Publications by authors named "Stephen G Lisberger"

81 Publications

Publisher Correction: Diversity and dynamism in the cerebellum.

Nat Neurosci 2021 Mar;24(3):450

Department of Neurobiology, Stanford University, Stanford, CA, USA.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41593-020-00782-5DOI Listing
March 2021

Diversity and dynamism in the cerebellum.

Nat Neurosci 2021 02 7;24(2):160-167. Epub 2020 Dec 7.

Department of Neurobiology, Stanford University, Stanford, CA, USA.

The past several years have brought revelations and paradigm shifts in research on the cerebellum. Historically viewed as a simple sensorimotor controller with homogeneous architecture, the cerebellum is increasingly implicated in cognitive functions. It possesses an impressive diversity of molecular, cellular and circuit mechanisms, embedded in a dynamic, recurrent circuit architecture. Recent insights about the diversity and dynamism of the cerebellum provide a roadmap for the next decade of cerebellar research, challenging some old concepts, reinvigorating others and defining major new research directions.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41593-020-00754-9DOI Listing
February 2021

The Rules of Cerebellar Learning: Around the Ito Hypothesis.

Neuroscience 2021 05 29;462:175-190. Epub 2020 Aug 29.

Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA. Electronic address:

As a tribute to Masao Ito, we propose a model of cerebellar learning that incorporates and extends his original model. We suggest four principles that align well with conclusions from multiple cerebellar learning systems. (1) Climbing fiber inputs to the cerebellum drive early, fast, poorly-retained learning in the parallel fiber to Purkinje cell synapse. (2) Learned Purkinje cell outputs drive late, slow, well-retained learning in non-Purkinje cell inputs to neurons in the cerebellar nucleus, transferring learning from the cortex to the nucleus. (3) Recurrent feedback from Purkinje cells to the inferior olive, through interneurons in the cerebellar nucleus, limits the magnitude of fast, early learning in the cerebellar cortex. (4) Functionally different inputs are subjected to plasticity in the cerebellar cortex versus the cerebellar nucleus. A computational neural circuit model that is based on these principles mimics a large amount of neural and behavioral data obtained from the smooth pursuit eye movements of monkeys.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuroscience.2020.08.026DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7914257PMC
May 2021

Principles of operation of a cerebellar learning circuit.

Elife 2020 04 30;9. Epub 2020 Apr 30.

Department of Neurobiology, Duke University School of Medicine, Durham, United States.

We provide behavioral evidence using monkey smooth pursuit eye movements for four principles of cerebellar learning. Using a circuit-level model of the cerebellum, we link behavioral data to learning's neural implementation. The four principles are: (1) early, fast, acquisition driven by climbing fiber inputs to the cerebellar cortex, with poor retention; (2) learned responses of Purkinje cells guide transfer of learning from the cerebellar cortex to the deep cerebellar nucleus, with excellent retention; (3) functionally different neural signals are subject to learning in the cerebellar cortex versus the deep cerebellar nuclei; and (4) negative feedback from the cerebellum to the inferior olive reduces the magnitude of the teaching signal in climbing fibers and limits learning. Our circuit-level model, based on these four principles, explains behavioral data obtained by strategically manipulating the signals responsible for acquisition and recall of direction learning in smooth pursuit eye movements across multiple timescales.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.7554/eLife.55217DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7255800PMC
April 2020

Mechanisms that allow cortical preparatory activity without inappropriate movement.

Elife 2020 02 21;9. Epub 2020 Feb 21.

Department of Neurobiology, Duke University School of Medicine, Durham, United States.

We reveal a novel mechanism that explains how preparatory activity can evolve in motor-related cortical areas without prematurely inducing movement. The smooth eye movement region of the frontal eye fields (FEF) is a critical node in the neural circuit controlling smooth pursuit eye movement. Preparatory activity evolves in the monkey FEF during fixation in parallel with an objective measure of visual-motor gain. We propose that the use of FEF output as a gain signal rather than a movement command allows for preparation to progress in pursuit without causing movement. We also show that preparatory modulation of firing rate in FEF predicts movement, providing evidence against the 'movement-null' space hypothesis as an explanation of how preparatory activity can progress without movement. Finally, there is a partial reorganization of FEF population activity between preparation and movement that would allow for a directionally non-specific component of preparatory visual-motor gain enhancement in pursuit.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.7554/eLife.50962DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7060051PMC
February 2020

Different mechanisms for modulation of the initiation and steady-state of smooth pursuit eye movements.

J Neurophysiol 2020 03 19;123(3):1265-1276. Epub 2020 Feb 19.

Department of Neurobiology, Duke University School of Medicine, Durham, North Carolina.

Smooth pursuit eye movements are used by primates to track moving objects. They are initiated by sensory estimates of target speed represented in the middle temporal (MT) area of extrastriate visual cortex and then supported by motor feedback to maintain steady-state eye speed at target speed. Here, we show that reducing the coherence in a patch of dots for a tracking target degrades the eye speed both at the initiation of pursuit and during steady-state tracking, when eye speed reaches an asymptote well below target speed. The deficits are quantitatively different between the motor-supported steady-state of pursuit and the sensory-driven initiation of pursuit, suggesting separate mechanisms. The deficit in visually guided pursuit initiation could not explain the deficit in steady-state tracking. Pulses of target speed during steady-state tracking revealed lower sensitivities to image motion across the retina for lower values of dot coherence. However, sensitivity was not zero, implying that visual motion should still be driving eye velocity toward target velocity. When we changed dot coherence from 100% to lower values during accurate steady-state pursuit, we observed larger eye decelerations for lower coherences, as expected if motor feedback was reduced in gain. A simple pursuit model accounts for our data based on separate modulation of the strength of visual-motor transmission and motor feedback. We suggest that reduced dot coherence allows us to observe evidence for separate modulations of the gain of visual-motor transmission during pursuit initiation and of the motor corollary discharges that comprise eye velocity memory and support steady-state tracking. We exploit low-coherence patches of dots to control the initiation and steady state of smooth pursuit eye movements and show that these two phases of movement are modulated separately by the reliability of visual motion signals. We conclude that the neural circuit for pursuit includes separate modulation of the strength of visual-motor transmission for movement initiation and of eye velocity positive feedback to support steady-state tracking.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1152/jn.00710.2019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7099477PMC
March 2020

The Neural Basis for Response Latency in a Sensory-Motor Behavior.

Cereb Cortex 2020 05;30(5):3055-3073

Department of Neurobiology, Duke University School of Medicine, Durham, NC 27710, USA.

We seek a neural circuit explanation for sensory-motor reaction times. In the smooth eye movement region of the frontal eye fields (FEFSEM), the latencies of pairs of neurons show trial-by-trial correlations that cause trial-by-trial correlations in neural and behavioral latency. These correlations can account for two-third of the observed variation in behavioral latency. The amplitude of preparatory activity also could contribute, but the responses of many FEFSEM neurons fail to support predictions of the traditional "ramp-to-threshold" model. As a correlate of neural processing that determines reaction time, the local field potential in FEFSEM includes a brief wave in the 5-15-Hz frequency range that precedes pursuit initiation and whose phase is correlated with the latency of pursuit in individual trials. We suggest that the latency of the incoming visual motion signals combines with the state of preparatory activity to determine the latency of the transient response that controls eye movement.

Impact Statement: The motor cortex for smooth pursuit eye movements contributes to sensory-motor reaction time through the amplitude of preparatory activity and the latency of transient, visually driven responses.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/cercor/bhz294DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7197090PMC
May 2020

Neural implementation of Bayesian inference in a sensorimotor behavior.

Nat Neurosci 2018 10 17;21(10):1442-1451. Epub 2018 Sep 17.

Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA.

Actions are guided by a Bayesian-like interaction between priors based on experience and current sensory evidence. Here we unveil a complete neural implementation of Bayesian-like behavior, including adaptation of a prior. We recorded the spiking of single neurons in the smooth eye-movement region of the frontal eye fields (FEF), a region that is causally involved in smooth-pursuit eye movements. Monkeys tracked moving targets in contexts that set different priors for target speed. Before the onset of target motion, preparatory activity encodes and adapts in parallel with the behavioral adaptation of the prior. During the initiation of pursuit, FEF output encodes a maximum a posteriori estimate of target speed based on a reliability-weighted combination of the prior and sensory evidence. FEF responses during pursuit are sufficient both to adapt a prior that may be stored in FEF and, through known downstream pathways, to cause Bayesian-like behavior in pursuit.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41593-018-0233-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6312195PMC
October 2018

Multiple components in direction learning in smooth pursuit eye movements of monkeys.

J Neurophysiol 2018 10 1;120(4):2020-2035. Epub 2018 Aug 1.

Department of Neurobiology, Duke University School of Medicine , Durham, North Carolina.

We analyzed behavioral features of smooth pursuit eye movements to characterize the course of acquisition and expression of multiple neural components of motor learning. Monkeys tracked a target that began to move in an initial "pursuit" direction and suddenly, but predictably, changed direction after a fixed interval of 250 ms. As the trial is repeated, monkeys learn to make eye movements that predict the change in target direction. Quantitative analysis of the learned response revealed evidence for multiple, dynamic, parallel processes at work during learning. 1) The overall learning followed at least two trial courses: a fast component grew and saturated rapidly over tens of trials, and a slow component grew steadily over up to 1,000 trials. 2) The temporal specificity of the learned response within each trial was crude during the first 100 trials but then improved gradually over the remaining trials. 3) External influences on the gain of pursuit initiation modulate the expression but probably not the acquisition of learning. The gain of pursuit initiation and the expression of the learned response decreased in parallel, both gradually through a 1,000-trial learning block and immediately between learning trials with different gains in the initiation of pursuit. We conclude that at least two distinct neural mechanisms drive the acquisition of pursuit learning over 100 to 1,000 trials (3 to 30 min). Both mechanisms generate underlying memory traces that are modulated in relation to the gain of pursuit initiation before expression in the final motor output. NEW & NOTEWORTHY We show that cerebellum-dependent direction learning in smooth pursuit eye movements grows in at least two components over 1,100 behavioral learning repetitions. One component grows over tens of trials and the other over hundreds. Within trials, learned temporal specificity gradually improves over hundreds of trials. The expression of each learning component on a given trial can be modified by external factors that do not affect the underlying memory trace.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1152/jn.00261.2018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6230775PMC
October 2018

Modulation of Complex-Spike Duration and Probability during Cerebellar Motor Learning in Visually Guided Smooth-Pursuit Eye Movements of Monkeys.

eNeuro 2017 May-Jun;4(3). Epub 2017 Jul 10.

Department of Neurobiology, Duke University School of Medicine, Durham, NC 27110.

Activation of an inferior olivary neuron powerfully excites Purkinje cells via its climbing fiber input and triggers a characteristic high-frequency burst, known as the complex spike (CS). The theory of cerebellar learning postulates that the CS induces long-lasting depression of the strength of synapses from active parallel fibers onto Purkinje cells, and that synaptic depression leads to changes in behavior. Prior reports showed that a CS on one learning trial is linked to a properly timed depression of simple spikes on the subsequent trial, as well as a learned change in pursuit eye movement. Further, the duration of a CS is a graded instruction for single-trial plasticity and behavioral learning. We now show across multiple learning paradigms that both the probability and duration of CS responses are correlated with the magnitudes of neural and behavioral learning in awake behaving monkeys. When the direction of the instruction for learning repeatedly was in the same direction or alternated directions, the duration and probability of CS responses decreased over a learning block along with the magnitude of trial-over-trial neural learning. When the direction of the instruction was randomized, CS duration, CS probability, and neural and behavioral learning remained stable across time. In contrast to depression, potentiation of simple-spike firing rate for ON-direction learning instructions follows a longer time course and plays a larger role as depression wanes. Computational analysis provides a model that accounts fully for the detailed statistics of a complex set of data.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1523/ENEURO.0115-17.2017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5502376PMC
September 2018

Control of the strength of visual-motor transmission as the mechanism of rapid adaptation of priors for Bayesian inference in smooth pursuit eye movements.

J Neurophysiol 2017 08 7;118(2):1173-1189. Epub 2017 Jun 7.

Department of Neurobiology, Duke University School of Medicine, Durham, North Carolina

Bayesian inference provides a cogent account of how the brain combines sensory information with "priors" based on past experience to guide many behaviors, including smooth pursuit eye movements. We now demonstrate very rapid adaptation of the pursuit system's priors for target direction and speed. We go on to leverage that adaptation to outline possible neural mechanisms that could cause pursuit to show features consistent with Bayesian inference. Adaptation of the prior causes changes in the eye speed and direction at the initiation of pursuit. The adaptation appears after a single trial and accumulates over repeated exposure to a given history of target speeds and directions. The influence of the priors depends on the reliability of visual motion signals: priors are more effective against the visual motion signals provided by low-contrast vs. high-contrast targets. Adaptation of the direction prior generalizes to eye speed and vice versa, suggesting that both priors could be controlled by a single neural mechanism. We conclude that the pursuit system can learn the statistics of visual motion rapidly and use those statistics to guide future behavior. Furthermore, a model that adjusts the gain of visual-motor transmission predicts the effects of recent experience on pursuit direction and speed, as well as the specifics of the generalization between the priors for speed and direction. We suggest that Bayesian inference in pursuit behavior is implemented by distinctly non-Bayesian internal mechanisms that use the smooth eye movement region of the frontal eye fields to control of the gain of visual-motor transmission. Bayesian inference can account for the interaction between sensory data and past experience in many behaviors. Here, we show, using smooth pursuit eye movements, that the priors based on past experience can be adapted over a very short time frame. We also show that a single model based on direction-specific adaptation of the strength of visual-motor transmission can explain the implementation and adaptation of priors for both target direction and target speed.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1152/jn.00282.2017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5547260PMC
August 2017

Responses of Purkinje cells in the oculomotor vermis of monkeys during smooth pursuit eye movements and saccades: comparison with floccular complex.

J Neurophysiol 2017 08 17;118(2):986-1001. Epub 2017 May 17.

Department of Neurobiology, Duke University School of Medicine Durham, North Carolina

We recorded the responses of Purkinje cells in the oculomotor vermis during smooth pursuit and saccadic eye movements. Our goal was to characterize the responses in the vermis using approaches that would allow direct comparisons with responses of Purkinje cells in another cerebellar area for pursuit, the floccular complex. Simple-spike firing of vermis Purkinje cells is direction selective during both pursuit and saccades, but the preferred directions are sufficiently independent so that downstream circuits could decode signals to drive pursuit and saccades separately. Complex spikes also were direction selective during pursuit, and almost all Purkinje cells showed a peak in the probability of complex spikes during the initiation of pursuit in at least one direction. Unlike the floccular complex, the preferred directions for simple spikes and complex spikes were not opposite. The kinematics of smooth eye movement described the simple-spike responses of vermis Purkinje cells well. Sensitivities were similar to those in the floccular complex for eye position and considerably lower for eye velocity and acceleration. The kinematic relations were quite different for saccades vs. pursuit, supporting the idea that the contributions from the vermis to each kind of movement could contribute independently in downstream areas. Finally, neither the complex-spike nor the simple-spike responses of vermis Purkinje cells were appropriate to support direction learning in pursuit. Complex spikes were not triggered reliably by an instructive change in target direction; simple-spike responses showed very small amounts of learning. We conclude that the vermis plays a different role in pursuit eye movements compared with the floccular complex. The midline oculomotor cerebellum plays a different role in smooth pursuit eye movements compared with the lateral, floccular complex and appears to be much less involved in direction learning in pursuit. The output from the oculomotor vermis during pursuit lies along a null-axis for saccades and vice versa. Thus the vermis can play independent roles in the two kinds of eye movement.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1152/jn.00209.2017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5539460PMC
August 2017

Signal, Noise, and Variation in Neural and Sensory-Motor Latency.

Neuron 2016 Apr 10;90(1):165-76. Epub 2016 Mar 10.

Department of Neurobiology, Duke University School of Medicine, Durham, NC 27710, USA.

Analysis of the neural code for sensory-motor latency in smooth pursuit eye movements reveals general principles of neural variation and the specific origin of motor latency. The trial-by-trial variation in neural latency in MT comprises a shared component expressed as neuron-neuron latency correlations and an independent component that is local to each neuron. The independent component arises heavily from fluctuations in the underlying probability of spiking, with an unexpectedly small contribution from the stochastic nature of spiking itself. The shared component causes the latency of single-neuron responses in MT to be weakly predictive of the behavioral latency of pursuit. Neural latency deeper in the motor system is more strongly predictive of behavioral latency. A model reproduces both the variance of behavioral latency and the neuron-behavior latency correlations in MT if it includes realistic neural latency variation, neuron-neuron latency correlations in MT, and noisy gain control downstream of MT.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuron.2016.02.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4824642PMC
April 2016

Visual Guidance of Smooth Pursuit Eye Movements.

Annu Rev Vis Sci 2015 Nov 2;1:447-468. Epub 2015 Oct 2.

Department of Neurobiology, Duke University School of Medicine, Durham, North Carolina 27710; email:

Smooth pursuit eye movements provide a model system for studying how visual inputs are transformed into commands for accurate movement. The neural circuit for pursuit eye movements is largely known and has strong parallels to the circuits for many other movements. Here, we outline progress in defining the conceptual operations that are performed by the pursuit circuit and in aligning those functions with neural circuit mechanisms. We discuss how the visual motion that drives pursuit is represented in the visual cortex, and how the visuomotor circuits decode that representation to estimate target direction and speed and to create motor commands. We outline a modulatory influence called gain control that evaluates the reliability and value of visual inputs and programs appropriate motor activity. Future research on pursuit in nonhuman primates holds the potential to reveal, at an unprecedented level of detail, how visuomotor circuits create coordinated actions.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1146/annurev-vision-082114-035349DOI Listing
November 2015

The Neural Code for Motor Control in the Cerebellum and Oculomotor Brainstem.

eNeuro 2014 Nov-Dec;1(1). Epub 2014 Nov 12.

Howard Hughes Medical Institute and Department of Neurobiology, Duke University School of Medicine, Durham, North Carolina 27710.

A single extra spike makes a difference. Here, the size of the eye velocity in the initiation of smooth eye movements in the right panel depends on whether a cerebellar Purkinje cell discharges 3 (red), 4 (green), 5 (blue), or 6 (black) spikes in the 40-ms window indicated by the gray shading in the rasters on the left. Spike trains are rich in information that can be extracted to guide behaviors at millisecond time resolution or across longer time intervals. In sensory systems, the information usually is defined with respect to the stimulus. Especially in motor systems, however, it is equally critical to understand how spike trains predict behavior. Thus, our goal was to compare systematically spike trains in the oculomotor system with eye movement behavior on single movements. We analyzed the discharge of Purkinje cells in the floccular complex of the cerebellum, floccular target neurons in the brainstem, other vestibular neurons, and abducens neurons. We find that an extra spike in a brief analysis window predicts a substantial fraction of the trial-by-trial variation in the initiation of smooth pursuit eye movements. For Purkinje cells, a single extra spike in a 40 ms analysis window predicts, on average, 0.5 SDs of the variation in behavior. An optimal linear estimator predicts behavioral variation slightly better than do spike counts in brief windows. Simulations reveal that the ability of single spikes to predict a fraction of behavior also emerges from model spike trains that have the same statistics as the real spike trains, as long as they are driven by shared sensory inputs. We think that the shared sensory estimates in their inputs create correlations in neural spiking across time and across each population. As a result, one or a small number of spikes in a brief time interval can predict a substantial fraction of behavioral variation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1523/ENEURO.0004-14.2014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4596133PMC
October 2015

Interactions between target location and reward size modulate the rate of microsaccades in monkeys.

J Neurophysiol 2015 Nov 26;114(5):2616-24. Epub 2015 Aug 26.

Department of Neurobiology and Howard Hughes Medical Institute, Duke University, Durham, North Carolina.

We have studied how rewards modulate the occurrence of microsaccades by manipulating the size of an expected reward and the location of the cue that sets the expectations for future reward. We found an interaction between the size of the reward and the location of the cue. When monkeys fixated on a cue that signaled the size of future reward, the frequency of microsaccades was higher if the monkey expected a large vs. a small reward. When the cue was presented at a site in the visual field that was remote from the position of fixation, reward size had the opposite effect: the frequency of microsaccades was lower when the monkey was expecting a large reward. The strength of pursuit initiation also was affected by reward size and by the presence of microsaccades just before the onset of target motion. The gain of pursuit initiation increased with reward size and decreased when microsaccades occurred just before or after the onset of target motion. The effect of the reward size on pursuit initiation was much larger than any indirect effects reward might cause through modulation of the rate of microsaccades. We found only a weak relationship between microsaccade direction and the location of the exogenous cue relative to fixation position, even in experiments where the location of the cue indicated the direction of target motion. Our results indicate that the expectation of reward is a powerful modulator of the occurrence of microsaccades, perhaps through attentional mechanisms.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1152/jn.00401.2015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4643096PMC
November 2015

How and why neural and motor variation are related.

Curr Opin Neurobiol 2015 Aug 2;33:110-6. Epub 2015 Apr 2.

Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States.

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. Variation in sensory estimation is propagated through sensory-motor circuits and ultimately causes motor variation. The sensory origin of motor variation creates trial-by-trial correlations among the responses of neurons at each level of the sensory motor circuit, and between neural and behavioral responses. We suggest that motor variation is a compromise between multiple competing constraints. The brain strives for motor behavior that is 'good enough' in the face of constraints that tend to promote variation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.conb.2015.03.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4576830PMC
August 2015

Role of plasticity at different sites across the time course of cerebellar motor learning.

J Neurosci 2014 May;34(21):7077-90

Department of Neurobiology and Howard Hughes Medical Institute, Duke University School of Medicine, Durham, North Carolina 27110.

Learning comprises multiple components that probably involve cellular and synaptic plasticity at multiple sites. Different neural sites may play their largest roles at different times during behavioral learning. We have used motor learning in smooth pursuit eye movements of monkeys to determine how and when different components of learning occur in a known cerebellar circuit. The earliest learning occurs when one climbing-fiber response to a learning instruction causes simple-spike firing rate of Purkinje cells in the floccular complex of the cerebellum to be depressed transiently at the time of the instruction on the next trial. Trial-over-trial depression and the associated learning in eye movement are forgotten in <6 s, but facilitate long-term behavioral learning over a time scale of ∼5 min. During 100 repetitions of a learning instruction, simple-spike firing rate becomes progressively depressed in Purkinje cells that receive climbing-fiber inputs from the instruction. In Purkinje cells that prefer the opposite direction of pursuit and therefore do not receive climbing-fiber inputs related to the instruction, simple-spike responses undergo potentiation, but more weakly and more slowly. Analysis of the relationship between the learned changes in simple-spike firing and learning in eye velocity suggests an orderly progression of plasticity: first on Purkinje cells with complex-spike (CS) responses to the instruction, later on Purkinje cells with CS responses to the opposite direction of instruction, and last in sites outside the cerebellar cortex. Climbing-fiber inputs appear to play a fast and primary, but nonexclusive, role in pursuit learning.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1523/JNEUROSCI.0017-14.2014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4028490PMC
May 2014

Purkinje-cell plasticity and cerebellar motor learning are graded by complex-spike duration.

Nature 2014 Jun 11;510(7506):529-32. Epub 2014 May 11.

1] Department of Neurobiology, Duke University, Durham, North Carolina 27710, USA [2] Howard Hughes Medical Institute, Duke University, Durham, North Carolina 27710, USA.

Behavioural learning is mediated by cellular plasticity, such as changes in the strength of synapses at specific sites in neural circuits. The theory of cerebellar motor learning relies on movement errors signalled by climbing-fibre inputs to cause long-term depression of synapses from parallel fibres to Purkinje cells. However, a recent review has called into question the widely held view that the climbing-fibre input is an 'all-or-none' event. In anaesthetized animals, there is wide variation in the duration of the complex spike (CS) caused in Purkinje cells by a climbing-fibre input. Furthermore, the amount of plasticity in Purkinje cells is graded according to the duration of electrically controlled bursts in climbing fibres. The duration of bursts depends on the 'state' of the inferior olive and therefore may be correlated across climbing fibres. Here we provide a potential functional context for these mechanisms during motor learning in behaving monkeys. The magnitudes of both plasticity and motor learning depend on the duration of the CS responses. Furthermore, the duration of CS responses seems to be a meaningful signal that is correlated across the Purkinje-cell population during motor learning. We suggest that during learning, longer bursts in climbing fibres lead to longer-duration CS responses in Purkinje cells, more calcium entry into Purkinje cells, larger synaptic depression, and stronger learning. The same graded impact of instructive signals for plasticity and learning might occur throughout the nervous system.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/nature13282DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4132823PMC
June 2014

Interaction of plasticity and circuit organization during the acquisition of cerebellum-dependent motor learning.

Elife 2013 Dec 31;2:e01574. Epub 2013 Dec 31.

Department of Neurobiology, Duke University School of Medicine, Durham, United States.

Motor learning occurs through interactions between the cerebellar circuit and cellular plasticity at different sites. Previous work has established plasticity in brain slices and suggested plausible sites of behavioral learning. We now reveal what actually happens in the cerebellum during short-term learning. We monitor the expression of plasticity in the simple-spike firing of cerebellar Purkinje cells during trial-over-trial learning in smooth pursuit eye movements of monkeys. Our findings imply that: 1) a single complex-spike response driven by one instruction for learning causes short-term plasticity in a Purkinje cell's mossy fiber/parallel-fiber input pathways; 2) complex-spike responses and simple-spike firing rate are correlated across the Purkinje cell population; and 3) simple-spike firing rate at the time of an instruction for learning modulates the probability of a complex-spike response, possibly through a disynaptic feedback pathway to the inferior olive. These mechanisms may participate in long-term motor learning. DOI: http://dx.doi.org/10.7554/eLife.01574.001.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.7554/eLife.01574DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3871706PMC
December 2013

Gamma synchrony predicts neuron-neuron correlations and correlations with motor behavior in extrastriate visual area MT.

J Neurosci 2013 Dec;33(50):19677-88

Howard Hughes Medical Institute and Department of Neurobiology, Duke University School of Medicine, Durham, North Carolina 27710.

Correlated variability of neuronal responses is an important factor in estimating sensory parameters from a population response. Large correlations among neurons reduce the effective size of a neural population and increase the variation of the estimates. They also allow the activity of one neuron to be informative about impending perceptual decisions or motor actions on single trials. In extrastriate visual area MT of the rhesus macaque, for example, some but not all neurons show nonzero "choice probabilities" for perceptual decisions or non-zero "MT-pursuit" correlations between the trial-by-trial variations in neural activity and smooth pursuit eye movements. To understand the functional implications of zero versus nonzero correlations between neural responses and impending perceptions or actions, we took advantage of prior observations that specific frequencies of local field potentials reflect the correlated activity of neurons. We found that the strength of the spike-field coherence of a neuron in the gamma-band frequency range is related to the size of its MT-pursuit correlations for eye direction, as well as to the size of the neuron-neuron correlations. Spike-field coherence predicts MT-pursuit correlations better for direction than for speed, perhaps because the topographic organization of direction preference in MT is more amenable to creating meaningful local field potentials. We suggest that the relationship between spiking and local-field potentials is stronger for neurons that have larger correlations with their neighbors; larger neuron-neuron correlations create stronger MT-pursuit correlations. Neurons that lack strong correlations with their neighbors also have weaker correlations with pursuit behavior, but still could drive pursuit strongly.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1523/JNEUROSCI.3478-13.2013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3858635PMC
December 2013

A framework for using signal, noise, and variation to determine whether the brain controls movement synergies or single muscles.

J Neurophysiol 2014 Feb 20;111(4):733-45. Epub 2013 Nov 20.

Department of Neurobiology and Howard Hughes Medical Institute, Duke University, Durham, North Carolina.

We have used an analysis of signal and variation in motor behavior to elucidate the organization of the cerebellar and brain stem circuits that control smooth pursuit eye movements. We recorded from the abducens nucleus and identified floccular target neurons (FTNs) and other, non-FTN vestibular neurons. First, we assessed neuron-behavior correlations, defined as the trial-by-trial correlation between the variation in neural firing and eye movement, in brain stem neurons. In agreement with prior data from the cerebellum, neuron-behavior correlations during pursuit initiation were large in all neurons. Second, we asked whether movement variation arises upstream from, in parallel to, or downstream from a given site of recording. We developed a model that highlighted two measures: the ratio of the SDs of neural firing rate and eye movement ("SDratio") and the neuron-behavior correlation. The relationship between these measures defines possible sources of variation. During pursuit initiation, SDratio was approximately equal to neuron-behavior correlation, meaning that the source of signal and variation is upstream from the brain stem. During steady-state pursuit, neuron-behavior correlation became somewhat smaller than SDratio for FTNs, meaning that some variation may arise downstream in the brain stem. The data contradicted the model's predictions for sources of variation in pathways that run parallel to the site of recording. Because signal and noise are tightly linked in motor control, we take the source of variation as a proxy for the source of signal, leading us to conclude that the brain controls movement synergies rather than single muscles for eye movements.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1152/jn.00510.2013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3921394PMC
February 2014

Sensory population decoding for visually guided movements.

Neuron 2013 Jul;79(1):167-79

Department of Physiology, University of California, San Francisco, San Francisco, CA 94143, USA.

We have used a new approach to study the neural decoding function that converts the population response in extrastriate area MT into estimates of target motion to drive smooth pursuit eye movement. Experiments reveal significant trial-by-trial correlations between the responses of MT neurons and the initiation of pursuit. The preponderance of significant correlations and the relatively low reduction in noise between MT and the behavioral output support the hypothesis of a sensory origin for at least some of the trial-by-trial variation in pursuit initiation. The finding of mainly positive MT-pursuit correlations, whether the target speed is faster or slower than the neuron's preferred speed, places strong constraints on the neural decoding computation. We propose that decoding is based on normalizing a weighted population vector of opponent motion responses; normalization comes from neurons uncorrelated with those used to compute the weighted population vector.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuron.2013.05.026DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3757094PMC
July 2013

Sound the alarm: fraud in neuroscience.

Cerebrum 2013 May 2;2013. Epub 2013 May 2.

We expect scientists to follow a code of honor and conduct and to report their research honestly and accurately, but so-called scientific misconduct, which includes plagiarism, faked data, and altered images, has led to a tenfold increase in the number of retractions over the past decade. Among the reasons for this troubling upsurge is increased competition for journal placement, grant money, and prestigious appointments. The solutions are not easy, but reform and greater vigilance is needed.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3704310PMC
May 2013

Control of the gain of visual-motor transmission occurs in visual coordinates for smooth pursuit eye movements.

J Neurosci 2013 May;33(22):9420-30

Howard Hughes Medical Institute, Department of Neurobiology, Duke University, Durham, North Carolina 27710-0001, USA.

Sensory inputs control motor behavior with a strength, or gain, that can be modulated according to the movement conditions. In smooth pursuit eye movements, the response to a brief perturbation of target motion is larger during pursuit of a moving target than during fixation of a stationary target. As a step toward identifying the locus and mechanism of gain modulation, we test whether it acts on signals that are in visual or motor coordinates. Monkeys tracked targets that moved at 15°/s in one of eight directions, including left, right, up, down, and the four oblique directions. In eight-ninths of the trials, the target underwent a brief perturbation that consisted of a single cycle of a 10 Hz sine wave of amplitude ±5°/s in one of the same eight directions. Even for oblique directions of baseline target motion, the magnitude of the eye velocity response to the perturbation was largest for a perturbation near the axis of target motion and smallest for a perturbation along the orthogonal axis. Computational modeling reveals that our data are reproduced when the strength of visual-motor transmission is modulated in sensory coordinates, and there is a static motor bias that favors horizontal eye movements. A network model shows how the output from the smooth eye movement region of the frontal eye fields (FEF(SEM)) could implement gain control by shifting the peak of a visual population response along the axes of preferred image speed and direction.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1523/JNEUROSCI.4846-12.2013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3705569PMC
May 2013

Diversity of neural responses in the brainstem during smooth pursuit eye movements constrains the circuit mechanisms of neural integration.

J Neurosci 2013 Apr;33(15):6633-47

Department of Physiology and W.M. Keck Foundation Center for Integrative Neuroscience, University of California, San Francisco, California 94143-0248, USA.

Neural integration converts transient events into sustained neural activity. In the smooth pursuit eye movement system, neural integration is required to convert cerebellar output into the sustained discharge of extraocular motoneurons. We recorded the expression of integration in the time-varying firing rates of cerebellar and brainstem neurons in the monkey during pursuit of step-ramp target motion. Electrical stimulation with single shocks in the cerebellum identified brainstem neurons that are monosynaptic targets of inhibition from the cerebellar floccular complex. They discharge in relation to eye acceleration, eye velocity, and eye position, with a stronger acceleration signal than found in most other brainstem neurons. The acceleration and velocity signals can be accounted for by opponent contributions from the two sides of the cerebellum, without integration; the position signal implies participation in the integrator. Other neurons in the vestibular nucleus show a wide range of blends of signals related to eye velocity and eye position, reflecting different stages of integration. Neurons in the abducens nucleus discharge homogeneously in relation mainly to eye position, and reflect almost perfect integration of the cerebellar outputs. Average responses of neural populations and the diverse individual responses of large samples of individual neurons are reproduced by a hierarchical neural circuit based on a model suggested the anatomy and physiology of the larval zebrafish brainstem. The model uses a combination of feedforward and feedback connections to support a neural circuit basis for integration in monkeys and other species.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1523/JNEUROSCI.3732-12.2013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3705570PMC
April 2013

Multitasking on the run.

Elife 2013 Mar 19;2:e00641. Epub 2013 Mar 19.

is at the Laboratory of Developmental Neurobiology , The Rockefeller University , New York , United States

Researchers combine genetics and imaging to reveal that individual granule cells in the cerebellum integrate sensory and motor information.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.7554/eLife.00641DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3601634PMC
March 2013

The interaction of bayesian priors and sensory data and its neural circuit implementation in visually guided movement.

J Neurosci 2012 Dec;32(49):17632-45

Howard Hughes Medical Institute, W.M. Keck Foundation Center for Integrative Neuroscience, Sloan-Swartz Foundation, University of California, San Francisco, San Francisco, California 94143, USA.

Sensory-motor behavior results from a complex interaction of noisy sensory data with priors based on recent experience. By varying the stimulus form and contrast for the initiation of smooth pursuit eye movements in monkeys, we show that visual motion inputs compete with two independent priors: one prior biases eye speed toward zero; the other prior attracts eye direction according to the past several days' history of target directions. The priors bias the speed and direction of the initiation of pursuit for the weak sensory data provided by the motion of a low-contrast sine wave grating. However, the priors have relatively little effect on pursuit speed and direction when the visual stimulus arises from the coherent motion of a high-contrast patch of dots. For any given stimulus form, the mean and variance of eye speed covary in the initiation of pursuit, as expected for signal-dependent noise. This relationship suggests that pursuit implements a trade-off between movement accuracy and variation, reducing both when the sensory signals are noisy. The tradeoff is implemented as a competition of sensory data and priors that follows the rules of Bayesian estimation. Computer simulations show that the priors can be understood as direction-specific control of the strength of visual-motor transmission, and can be implemented in a neural-network model that makes testable predictions about the population response in the smooth eye movement region of the frontal eye fields.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1523/JNEUROSCI.1163-12.2012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3527106PMC
December 2012

Circuit mechanisms revealed by spike-timing correlations in macaque area MT.

J Neurophysiol 2013 Feb 14;109(3):851-66. Epub 2012 Nov 14.

Dept. of Neuroscience, Univ. of Wisconsin, Madison, WI 53706.

We recorded simultaneously from pairs of motion-sensitive neurons in the middle temporal cortex (MT) of macaque monkeys and used cross-correlations in the timing of spikes between neurons to gain insights into cortical circuitry. We characterized the time course and stimulus dependency of the cross-correlogram (CCG) for each pair of neurons and of the auto-correlogram (ACG) of the individual neurons. For some neuron pairs, the CCG showed negative flanks that emerged next to the central peak during stimulus-driven responses. Similar negative flanks appeared in the ACG of many neurons. Negative flanks were most prevalent and deepest when the neurons were driven to high rates by visual stimuli that moved in the neurons' preferred directions. The temporal development of the negative flanks in the CCG coincided with a parallel, modest reduction of the noise correlation between the spike counts of the neurons. Computational analysis of a model cortical circuit suggested that negative flanks in the CCG arise from the excitation-triggered mutual cross-inhibition between pairs of excitatory neurons. Intracortical recurrent inhibition and afterhyperpolarization caused by intrinsic outward currents, such as the calcium-activated potassium current of small conductance, can both contribute to the negative flanks in the ACG. In the model circuit, stronger intracortical inhibition helped to maintain the temporal precision between the spike trains of pairs of neurons and led to weaker noise correlations. Our results suggest a neural circuit architecture that can leverage activity-dependent intracortical inhibition to adaptively modulate both the synchrony of spike timing and the correlations in response variability.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1152/jn.00775.2012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3567398PMC
February 2013

Role of the lateral intraparietal area in modulation of the strength of sensory-motor transmission for visually guided movements.

J Neurosci 2012 Jul;32(28):9745-54

Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, California 94143-0444, USA.

The lateral intraparietal area (LIP) has been implicated as a salience map for control of saccadic eye movements and visual attention. Here, we report evidence to link the encoding of saccades and saliency in LIP to modulation of several other sensory-motor behaviors in monkeys. In many LIP neurons, there was a significant trial-by-trial correlation between the firing rate just before a saccade and the postsaccadic or presaccadic pursuit eye velocity. Some neurons also showed trail-by-trial correlations of the firing rate of LIP neurons with the speed of "glissades" that occur at the end of saccades to stationary targets. LIP-pursuit correlations were spatially specific and were strong only when the target appeared in the receptive/movement field of the neuron under study. We suggest that LIP is a component of a salience representation that modulates the strength of visual-motor transmission for pursuit, and that may play a similar role for many movements, beyond its traditional roles in guiding saccadic eye movements and localizing attention.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1523/JNEUROSCI.0269-12.2012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3415687PMC
July 2012