A sensory-motor decoder that transforms neural responses in extrastriate area MT into smooth pursuit eye movements.

Visual motion drives smooth pursuit eye movements through a sensory-motor decoder that uses multiple parallel neural pathways to transform the population response in extrastriate area MT into movement. We evaluated the decoder by challenging pursuit in monkeys with reduced motion reliability created by reducing coherence of motion in patches of dots. Our strategy was to determine how reduced dot coherence changes the population response in MT. We then predicted the properties of a decoder that transforms the MT population response into dot coherence-induced deficits in the initiation of pursuit and steady-state tracking. During pursuit initiation, decreased dot coherence reduces MT population response amplitude without changing the preferred speed at its peak. The successful decoder reproduces the measured eye movements by multiplication of 1) the estimate of target speed from the peak of the population response with 2) visual-motor gain based on the amplitude of the population response. During steady-state tracking, the decoder that worked for pursuit initiation failed to reproduce the paradox that steady-state eye speeds do not accelerate to the target speed despite persistent image motion. It predicted eye acceleration to target speed even when monkeys' eye speeds were steady at well below the target speed. To account for the effect of dot coherence on steady-state eye speed, we postulate that the decoder uses sensory-motor gain to modulate the eye velocity positive feedback that normally sustains perfect steady-state tracking. Then, poor steady-state tracking persists because of balance between eye deceleration caused by low positive feedback gain and acceleration driven by MT.NEW & NOTEWORTHY By challenging a sensory-motor system with degraded sensory stimuli, we reveal how the sensory-motor decoder transforms the population response in extrastriate area MT into commands for the initiation and steady-state behavior of smooth pursuit eye movements. Conclusions are based on measuring population responses in MT for multiple target speeds and different levels of motion reliability and evaluating a decoder with a biologically motivated architecture to determine the decoder properties that create the measured eye movements.

Toward a Biomimetic Neural Circuit Model of Sensory-Motor Processing.

Computational models have been a mainstay of research on smooth pursuit eye movements in monkeys. Pursuit is a sensory-motor system that is driven by the visual motion of small targets. It creates a smooth eye movement that accelerates up to target speed and tracks the moving target essentially perfectly. In this review of my laboratory's research, I trace the development of computational models of pursuit eye movements from the early control-theory models to the most recent neural circuit models. I outline a combined experimental and computational plan to move the models to the next level. Finally, I explain why research on nonhuman primates is so critical to the development of the neural circuit models I think we need.

Evaluation and resolution of many challenges of neural spike sorting: a new sorter.

We evaluate existing spike sorters and present a new one that resolves many sorting challenges. The new sorter, called "full binary pursuit" or FBP, comprises multiple steps. First, it thresholds and clusters to identify the waveforms of all unique neurons in the recording. Second, it uses greedy binary pursuit to optimally assign all the spike events in the original voltages to separable neurons. Third, it resolves spike events that are described more accurately as the superposition of spikes from two other neurons. Fourth, it resolves situations where the recorded neurons drift in amplitude or across electrode contacts during a long recording session. Comparison with other sorters on ground-truth data sets reveals many of the failure modes of spike sorting. We examine overall spike sorter performance in ground-truth data sets and suggest postsorting analyses that can improve the veracity of neural analyses by minimizing the intrusion of failure modes into analysis and interpretation of neural data. Our analysis reveals the tradeoff between the number of channels a sorter can process, speed of sorting, and some of the failure modes of spike sorting. FBP works best on data from 32 channels or fewer. It trades speed and number of channels for avoidance of specific failure modes that would be challenges for some use cases. We conclude that all spike sorting algorithms studied have advantages and shortcomings, and the appropriate use of a spike sorter requires a detailed assessment of the data being sorted and the experimental goals for analyses.NEW & NOTEWORTHY Electrophysiological recordings from multiple neurons across multiple channels pose great difficulty for spike sorting of single neurons. We propose methods that improve the ability to determine the number of individual neurons present in a recording and resolve near-simultaneous spike events from single neurons. We use ground-truth data sets to demonstrate the pros and cons of several current sorting algorithms and suggest strategies for determining the accuracy of spike sorting when ground-truth data are not available.

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

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.

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

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.NEW & NOTEWORTHY 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.

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

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.

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

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.

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

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.NEW & NOTEWORTHY 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.

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

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.NEW & NOTEWORTHY 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.

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

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.

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

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.

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

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.

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

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.

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

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.

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

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.

A deep-learning strategy to identify cell types across species from high-density extracellular recordings.

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but don't reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifier's predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously-recorded cell types during behavior.

Reward action in the initiation of smooth pursuit eye movements.

Reward has a powerful influence on motor behavior. To probe how and where reward systems alter motor behavior, we studied smooth pursuit eye movements in monkeys trained to associate the color of a visual cue with the size of the reward to be issued at the end of the target motion. When the tracking task presented two different colored targets that moved orthogonally, monkeys biased the initiation of pursuit toward the direction of motion of the target that led to larger reward. The bias was larger than expected given the modest effects of reward size on tracking of single targets. Experiments with three different reward sizes suggested that the bias afforded a given target depends mainly on the size of the larger reward. To analyze the effect of reward on directional learning in pursuit, monkeys tracked a single moving target that changed direction 250 ms after the onset of motion. Expectation of a larger reward led to a larger learned eye movement during the acquisition of the learned response and during subsequent probes of what had been learned, implying that reward influenced the expression rather than the acquisition of learning. The specific effects of reward size on learning and two-target stimuli imply that the site of reward modulation is at a level where multiple target motions compete for control of eye movement, downstream from sensory processing and learning and upstream from final motor processing.

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

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.

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

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.

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

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.