Our Research Approach 

Our goal is to discover general principles of the organization and operation of neural systems. For many years, we studied the eye movements of rhesus monkeys to 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. Smooth pursuit eye movements 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 generate a signal that modulates the strength of visual-motor transmission. Downstream, a recurrent circuit between the brainstem and cerebellum converts a transient visual motion signal into a sustained eye velocity command to allow perfect tracking during steady-state pursuit. Together, these simple building blocks allow pursuit to be based on a reliability-weighted combination of sensory data and priors based on past experience and expectations, that is to approximate “Bayesian inference”.

The next step is creation of 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 the cerebellum, and on motor learning in pursuit eye movements, 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 the frequency of climbing fiber inputs and limits the amount of learning in the cerebellar cortex.

We have used multi-contract probes to record from all major neuron types in the cerebellar cortex and have used carefully-contrived approaches to spike sorting and a deep learning classifier to identify neuron types by the features of the extracellular waveforms and discharge statistics. map more precisely the multiple sites and mechanisms of learning. Analysis of our data has revealed how the cerebellar micro-circuit performs input-output transformations of both temporal dynamics and directional organization. A large-scale computer simulation reproducez the time varying responses of individual Purkinje cells. It also predicts that details of a the temporal basis set provided by granule cells and shows how the cerebellum uses that temporal basis set during baseline pursuit and motor learning.  Next steps will leverage our data to create a biomimetic circuit model of the cerebellar circuit that learns autonomously.

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.

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