Javier F. Medina

Computer simulation of cerebellar information processing.

Although many functions have been ascribed to the cerebellum, the uniformity of its synaptic organization suggests that a single, characteristic computation may be common to all. Computer simulations are useful in examining this cerebellar computation, as they inherently address function at the level of information processing. Progress is facilitated by factors that make the cerebellum particularly amenable to such analysis. We review progress from two contrasting approaches. Top-down simulations begin with hypotheses about computational mechanisms and then ask how such mechanisms might operate within the cerebellum. Bottom-up simulations attempt to build a representation of the cerebellum that reflects known cellular and synaptic components as accurately as possible. We describe recent advances from these two approaches that are leading to an understanding of what information the cerebellum processes and how its neurons and synapses accomplish this task.

Inhibition of climbing fibres is a signal for the extinction of conditioned eyelid responses

A fundamental tenet of cerebellar learning theories asserts that climbing fibre afferents from the inferior olive provide a teaching signal that promotes the gradual adaptation of movements. Data from several forms of motor learning provide support for this tenet. In pavlovian eyelid conditioning, for example, where a tone is repeatedly paired with a reinforcing unconditioned stimulus like periorbital stimulation, the unconditioned stimulus promotes acquisition of conditioned eyelid responses by activating climbing fibres. Climbing fibre activity elicited by an unconditioned stimulus is inhibited during the expression of conditioned responses—consistent with the inhibitory projection from the cerebellum to inferior olive. Here, we show that inhibition of climbing fibres serves as a teaching signal for extinction, where learning not to respond is signalled by presenting a tone without the unconditioned stimulus. We used reversible infusion of synaptic receptor antagonists to show that blocking inhibitory input to the climbing fibres prevents extinction of the conditioned response, whereas blocking excitatory input induces extinction. These results, combined with analysis of climbing fibre activity in a computer simulation of the cerebellar–olivary system, suggest that transient inhibition of climbing fibres below their background level is the signal that drives extinction.

Parallels between cerebellum- and amygdala-dependent conditioning

Recent evidence from cerebellum-dependent motor learning and amygdala-dependent fear conditioning indicates that, despite being mediated by different brain systems, these forms of learning might use a similar sequence of events to form new memories. In each case, learning seems to induce changes in two different groups of neurons. Changes in the first class of cells are induced very rapidly during the initial stages of learning, whereas changes in the second class of cells develop more slowly and are resistant to extinction. So, anatomically distinct cell populations might contribute differentially to the initial encoding and the long-term storage of memory in these two systems.

Links from complex spikes to local plasticity and motor learning in the cerebellum of awake-behaving monkeys

The hypothesis of cerebellar learning proposes that complex spikes in Purkinje cells engage mechanisms of plasticity in the cerebellar cortex; in turn, changes in the cerebellum depress the simple-spike response of Purkinje cells to a given stimulus and cause the adaptive modification of a motor behavior. Many elements of this hypothesis have been supported by prior experiments, and correlations have been found between complex spikes, simple-spike plasticity and behavior during the learning process. We carried out a trial-by-trial analysis of Purkinje cell responses in awake-behaving monkeys and found evidence for a causal role for complex spikes in the induction of cerebellar plasticity during a simple motor learning task. We found that the presence of a complex spike on one learning trial was linked to a substantial depression of simple-spike responses on the subsequent trial, at a time when behavioral learning was expressed.The hypothesis of cerebellar learning proposes that complex spikes in Purkinje cells engage mechanisms of plasticity in the cerebellar cortex; in turn, changes in the cerebellum depress the simple-spike response of Purkinje cells to a given stimulus and cause the adaptive modification of a motor behavior. Many elements of this hypothesis have been supported by prior experiments, and correlations have been found [corrected] between complex spikes, simple-spike plasticity and behavior [corrected] during the learning process. We carried out a trial-by-trial analysis of Purkinje cell responses in awake-behaving monkeys and found evidence for a causal role for complex spikes in the induction of cerebellar plasticity during a simple motor learning task. We found that the presence of a complex spike on one learning trial was linked to a substantial depression of simple-spike responses on the subsequent trial, at a time when behavioral learning was expressed.

Learning-Induced Plasticity in Deep Cerebellar Nucleus

Evidence that cerebellar learning involves more than one site of plasticity comes from, in part, pavlovian eyelid conditioning, where disconnecting the cerebellar cortex abolishes one component of learning, response timing, but spares the expression of abnormally timed short-latency responses (SLRs). Here, we provide evidence that SLRs unmasked by cerebellar cortex lesions are mediated by an associative form of learning-induced plasticity in the anterior interpositus nucleus (AIN) of the cerebellum. We used pharmacological inactivation and/or electrical microstimulation of various sites afferent and efferent to the AIN to systematically eliminate alternative candidate sites of plasticity upstream or downstream from this structure. Collectively, the results suggest that cerebellar learning is mediated in part by plasticity in target nuclei downstream of the cerebellar cortex. These data demonstrate an instance in which an aspect of associative learning, SLRs, can be used as an index of plasticity at a specific site in the brain.

Precise Control of Movement Kinematics by Optogenetic Inhibition of Purkinje Cell Activity

Purkinje cells (PCs) of the cerebellar cortex are necessary for controlling movement with precision, but a mechanistic explanation of how the activity of these inhibitory neurons regulates motor output is still lacking. We used an optogenetic approach in awake mice to show for the first time that transiently suppressing spontaneous activity in a population of PCs is sufficient to cause discrete movements that can be systematically modulated in size, speed, and timing depending on how much and how long PC firing is suppressed. We further demonstrate that this fine control of movement kinematics is mediated by a graded disinhibition of target neurons in the deep cerebellar nuclei. Our results prove a long-standing model of cerebellar function and provide the first demonstration that suppression of inhibitory signals can act as a powerful mechanism for the precise control of behavior.

Variation, Signal, and Noise in Cerebellar Sensory–Motor Processing for Smooth-Pursuit Eye Movements

Neural responses are variable, yet motor performance can be quite precise. To ask how neural signal and noise are processed in the brain during sensory–motor behavior, we have evaluated the trial-by-trial variation of Purkinje cell (PC) activity in the floccular complex of the cerebellum, an intermediate stage in the neural circuit for smooth-pursuit eye movements. We find strong correlations between small trial-by-trial variations in the simple spike activity of individual PCs and the eye movements at the initiation of pursuit. The correlation is lower but still present during steady-state pursuit. Recordings from a few pairs of PCs verified the predictions of a model of the PC population, that there is a transition from highly covariant PC activity during movement initiation to more independent activity later on. Application to the data of a theoretical and computational analysis suggests that variation in pursuit initiation arises mostly from variation in visual motion signals that provide common inputs to the PC population. Variation in eye movement during steady-state pursuit can be attributed primarily to signal-dependent motor noise that arises downstream from PCs.

The Representation of Time for Motor Learning

We have identified factors that control precise motor timing by studying learning in smooth pursuit eye movements. Monkeys tracked a target that moved horizontally for a fixed time interval before changing direction through the addition of a vertical component of motion. After repeated presentations of the same target trajectory, infrequent probe trials of purely horizontal target motion evoked a vertical eye movement around the time when the change in target direction would have occurred. The pursuit system timed the vertical eye movement by keeping track of the duration of horizontal target motion and by measuring the distance the target traveled before changing direction, but not by learning the position in space where the target changed direction. We conclude that high temporal precision in motor output relies on multiple signals whose contributions to timing vary according to task requirements.

Encoding and Decoding of Learned Smooth-Pursuit Eye Movements in the Floccular Complex of the Monkey Cerebellum

We recorded the simple-spike (SS) firing of Purkinje cells (PCs) in the floccular complex both during normal pursuit caused by step-ramp target motions and after learning induced by a consistently timed change in the direction of target motion. The encoding of eye movement by the SS firing rate of individual PCs was described by a linear regression model, in which the firing rate is a sum of weighted components related to eye acceleration, velocity, and position. Although the model fit the data well for individual conditions, the regression coefficients for the learned component of firing often differed substantially from those for normal pursuit of step-ramp target motion. We suggest that the different encoding of learned versus normal pursuit responses in individual PCs reflects different amounts of learning in their inputs. The decoded output from the floccular complex, estimated by averaging responses across the population of PCs, also was fitted by the regression model. Regression coefficients were equal for the two conditions for on-direction pursuit, but differed for off-direction target motion. We conclude that the average output from the population of floccular PCs provides some, but not all, of the neural signals that drive the learned component of pursuit and that plasticity outside of the flocculus makes an important contribution.

The multiple roles of Purkinje cells in sensori-motor calibration: to predict, teach and command

Neurophysiological recordings in the cerebellar cortex of awake-behaving animals are revolutionizing the way we think about the role of Purkinje cells in sensori-motor calibration. Early theorists suggested that if a movement became miscalibrated, Purkinje cell output would be changed to adjust the motor command and restore good performance. The finding that Purkinje cell activity changed in many sensori-motor calibration tasks was taken as strong support for this hypothesis. Based on more recent data, however, it has been suggested that changes in Purkinje cell activity do not contribute to the motor command directly; instead, they are used either as a teaching signal, or to predict the altered kinematics of the movement after calibration has taken place. I will argue that these roles are not mutually exclusive, and that Purkinje cells may contribute to command generation, teaching, and prediction at different times during sensori-motor calibration.