Professor of Neurobiology
Chair in the Department of Neurobiology
Faculty Network Member of the Duke Institute for Brain Sciences
We ask how the brain works when it is working. Our goal is to understand the general principles of brain operation, through analysis of a relatively simple sensory-motor system in a complex animal. We study the control of eye movements in awake, behaving rhesus monkeys. We analyze eye movement behavior quantitatively, we make recordings from one or multiple brain cells during eye movement behavior, and we use theory and computational modeling.
One area of our research concerns how the neural circuit for pursuit works as a whole to transform the sensory representation in extrastriate area MT into commands for rationale and accurate movements. A second area of research concerns how we learn motor skills. We have shown that the cerebellum is critical for motor learning, and we have provided evidence that learning occurs both in the cerebellar cortex and the deep cerebellar nuclei. We also have discovered a form of very rapid plasticity that occurs when the visual detection of movement errors causes "climbing fiber responses" in the cerebellum.
Selected Recent Publications
- Lisberger SG (2020) The rules of cerebellar learning: around the Ito hypothesis. Neuroscience 462: 175-190. PMC7914257
- De Zeeuw CI, Lisberger SG Raymond JL (2021) Diversity and dynamism in the cerebellum. Nat. Neurosci. 24: 160-167.
- Darlington TR, Beck JM, Lisberger SG (2018) Neural implementation of Bayesian inference in a sensory-motor behavior. Nat. Neurosci. 21: 1442-1451. PMC6312195.
- Herzfeld DJ, Hall NJ, Trigides M, Lisberger SG (2020) Principles of operation of a learning neural circuit. eLife. 2020;9:e55217 DOI: 10.7554/eLife.55217 PMC7255800
- Behling S, Lisberger SG (2020) Different mechanisms for modulation of the initiation and steady-state of smooth pursuit eye movements. J. Neurophysiol. 123: 1265-1276. PMC7099477
Our Research Approach
Our goal is to discover general principles of the organization and operation of neural systems. Our approach is to study the eye movements of rhesus monkeys so that we can take advantage of the ease of controlling, measuring, and quantifying eye movement behavior while we monitor the activity of one or more neurons in the circuit that generates the movements. We study smooth pursuit eye movements because they comprise a motor system with the same cortical and sub-cortical architecture as other sensory-motor systems. Thus, we can learn how the brain transforms sensory inputs into coordinated movements, through analysis of how it moves the eyes.
The basic circuit for pursuit is known. Sensory inputs arise from the middle temporal visual area (MT). The “motor cortex” for pursuit is the smooth eye movement region of the frontal eye fields (FEFsem). Other well studied areas include the floccular complex of the cerebellum, the pontine nuclei that connect cortex to cerebellum, and the final brainstem motor circuits.
The multiple parallel pathways in the pursuit system decode the representation of visual motion in area MT and transform it into commands for eye velocity. We think of sensory-motor decoding as having two major components. One converts the population response in area MT into estimates of target speed and direction. The other, through FEFsem, uses the population response in MT to estimate motion reliability and generates a signal that modulates the strength of visual-motor transmission. Downstream, neural integration converts a transient visual motion signal into a sustained eye velocity command to allow perfect tracking during steady-state pursuit. We think that these simple building blocks work together to allow pursuit to be based on a reliability-weighted combination of sensory data and priors based on past experience and expectations, that is “Bayesian inference”.
Current efforts include expanding our knowledge of the physiology of the pursuit circuit through new recordings with multi-contact probes in the basal ganglia, cerebellum, and cerebral cortex. We also are embarking on a large-scale computer simulation that will use a biologically-motivated circuit model and operate dynamically on a millisecond time scale. We will determine the local and long-range neural computations that allow model neurons to mimic the first- and second-order statistics of the responses of real neurons.
Our research on motor learning in pursuit eye movements has led to a theory of the principles of operation of a learning neural circuit. (i) Early, fast learning occurs in the cerebellar cortex based on depression of synapses from parallel fibers to Purkinje cells guided by errors signaled on climbing fiber inputs. (ii) Later, slow learning occurs in the cerebellar nucleus, transferred from and instructed by the learned changes in simple-spike firing of Purkinje cells. (iii) Feedback from the cerebellar nucleus to the inferior olive reduces climbing fiber inputs and limits the amount of learning in the cerebellar cortex. Current research uses multi-contract probes to record from all cell types in the cerebellar cortex and map more precisely the multiple sites and mechanisms of learning. A large-scale computer simulation will create a model of the cerebellar circuit that learns autonomously. We also will explore long-term learning in the FEFsem.
We rely on exquisite control over a very precise behavior, quantitative and accurate measurements of eye movements, and recordings from isolated neurons throughout the pursuit circuit. Increasingly, our recordings use multi-contact probes that allow us to analyze local connectivity and record from multiple neuron types. We analyze our data quantitatively, frequently using methods that are derived from theoretical approaches. I am open to the possibility of deploying optogenetic approaches strategically in non-human primates.
Organization of the neural system for pursuit eye movements.
Our long-term goal is to assemble our extensive knowledge of the neural basis for pursuit into a biomimetic neural circuit model that operates autonomously and dynamically on a time scale of milliseconds for a full 1-second pursuit response. We possess extensive data on the responses of neurons in sensory area MT, motor area FEFsem, the dorsolateral pontine nucleus, the nucleus reticularis tegmenti pontis, and the floccular complex of the cerebellum. Our neural circuit model will include local recurrent circuits at each node of the system, and long-range forward and recurrent connections among the nodes. We will explore what circuits and connections are needed so that the model neurons reproduce the diversity of neural responses in each area as well as the first- and second-order statistics of the neural responses and the behavior.
Our recordings are obtained with single electrodes, tetrodes, or multi-contact probes during multiple behavioral paradigms. These include the initiation of pursuit and steady-state tracking for different target speeds and the effects of reduced motion reliability through control of stimulus contrast or the coherence of a dot patch. We study the relationship between neural and behavioral responses during brief pulses of target motion under different conditions of modulation of the strength (“gain”) of visual-motor transmission. We explore how the neural system changes in relation to a reliability-weighted combination of sensory data and past experience. To complete our data set, a number of experiments are needed in the cerebellar flocculus, MT, FEFsem, and we plan to explore responses in the basal ganglia. Finally, we are open to the strategic use of optogenetics in monkeys to crack the neural circuit and explore the predictions of our circuit model.
Multiple components of learning in pursuit eye movements.
A single change in target direction causes a small change in eye movement in the subsequent trial. An important neural correlate of "single trial learning" occurs in the floccular complex of the cerebellum, where a single complex spike on one learning trial causes a properly-timed depression of simple spike response on the subsequent trial. Yet, there are multiple components of learning that have different time courses of acquisition. Preliminary data indicate that neural learning in the cerebellar cortex declines after a few hundred learning trials, even as behavioral learning continues to increase. We are investigating the neural expression of learning over short and long time courses in both the cerebellum and the cerebral cortex
Circuit mechanisms for cerebellar function and learning.
We are using multiple-contact electrodes to record simultaneously from multiple neighboring single-units in the cerebellar cortex of the floccular complex. Use of an improved spike-sorter designed for our use case, we can identify essentially all of the spikes of many neurons simultaneously and identify the layer of each recording. Through calculation of spike-timing cross-correlograms with Purkinje cells, we can determine the anatomical identify of interneuron recordings. After identifying the cell-types of isolated neurons and recording their activity during smooth pursuit eye movements and pursuit learning, then we will construct a circuit model of the cerebellar cortex and explain how the circuit works and learns.
Lisberger, Stephen G. “The Rules of Cerebellar Learning: Around the Ito Hypothesis.” Neuroscience 462 (May 10, 2021): 175–90. https://doi.org/10.1016/j.neuroscience.2020.08.026.
De Zeeuw, Chris I., Stephen G. Lisberger, and Jennifer L. Raymond. “Publisher Correction: Diversity and dynamism in the cerebellum.” Nat Neurosci 24, no. 3 (March 2021): 450. https://doi.org/10.1038/s41593-020-00782-5.
De Zeeuw, Chris I., Stephen G. Lisberger, and Jennifer L. Raymond. “Diversity and dynamism in the cerebellum.” Nat Neurosci 24, no. 2 (February 2021): 160–67. https://doi.org/10.1038/s41593-020-00754-9.
Lee, Joonyeol, Timothy R. Darlington, and Stephen G. Lisberger. “The Neural Basis for Response Latency in a Sensory-Motor Behavior.” Cereb Cortex 30, no. 5 (May 14, 2020): 3055–73. https://doi.org/10.1093/cercor/bhz294.
Herzfeld, David J., Nathan J. Hall, Marios Tringides, and Stephen G. Lisberger. “Principles of operation of a cerebellar learning circuit.” Elife 9 (April 30, 2020). https://doi.org/10.7554/eLife.55217.
Behling, Stuart, and Stephen G. Lisberger. “Different mechanisms for modulation of the initiation and steady-state of smooth pursuit eye movements.” Journal of Neurophysiology 123, no. 3 (March 2020): 1265–76. https://doi.org/10.1152/jn.00710.2019.
Darlington, Timothy R., and Stephen G. Lisberger. “Mechanisms that allow cortical preparatory activity without inappropriate movement.” Elife 9 (February 21, 2020). https://doi.org/10.7554/elife.50962.
Hall, Nathan J., Yan Yang, and Stephen G. Lisberger. “Multiple components in direction learning in smooth pursuit eye movements of monkeys.” J Neurophysiol 120, no. 4 (October 1, 2018): 2020–35. https://doi.org/10.1152/jn.00261.2018.
Darlington, Timothy R., Jeffrey M. Beck, and Stephen G. Lisberger. “Neural implementation of Bayesian inference in a sensorimotor behavior.” Nat Neurosci 21, no. 10 (October 2018): 1442–51. https://doi.org/10.1038/s41593-018-0233-y.
Raghavan, Ramanujan T., and Stephen G. Lisberger. “Responses of Purkinje cells in the oculomotor vermis of monkeys during smooth pursuit eye movements and saccades: comparison with floccular complex.” J Neurophysiol 118, no. 2 (August 1, 2017): 986–1001. https://doi.org/10.1152/jn.00209.2017.
De Zeeuw CI, Lisberger SG Raymond JL (2021) Diversity and dynamism in the cerebellum. Nat. Neurosci. 24: 160-167.
Lisberger SG (2020) The rules of cerebellar learning: around the Ito hypothesis. Neuroscience 462: 175-190. PMC7914257
Lisberger, S.G. (2015) Visual guidance of smooth pursuit eye movements. Ann. Rev. Vis. Sci. 1: 447-468.
Lisberger S.G. and Medina J.F. (2015) How and why neural and motor variation are related. Curr. Opin. Neurosci. Curr Op Neurobio 33: 110-116.
Lisberger, S.G. (2013) Sound the Alarm: Fraud in Neuroscience. Cerebrum, Dana Foundation, May 1, 2013.
Hatten, M.E. and Lisberger, S.G. (2013) Multitasking on the run. eLife2: e00641. doi:7554/eLife.00641
Lisberger, S.G. (2010) Smooth pursuit eye movements: sensation, action, and what happens in between. Neuron 66: 477-491. PMC2887486
Lisberger, S.G. (2009) Internal models of eye movement in the floccular complex of the monkey cerebellum. Neuroscience, 162: 763-776. PMC2740815. PDF
Carey, M.R. and Lisberger, S.G. (2002) Embarrassed but not depressed: some eye opening lessons for cerebellar learning. Neuron 35: 223-226.
Raymond, J.L. and Lisberger, S.G. (2000) Hypotheses about the neural trigger for plasticity in the circuit for the vestibulo-ocular reflex.Prog. Brain Res. 124: 235-246.
Lisberger, S.G. (1998) Cerebellar LTD: A molecular mechanism of behavioral learning? Cell 92: 701-704.
Lisberger, S.G. (1998) Physiological basis for motor learning in the Vestibulo-ocular reflex. Otalaryngology - Head & Neck Surgery, 119: 43-48.
Raymond, J., Lisberger, S.G., and Mauk, M., (1996) The cerebellum: a neuronal learning machine? Science 272: 1126-1131.
du Lac, S., Raymond, J.L., Sejnowski, T.J. and Lisberger, S.G. (1995) Learning and memory in the vestibulo-ocular reflex. Annual Review of Neuroscience, 18: 409-41, 1995.
Raymond, J.L and Lisberger, S.G. (1995) Error signals in horizontal gaze velocity Purkinje cells under stimulus conditions that cause learning in the VOR. Proceedings of The New York Academy of Sciences Symposia, June.
Lisberger, S.G. (1995) Motor learning and memory in the vestibulo-ocular reflex: the dark side. Proceedings of The New York Academy of Sciences Symposia, June.
Lisberger, S. G. (1995) Learning and memory in the vestibulo-ocular reflex, in “Mechanisms of motor learning and memory.”
Lisberger, S.G. (1995) A mechanism of learning found? Current Biology, 5: 221-224.
Lisberger, S.G. (1994) Multiple sites of motor learning in the vestibulo-ocular reflex. In: Cellular and Molecular Mechanisms Underlying Higher Neural Functions, A.I. Selverston and P. Ascher, eds., pp. 41‑47, John Wiley & Sons, Ltd.
Lisberger, S.G. and Sejnowski, T.J. (1993) Cerebellar flocculus hypothesis - reply. Nature 363: 25.
Coenen, O., Sejnowski, T.J. and Lisberger, S.G. (1993) Biologically plausible local learning rules for the adaptation of the vestibulo-ocular reflex. Advances in Neural Information Processing Systems 5: 961‑968.
Viola, P.A., Lisberger, S.G. and Sejnowski, T.J. (1992) Recurrent eye tracking network using a distributed representation of image motion.Advances in Neural Information Processing Systems 4: 380‑387.
Lisberger, S.G. and Sejnowski, T.J. (1992) Computational analysis suggests a new hypothesis for motor learning in the vestibulo-ocular reflex. Technical Report INC‑9201, Inst. Neural Computation, UC, San Diego.
Sejnowski, T.J. and Lisberger, S.G. (1991) Neural systems for eye tracking. Naval Research Reviews 4:10‑15.
Lisberger, S.G., Broussard, D.M. and Brontë-Stewart, H.M. (1990) Properties of pathways that mediate motor learning in the vestibulo-ocular reflex of monkeys. Cold Spring Harbor Symposia on Quantitative Biology 55: 813‑822.
Movshon, J.A., Lisberger, S.G. and Krauzlis, R.J. (1990) Visual cortical signals supporting smooth pursuit eye movements. Cold Spring Harbor Symposia on Quantitative Biology. 55: 707‑716.
Stone, L.S. and S.G. Lisberger (1989) Synergistic action of complex and simple spikes in the monkey flocculus in the control of smooth-pursuit eye movement. Exp. Brain Res. (Supplement) 17: 299‑312.
Lisberger, S.G. (1988) The neural basis for motor learning in the vestibulo-ocular reflex in monkeys. Trends in Neurosci. 11: 147‑152.
Lisberger, S.G., Morris, E.J. and Tychsen, L. (1987) Visual motion processing and sensory motor integration for smooth pursuit eye movements. Ann. Review Neurosci. 10: 97‑129.
Lisberger, S.G. (1986) Properties of pathways subserving long-term adaptive plasticity of the vestibulo-ocular reflex in monkeys. In: The Biology of Change, R. Ruben, ed., Elsevier.
Miles, F.A., Optican, L.M. and Lisberger, S.G. (1985) An adaptive equalizer model of the primate vestibulo-ocular reflex. In: Adaptive mechanisms in gaze control: Facts and theories, A. Berthoz and G. Melvill Jones, eds., pp. 313‑362, Elsevier.
Lisberger, S.G. (1982) Role of the cerebellum during motor learning in the vestibulo-ocular reflex: different mechanisms in different species? TINS 5:437‑441.
Lisberger, S.G. (1981) The signal processing and function of the flocculus during smooth eye movement in the monkey. In: the Cerebellum: New Vistas, S. Palay and V. Chan‑Palay, eds., Elsevier.
Miles, F.A. and Lisberger, S.G. (1981) Plasticity in the vestibulo-ocular reflex: a new hypothesis. Annual Rev. Neurosci. 4: 273‑299.
Miles, F.A. and Lisberger, S.G. (1981) The "error" signals subserving adaptive gain control in the primate vestibulo-ocular reflex. Ann. N.Y. Acad. Sci. 374:513‑525.
Fuchs, A.F., Evinger, L.C., King. W.M., Lisberger, S.G., Baker, R. (1978) Single unit and lesion studies on the monkey. In: Disorders of Ocular Motility: Neurophysiological and Clinical Aspects, G. Kommerell, ed., J.F. Bergmann Verlag, Munchen.
Lisberger, S.G. and Fuchs, A.F. (1978) Role of the primate flocculus during rapid behavioral modification of the vestibulo-ocular reflex: clinical implication. In: Disorders of Ocular Motility: Neurophysiological and Clinical Aspects, G. Kommerell, ed., J.F. Bergmann Verlag, Munchen.
Lisberger, S.G. and Fuchs, A.F. (1977) Role of the primate flocculus in smooth pursuit eye movements and rapid behavioral modification of the vestibulo-ocular reflex. In: Control of Gaze by Brainstem Interneurons, R. Baker and A. Berthoz, eds., Elsevier.
Redesigning signal conditioning hardware
Published March 10, 2015
We are currently working with an engineer to develop a new set of printed circuit boards that will implement the signal conditioning boxes originally designed by Ken McGary. In due course, we should be able to provide fresh boards.
"How and why neural and motor variation are related" is in press
Published March 10, 2015
Movements are variable. Recent findings in smooth pursuit eye movements provide an explanation for motor variation in terms of the organization of the brain’s sensory-motor pathways.