulate Pacemaker Period in Complementary Ways. J Neurophysiology, 89:1327-1338.
JB Thuma and SL Hooper (2003) Quantification of cardiac sac network effects on a movement related parameter of pyloric network output in the lobster. J Neurophysiology, 89:745-753.
SL Hooper, E Buchman, KH Hobbs (2002) A computational role for slow conductances: single neuron models that measure duration. Nature Neuroscience, 5:552-556
JB Thuma and SL Hooper (2002) Quantification of gastric mill network effects on a movement related parameter of pyloric network output in the lobster. J Neurophysiology, 87:2372-2384.
NJ Hoover, AL Weaver, PI Harness, and SL Hooper (2002) Combinatorial and cross-fiber averaging transform muscle electrical responses with a large random component into deterministic contractions. J Neuroscience 22:1895-1904.
LG Morris and SL Hooper (2001) Mechanisms underlying stabilization of temporally summated muscle contractions in the lobster (Panulirus) pyloric system. J Neurophysiology 85:254-268.
LG Morris, JB Thuma, and SL Hooper (2000) Muscles express motor patterns of non-innervating neural networks by filtering broad-band input. Nature Neuroscience 3:245-250.
SL Hooper (1998) Transduction of temporal patterns by single neurons. Nature Neuroscience 1:720-726.
LG Morris and SL Hooper (1998) Muscle response to changing neuronal input in the lobster (Panulirus interruptus) stomatogastric system: Slow muscle properties can transform rhythmic input into tonic output. J Neuroscience, 18:3433-3442.
LG Morris and SL Hooper (1997) Muscle response to changing neuronal input in the lobster (Panulirus interruptus) stomatogastric system: Spike number vs. spike frequency dependent domains. J Neuroscience 17:5956-5971.
SL Hooper (1997) The pyloric pattern of the lobster (Panulirus interruptus) stomatogastric ganglion comprises two phase maintaining subsets. J Computational Neurosci 4:207-219.
SL Hooper (1997) Phase maintenance in the pyloric pattern of the lobster (Panulirus interruptus) stomatogastric ganglion. J Computational Neurosci 4:191-206.
Book Chapters and Past Publications
Lab Members
Current | | | | Former
Grad Students | | | | Former Undergrads |
| | | | Lee Morris (PhD) | | | | Tim Ellis |
| | | | Einat Arian (Post Doc) | | | | Patricia Harness |
| | | | | | | | Kevin Hobbs |
Bill White
(PhD candidate) | | | | Chuck Geier (MS) | | | | Neil Hoover |
Chris Quolke
(undergrad) | | | | Bethany Revill (BS) | | | | Holly Hunt |
| | | | Boban Abraham (MS) | | | | Stacy Kehl |
| | | | | | | | Thomas Koehnle |
| | | | | | | | Melissa Rehn |
| | | | | | | | Bethany Revill |
