Neural responses are typically characterized by computing the mean firing rate. Yet response variability can exist across trials. Many studies have examined the impact of a stimulus on the mean response, yet few have examined the impact on response variability. We measured neural variability in 13 extracellularly-recorded datasets and one intracellularly-recorded dataset from 7 areas spanning the four cortical lobes. In every case, stimulus onset caused a decline in neural variability. This occurred even when the stimulus produced little change in mean firing rate. The variability decline was observable in membrane potential recordings, in the spiking of individual neurons, and in correlated spiking variability measured with implanted 96-electrode arrays. The variability decline was observed for all stimuli tested, regardless of whether the animal was awake, behaving, or anaesthetized. This widespread variability decline suggests a rather general property of cortex: that its state is stabilized by an input.

Comparison of two seemingly quite different behaviors yields a surprisingly consistent picture of the role of the cerebellum in motor learning. Behavioral and physiological data about classical conditioning of the eyelid response and motor learning in the vestibulo-ocular reflex suggests that (i) plasticity is distributed between the cerebellar cortex and the deep cerebellar nuclei; (ii) the cerebellar cortex plays a special role in learning the timing of movement; and (iii) the cerebellar cortex guides learning in the deep nuclei, which may allow learning to be transferred from the cortex to the deep nuclei. Because many of the similarities in the data from the two systems typify general features of cerebellar organization, the cerebellar mechanisms of learning in these two systems may represent principles that apply to many motor systems.

Tuning for speed is one key feature of motion-selective neurons in the middle temporal visual area of the macaque cortex (MT, or V5). The present paper asks whether speed is coded in a way that is invariant to the shape of the moving stimulus, and if so, how. When tested with single sine-wave gratings of different spatial and temporal frequencies, MT neurons show a continuum in the degree to which preferred speed depends on spatial frequency. There is some dependence in 75% of MT neurons, and the other 25% maintain speed tuning despite changes in spatial frequency. When tested with stimuli constructed by adding two superimposed sine-wave gratings, the preferred speed of MT neurons becomes less dependent on spatial frequency. Analysis of these responses reveals a speed-tuning nonlinearity that selectively enhances the responses of the neuron when multiple spatial frequencies are present and moving at the same speed. Consistent with the presence of the nonlinearity, MT neurons show speed tuning that is close to form-invariant when the moving stimuli comprise square-wave gratings, which contain multiple spatial frequencies moving at the same speed. We conclude that the neural circuitry in and before MT makes no explicit attempt to render MT neurons speed-tuned for sine-wave gratings, which do not occur in natural scenes. Instead, MT neurons derive form-invariant speed tuning in a way that takes advantage of the multiple spatial frequencies that comprise moving objects in natural scenes.

Suppose that the variability in our movements is caused not by noise in the motor system itself, nor by fluctuations in our intentions or plans, but rather by errors in our sensory estimates of the external parameters that define the appropriate action. For tasks in which precision is at a premium, performance would be optimal if no noise were added in movement planning and execution: motor output would be as accurate as possible given the quality of sensory inputs. Here we use visually guided smooth-pursuit eye movements in primates as a testing ground for this notion of optimality. In response to repeated presentations of identical target motions, nearly 92% of the variance in eye trajectory can be accounted for as a consequence of errors in sensory estimates of the speed, direction and timing of target motion, plus a small background noise that is observed both during eye movements and during fixations. The magnitudes of the inferred sensory errors agree with the observed thresholds for sensory discrimination by perceptual systems, suggesting that the very different neural processes of perception and action are limited by the same sources of noise.

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. Although many elements of this hypothesis have been supported by prior experiments, the links between complex spikes, simple spike plasticity, and behavior have not yet been examined simultaneously during the learning process. We now pioneer a trial-by-trial analysis of Purkinje cell responses in awake-behaving monkeys, with results that strongly favor a causal role for complex spikes in the induction of cerebellar plasticity during a simple motor learning task. We show that the presence of a complex spike on one learning trial is linked to a substantial depression of simple spike responses on the subsequent trial, at a time when behavioral learning is expressed.

We asked whether the dynamics of target motion are represented in visual area MT and how information about image velocity and acceleration might be extracted from the population responses in area MT for use in motor control. The time course of MT neuron responses was recorded in anesthetized macaque monkeys during target motions that covered the range of dynamics normally seen during smooth pursuit eye movements. When the target motion provided steps of target speed, MT neurons showed a continuum from purely tonic responses to those with large transient pulses of firing at the onset of motion. Cells with large transient responses for steps of target speed also had larger responses for smooth accelerations than for decelerations through the same range of target speeds. Condition-test experiments with pairs of 64 msec pulses of target speed revealed response attenuation at short interpulse intervals in cells with large transient responses. For sinusoidal modulation of target speed, MT neuron responses were strongly modulated for frequencies up to, but not higher than, 8 Hz. The phase of the responses was consistent with a 90 msec time delay between target velocity and firing rate. We created a model that reproduced the dynamic responses of MT cells using divisive gain control, used the model to visualize the population response in MT to individual stimuli, and devised weighted-averaging computations to reconstruct target speed and acceleration from the population response. Target speed could be reconstructed if each neuron's output was weighted according to its preferred speed. Target acceleration could be reconstructed if each neuron's output was weighted according to the product of preferred speed and a measure of the size of its transient response.

Frontotemporal lobar degeneration (FTLD) often overlaps clinically with corticobasal syndrome (CBS) and progressive supranuclear palsy (PSP), both of which have prominent eye movement abnormalities. To investigate the ability of oculomotor performance to differentiate between FTLD, Alzheimer's disease, CBS and PSP, saccades and smooth pursuit were measured in three FTLD subtypes, including 24 individuals with frontotemporal dementia (FTD), 19 with semantic dementia (SD) and six with progressive non-fluent aphasia (PA), as compared to 28 individuals with Alzheimer's disease, 15 with CBS, 10 with PSP and 27 control subjects. Different combinations of oculomotor abnormalities were identified in all clinical syndromes except for SD, which had oculomotor performance that was indistinguishable from age-matched controls. Only PSP patients displayed abnormalities in saccade velocity, whereas abnormalities in saccade gain were observed in PSP > CBS > Alzheimer's disease subjects. All patient groups except those with SD were impaired on the anti-saccade task, however only the FTLD subjects and not Alzheimer's disease, CBS or PSP groups, were able to spontaneously self-correct anti-saccade errors as well as controls. Receiver operating characteristic statistics demonstrated that oculomotor findings were superior to neuropsychological tests in differentiating PSP from other disorders, and comparable to neuropsychological tests in differentiating the other patient groups. These data suggest that oculomotor assessment may aid in the diagnosis of FTLD and related disorders.

Behavioral 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 learning1,2,3 relies on movement errors signaled by climbing-fiber inputs to cause long-term depression of synapses from parallel fibers to Purkinje cells4,5. Yet, a recent review6 has called into question the widely-held view that the climbing fiber input is an “all-or-none” event. In anesthetized animals, there is wide variation in the duration of the complex-spike (CS) caused in Purkinje cells by a climbing fiber input7. Further, the duration of electrically-controlled bursts in climbing fibers grades the amount of plasticity in Purkinje cells8,9. The duration of bursts depends on the “state” of the inferior olive and therefore could be correlated across climbing fibers8,10. 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. Further, the duration of CS responses appears 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 fibers 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 could occur throughout the nervous system.