Georgia State University
Atlanta, GA 30303
Tel: (404) 413-5394 (office)
Tel: (404) 413-5357 (Lab)
and Research Centers
Neuroscience at GSU
Donald H. Edwards (Regents' Professor)
David W. Cofer (Postdoc)
Giselle Linan-Velez (Ph.D. Student)
Eric Randall (Ph.D. Student)
Bryce Chung (Ph.D. Student)
Maryanne Dos Santos (M.S. Student)
Uzma Tahir (Undergraduate)
Marjorie Sen (Emory University)
Fadi Issa (Georgia State University)
Daniel Adamson (Paidea School, Atlanta)
Eric James (St. Johns University, NY)
Katie McAlister (Swarthmore College)
Aaron Miller (Oberlin College)
Catherine McCurdy (Georgia State University)
Rodney Hillis, M.S. 1988
Jiangyan Shi, M.S., 1999
Lisa Blumke, M.S., 2008 Currently: Instructor Biology, Georgia Highlands College
Dr. Ted W. Simon, Ph.D. 1988 Currently: US EPA
Dr. Shih-Rung Yeh, Ph.D., 1996; Postdoc: 1996-1998 Currently Assoc. Prof. Biology National Tsing-Hua University, Taiwan
Dr. Steven Versteeg Ph.D. student, Univ. Melbourne, 2004; Dissertation under my direction, Currently: Research Staff, CA Labs, Melbourne, Australia
Dr. Cha-Kyong Song Ph.D., 2006) Currently postdoc, Ewha Women’s University, Seoul, Korea
Dr. Nadja Spitzer, Ph.D. 2006. Postdoc, 2007-2007; Currently, Postdoc, Dept. Biology, Marshall Univ., Huntington, W.Va.
Dr. Barbara E. Musolf, Ph.D. 2007 Currently: Assoc. Professor of Biology, Clayton State University, Georgia.
Dr. Fadi A. Issa, Ph.D. 2008. Currently postdoc, Department of Physiology, UCLA School of Medicine.
Dr. David Wayne Cofer, Ph.D. 2009 Currently postdoc in this lab.
Dr. Esther M. Leise, Postdoc: 1989-1990 Currently: Prof. of Biology, UNC Greensoboro
Dr. Peri Nagappan, Postdoc: 1990-1994 Currently with the Morehouse School of Medicine
Dr. Marc Weissburg, Postdoc: 1994 Currently: Prof. of Biology, Georgia Institute of Technology
Dr. Clifford Opdyke, Postdoc: 1994-1995 Currently: Georgia Environmental Protection Division
Dr. Linda Anderson, Postdoc: 1995-1996
Dr. Shih-Rung Yeh, 1996-1998 Currently Assoc. Prof. Biology National Tsing-Hua University, Taiwan
Dr. Joanne Drummond, Postdoc: 1996-1998 Currently with GlaxoSmithKline Australia
Dr. Jens Herberholz, Postdoc: 1999-2005 Currently: Assist. Prof. Psychology, Univ. Maryland, College Park
Dr. Brian Antonsen, Postdoc: 1999-2007 Currently: Assist. Prof. Biology, Marshall Univ., Huntington, W.Va.
Dr. Jeffrey Triblehorn Postdoc: 2003-2006 currently: Assist Prof. College of Charleston, Charleston, SC
Dr. Nadja Spitzer, Postdoc: 2007-2007; Currently: Postdoc, Dept. Biology, Marshall Univ., Huntington, W.Va.
Dr. Fadi A. Issa, Postdoc, 2008 Currently postdoc, Department of Physiology, UCLA School of Medicine.
Dr. David W. Cofer, Postdoc: 2009-2011.
Mr. Christopher Mims (Tech. 2001-04) Currently: Editor, Scientific American.com
Ms. Elizabeth DeGoursac (Tech. 2002-03): Currently: Emory Univ. Neuroscience Grad. Prog.
Ms. Laurel Johnstone (Tech. 2000-06)
We use a "reversed-engineering" approach to discover how animals work. We first study the behavior, anatomy, and physiology of an animal to determine how its parts fit together and interact. We then build computer models of these parts, including some or all of the body and the nervous system, that capture their key properties that allow them to function. We then put the models together in an overall model of the animal, place it in a virtual Newtonian world, and then test it to see whether the model's behavior resembles that of the animal, and how the its different parts contribute to the behavior.
Arm Flexion Sensory feedback from muscle spindles and other receptors mediates reflexes that stabilize the body against outside perturbations. During voluntary limb movement, the reflex circuitry has to be prevented from interfering with the movement while also assisting the limb to reach the new position, stabilized against additional perturbation. We studied this using an AnimatLab model of human arm flexion (Fig. 1, Movie 1), and described it in Cofer et al., 2010a.
Locust Jump The neural circuitry and biomechanics of kicking in locusts have been studied to understand their roles in the control of both kicking and jumping. It had been hypothesized that the same neural circuit and biomechanics governed both behaviors, but this hypothesis was not testable with current technology. We built a neuromechanical model according to current knowledge of the leg biomechanics and the anatomy and physiology of the neural circuit that produced kicking (Fig. 2). When excited appropriately, the circuit produced model jumping and kicking behaviors (see Fig. 3, Movie) that we compared to published results of real kicks and jumps and to the kicking and jumping behavior of 5 live locusts. We found that the model replicated the live data for both kicks and jumps in all particulars. This confirmed that the kick neural circuitry can produce the jump behavior. This is described in Cofer et al., 2010b and Cofer et al., 2010c.
Crayfish We are interested in how the nervous system controls the behavior of animals. To this end, we have focused on the crayfish, a small invertebrate that has been extensively studied for the past 100 years (see www2.biology.ualberta.ca/palmer/thh/crayfish.htm for Thomas Huxley's classic description), and has a large behavioral repertoire and a relatively small nervous system. crayfish live primarily underwater, in streams, rivers, ponds and swamps, but they also venture out on land when it's wet, sometimes migrating considerable distances overland between watersheds. They dig multichambered burrows in mud banks for homes, and live and have their babies in them. They often live in communities of other crayfish, and compete with them for food, shelter, and mates. They have hierarchical social relationships based on their social interactions with each other and on their size and personality. They forage for food on land and underwater, eating plants and preying on other animals. Our research in the past has concerned the escape behavior of crayfish, the circuits that produce escape, and how they and the behavior is modulated by serotonin and by changes in the animal's social status. Descriptions of this research are found below.
Crayfish Posture and Walking We have been studying the control of posture and locomotion in crayfish, with particular attention to the role of sensory feedback from proprioceptors like the coxa-basipodite chordotonal organ (CBCO), which measures the angle and rate of change of the angle of the depression-levation joint of the legs. When crayfish walk, each leg elevates and promotes during the swing phase, and then depresses and remotes during the stance phase. Depressor motorneuron bursts alternate with anterior levator motorneuron bursts; the posterior levator overlaps both, beginning during the depression phase (Fig. 4). A movie of the animal walking, the motor nerves firing, and plots of the joint angles of the 5th left leg can be seen here: Crayfish Walking.
To start, have constructed a 4-legged AnimatLab model of the crayfish based on published descriptions of the animal's anatomy, the origins, insertions, and properties of the walking leg muscles, and the physiology of its sensory and motor neurons (Fig. 5). (A 4-legged model is significantly less complex than an 8-legged model). The rhythmic movements of the legs during walking are governed by a set of central pattern generators (CPGs). Because the precise circuitry that produces the relative phasing of each of the joints in a leg has not been described, we have made up circuitry that does that. We are using the model to study the role of the CBCO in mediating resistance and assistance reflexes during standing and locomotion. As can be seen here (Crayfish Walking Model), the model captures many of the features of crayfish walking.
Our primary focus has been on one of the best-understood circuits in any nervous system, the tail flip escape circuit in crayfish. We have used this circuit and behavior to study a variety of issues, including changes in the brain during social hierarchy formation (Yeh, et al., 1996; Yeh, et al., 1997) (Issa et al., 1999), coincidence detection (Edwards, et al., 1998), and the neural bases for behavioral choice (Edwards, 1991; see the web-based simulation of our model by Steven Versteeg at the University of Melbourne (http://www.cs.mu.oz.au/~scv/sim/simcray.html). Many of these results are summarized in a review in Trends in Neurosciences. We are also interested in understanding the neural bases for behavioral choice (Edwards, 1991; see the web-based simulation of our model by Steven Versteeg at the University of Melbourne (http://www.cs.mu.oz.au/~scv/sim/simcray.html). This work was supported by the NSF and by the NIH.
Dominance Hierarchy Formation Social animals form hierarchies so as to divide resources without unnecessary conflict. Conflict may occur at the outset, however, when two unfamiliar animals meet, and they are of about equal size and strength. Then agonistic interactions may escalate to full fighting. Usually these fights are brief, and end when one animal suddenly withdraws. The new dominant may pursue briefly, so as to 'pound in' the lesson of who won and who lost. The sudden change in behavior, from fighting to retreat, is indicative of a corresponding change in the brain about which we know very little. We have found that in crayfish, this change in behavior is reflected in the frequency with which several agonistic behaviors (approach, attack, and offensive tailflip) and several defensive behaviors (retreat, three forms of escape tailflip) are displayed (Herberholz
Coincidence Detection. Coincidence detection is important for functions as diverse as Hebbian learning, binaural localization, and visual attention. We have found that extremely precise coincidence detection is a natural consequence of the normal function of rectifying electrical synapses (Edwards et al., 1998). Such synapses open to bidirectional current flow when presynaptic cells depolarize relative to their postsynaptic targets, and remain open until well after completion of presynaptic spikes. When multiple input neurons fire simultaneously, the synaptic currents sum effectively and produce a large EPSP. However, when some inputs are delayed relative to the rest, their contributions are reduced by the early EPSP and by postsynaptic current shunts through junctions already opened by the earlier inputs. These mechanisms account for the ability of the lateral giant neurons of crayfish to sum synchronous inputs, but not inputs separated by only 100 msec. This coincidence detection enables crayfish to produce reflex escape responses only to very abrupt mechanical stimuli. In light of recent evidence that electrical synapses are common in the mammalian CNS, the mechanisms of coincidence detection described here may be widely used in many systems. (Return to Contents)
and Neuronal Integration.
Neurons must work both when they are small in young animals and when they are
larger in adult animals. The increase in size that neurons experience,
however, changes the way in which electrical current flows through them, and so
changes they way in which they respond to synaptic inputs. We have
found that two sets of giant neurons, one in cricket and one in crayfish,
exemplify two different patterns of neuronal growth that have different effects
on neuronal integration. The medial giant interneurons (MGI) in cricket
maintains its response properties as it grows. It grows approximately
uniformly, but such that the diameters of its processes increase as the square
of their increase in length (Hill et al., 1994). The lateral giant (LG) neuron in
crayfish becomes more of a low-pass filter as it grows, which causes it to
switch input circuits and become susceptible to response habituation. It
grows approximately isometrically (Edwards et al., 1994a, b) . A cable analysis of these
different patterns of uniform growth indicates that, regardless of the initial
shape or distribution of active and passive membrane properties, a cell that
grows uniformly and with neurite diameters increasing as the square of their
increase in length will not change the way voltage is distributed through the
cell (Olsen et al., 1996). This "isoelectrotonic"
pattern of growth will enable a synaptic potential created at corresponding
positions in the small and large cells to evoke identical responses at all
other corresponding points in the two cells. Conversely, uniform isometric growth
causes the cell to become electrically larger, and to become a low-pass
filter. These analytical results account for the different effects of
growth on the two cells, in which MGI grows isoelectrotonically, and LG grows
isometrically. (Return to Contents)