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.

Rate versus synchrony codes for cerebellar control of motor behavior.

Information transmission between neural populations could occur through either coordinated changes in firing rates or the precise transmission of spike timing. We investigate the code for information transmission from a part of the cerebellar cortex that is crucial for the accurate execution of a quantifiable motor behavior. Simultaneous recordings from Purkinje cell pairs in the cerebellum of rhesus macaques reveal how these cells coordinate their activity to drive smooth pursuit eye movements. Purkinje cells show millisecond-scale coordination of spikes (synchrony), but the level of synchrony is small and insufficient to impact the firing of downstream vestibular nucleus neurons. Analysis of previous metrics that purported to reveal Purkinje cell synchrony demonstrates that these metrics conflate changes in firing rate and neuron-neuron covariance. We conclude that the output of the cerebellar cortex uses primarily a rate rather than a synchrony code to drive the activity of downstream neurons and thus control motor behavior.

Rate versus synchrony codes for cerebellar control of motor behavior.

Control of movement requires the coordination of multiple brain areas, each containing populations of neurons that receive inputs, process these inputs via recurrent dynamics, and then relay the processed information to downstream populations. Information transmission between neural populations could occur through either coordinated changes in firing rates or the precise transmission of spike timing. We investigate the nature of the code for transmission of signals to downstream areas from a part of the cerebellar cortex that is crucial for the accurate execution of a quantifiable motor behavior. Simultaneous recordings from Purkinje cell pairs in the cerebellar flocculus of rhesus macaques revealed how these cells coordinate their activity to drive smooth pursuit eye movements. Purkinje cells show millisecond-scale coordination of spikes (synchrony), but the level of synchrony is small and likely insufficient to impact the firing of downstream neurons in the vestibular nucleus. Further, analysis of previous metrics for assaying Purkinje cell synchrony demonstrates that these metrics conflate changes in firing rate and neuron-neuron covariance. We conclude that the output of the cerebellar cortex uses primarily a rate code rather than synchrony code to drive activity of downstream neurons and thus control motor behavior. Impact statement: Information transmission in the brain can occur via changes in firing rate or via the precise timing of spikes. Simultaneous recordings from pairs of Purkinje cells in the floccular complex reveals that information transmission out of the cerebellar cortex relies almost exclusively on changes in firing rates rather than millisecond-scale coordination of spike timing across the Purkinje cell population.

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.

An implicit memory of errors limits human sensorimotor adaptation.

During extended motor adaptation, learning appears to saturate despite persistence of residual errors. This adaptation limit is not fixed but varies with perturbation variance; when variance is high, residual errors become larger. These changes in total adaptation could relate to either implicit or explicit learning systems. Here, we found that when adaptation relied solely on the explicit system, residual errors disappeared and learning was unaltered by perturbation variability. In contrast, when learning depended entirely, or in part, on implicit learning, residual errors reappeared. Total implicit adaptation decreased in the high-variance environment due to changes in error sensitivity, not in forgetting. These observations suggest a model in which the implicit system becomes more sensitive to errors when they occur in a consistent direction. Thus, residual errors in motor adaptation are at least in part caused by an implicit learning system that modulates its error sensitivity in response to the consistency of past errors.

Principles of operation of a cerebellar learning circuit.

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.

The human brain can do many things, from reading and remembering the words written on a page to adapting and improving movements. When a movement misses its goal, the strength of the connections between cells in a part of the brain known as the cerebellum changes. The cerebellum is important for coordinating movements, including eye movements. When the connections between the cells in the cerebellum ­ known as neurons ­ strengthen or weaken, the cerebellum changes how it will respond in the future, leading to more accurate movements. However, the speed of the changes in the connections and how the connections between different neurons evolve and coordinate were unknown. Herzfeld et al. have now combined eye-tracking studies in monkeys with computer modeling based on what is known about the neural circuits in the cerebellum to learn more about the changes in these connections. Monkeys watched a moving target that would abruptly change direction. In the next movement, the eye-tracking equipment monitored how well the monkey's eyes anticipated the unexpected change in the target's direction ­ a form of motor learning. Using the experimental data, Herzfeld et al. produced a model that outlines general principles of how the cerebellum might manage this process. The model suggested that neurons in one region in the cerebellum, known as Purkinje cells, learn from mistakes quickly, but have poor long-term retention. If the movement is repeated, Purkinje cells teach another area of the cerebellum, the cerebellar nucleus, which takes longer to learn but has much better retention. Although these findings are based on a simple motor learning task, they are the first step to understanding how the brain forms memories and how we might learn more complex behaviors.

Behavioral training of marmosets and electrophysiological recording from the cerebellum.

The common marmoset (Callithrix jacchus) is a promising new model for study of neurophysiological basis of behavior in primates. Like other primates, it relies on saccadic eye movements to monitor and explore its environment. Previous reports have demonstrated some success in training marmosets to produce goal-directed actions in the laboratory. However, the number of trials per session has been relatively small, thus limiting the utility of marmosets as a model for behavioral and neurophysiological studies. In this article, we report the results of a series of new behavioral training and neurophysiological protocols aimed at increasing the number of trials per session while recording from the cerebellum. To improve the training efficacy, we designed a precisely calibrated food regulation regime that motivates the subjects to perform saccade tasks, resulting in ~1,000 reward-driven trials on a daily basis. We then developed a multichannel recording system that uses imaging to target a desired region of the cerebellum, allowing for simultaneous isolation of multiple Purkinje cells in the vermis. In this report, we describe 1) the design and surgical implantation of a computer tomography (CT)-guided, subject-specific head post, 2) the design of a CT- and MRI-guided alignment tool for trajectory guidance of electrodes mounted on an absolute encoder microdrive, 3) development of a protocol for behavioral training of subjects, and 4) simultaneous recordings from pairs of Purkinje cells during a saccade task.NEW & NOTEWORTHY Marmosets present the opportunity to investigate genetically based neurological disease in primates, in particular, diseases that affect social behaviors, vocal communication, and eye movements. All of these behaviors depend on the integrity of the cerebellum. We present training methods that better motivate the subjects, allowing for improved performance, and we also present electrophysiological techniques that precisely target the subject's cerebellum, allowing for simultaneous isolation of multiple Purkinje cells.

Reward Prediction Error Modulates Saccade Vigor.

Movement vigor, defined as the reciprocal of the latency from availability of reward to its acquisition, changes with reward magnitude: movements exhibit shorter reaction time and increased velocity when they are directed toward more rewarding stimuli. This invigoration may be due to release of dopamine before movement onset, which has been shown to be modulated by events that signal reward prediction error (RPE). Here, we generated an RPE event in the milliseconds before movement onset and tested whether there was a relationship between RPE and vigor. Human subjects (both sexes) made saccades toward an image. During execution of the primary saccade, we probabilistically changed the position and content of that image, encouraging a secondary saccade. On some trials, the content of the secondary image was more valuable than the first image, resulting in a positive RPE (+RPE) event that preceded the secondary saccade. On other trials, this content was less valuable (-RPE event). We found that reaction time of the secondary saccade was affected in an orderly fashion by the magnitude and direction of the preceding RPE event: the most vigorous saccades followed the largest +RPE, whereas the least vigorous saccades followed the largest -RPE. Presence of the secondary saccade indicated that the primary saccade had experienced a movement error, inducing trial-to-trial adaptation. However, this learning from movement error was not modulated by the RPE event. The data suggest that RPE events, which are thought to transiently alter the release of dopamine, modulate the vigor of the ensuing movement.SIGNIFICANCE STATEMENT Does dopamine release in response to a stimulus serve to invigorate the ensuing movement? To test this hypothesis, we relied on the fact that reward prediction error (RPE) is a strong modulator of dopamine. Our innovation was a task in which an RPE event occurred precisely before onset of a stimulus-driven movement. We probabilistically produced a combination of large or small, negative or positive RPE events and observed that saccade vigor carried a robust signature of the preceding RPE event: high vigor saccades followed +RPE events, whereas low vigor saccades followed -RPE events. This suggests that in humans, vigor is partly controlled through release of dopamine in the moments before onset of a movement.

Movement vigor as a traitlike attribute of individuality.

A common aspect of individuality is our subjective preferences in evaluation of reward and effort. The neural circuits that evaluate these commodities influence circuits that control our movements, raising the possibility that vigor differences between individuals may also be a trait of individuality, reflecting a willingness to expend effort. In contrast, classic theories in motor control suggest that vigor differences reflect a speed-accuracy trade-off, predicting that those who move fast are sacrificing accuracy for speed. Here we tested these contrasting hypotheses. We measured motion of the eyes, head, and arm in healthy humans during various elementary movements (saccades, head-free gaze shifts, and reaching). For each person we characterized their vigor, i.e., the speed with which they moved a body part (peak velocity) with respect to the population mean. Some moved with low vigor, while others moved with high vigor. Those with high vigor tended to react sooner to a visual stimulus, moving both their eyes and arm with a shorter reaction time. Arm and head vigor were tightly linked: individuals who moved their head with high vigor also moved their arm with high vigor. However, eye vigor did not correspond strongly with arm or head vigor. In all modalities, vigor had no impact on end-point accuracy, demonstrating that differences in vigor were not due to a speed-accuracy trade-off. Our results suggest that movement vigor may be a trait of individuality, not reflecting a willingness to accept inaccuracy but demonstrating a propensity to expend effort. NEW & NOTEWORTHY A common aspect of individuality is how we evaluate economic variables like reward and effort. This valuation affects not only decision making but also motor control, raising the possibility that vigor may be distinct between individuals but conserved across movements within an individual. Here we report conservation of vigor across elementary skeletal movements, but not eye movements, raising the possibility that the individuality of our movements may be driven by a common neural mechanism of effort evaluation across modalities of skeletal motor control.

Encoding of error and learning to correct that error by the Purkinje cells of the cerebellum.

The primary output cells of the cerebellar cortex, Purkinje cells, make kinematic predictions about ongoing movements via high-frequency simple spikes, but receive sensory error information about that movement via low-frequency complex spikes (CS). How is the vector space of sensory errors encoded by this low-frequency signal? Here we measured Purkinje cell activity in the oculomotor vermis of animals during saccades, then followed the chain of events from experience of visual error, generation of CS, modulation of simple spikes, and ultimately change in motor output. We found that while error direction affected the probability of CS, error magnitude altered its temporal distribution. Production of CS changed the simple spikes on the next trial, but regardless of the actual visual error, this change biased the movement only along a vector that was parallel to the Purkinje cell's preferred error. From these results, we inferred the anatomy of a sensory-to-motor adaptive controller that transformed visual error vectors into motor-corrections.

Encoding of action by the Purkinje cells of the cerebellum.

Execution of accurate eye movements depends critically on the cerebellum, suggesting that the major output neurons of the cerebellum, Purkinje cells, may predict motion of the eye. However, this encoding of action for rapid eye movements (saccades) has remained unclear: Purkinje cells show little consistent modulation with respect to saccade amplitude or direction, and critically, their discharge lasts longer than the duration of a saccade. Here we analysed Purkinje-cell discharge in the oculomotor vermis of behaving rhesus monkeys (Macaca mulatta) and found neurons that increased or decreased their activity during saccades. We estimated the combined effect of these two populations via their projections to the caudal fastigial nucleus, and uncovered a simple-spike population response that precisely predicted the real-time motion of the eye. When we organized the Purkinje cells according to each cell's complex-spike directional tuning, the simple-spike population response predicted both the real-time speed and direction of saccade multiplicatively via a gain field. This suggests that the cerebellum predicts the real-time motion of the eye during saccades via the combined inputs of Purkinje cells onto individual nucleus neurons. A gain-field encoding of simple spikes emerges if the Purkinje cells that project onto a nucleus neuron are not selected at random but share a common complex-spike property.

A memory of errors in sensorimotor learning.

The current view of motor learning suggests that when we revisit a task, the brain recalls the motor commands it previously learned. In this view, motor memory is a memory of motor commands, acquired through trial-and-error and reinforcement. Here we show that the brain controls how much it is willing to learn from the current error through a principled mechanism that depends on the history of past errors. This suggests that the brain stores a previously unknown form of memory, a memory of errors. A mathematical formulation of this idea provides insights into a host of puzzling experimental data, including savings and meta-learning, demonstrating that when we are better at a motor task, it is partly because the brain recognizes the errors it experienced before.

Contributions of the cerebellum and the motor cortex to acquisition and retention of motor memories.

We investigated the contributions of the cerebellum and the motor cortex (M1) to acquisition and retention of human motor memories in a force field reaching task. We found that anodal transcranial direct current stimulation (tDCS) of the cerebellum, a technique that is thought to increase neuronal excitability, increased the ability to learn from error and form an internal model of the field, while cathodal cerebellar stimulation reduced this error-dependent learning. In addition, cathodal cerebellar stimulation disrupted the ability to respond to error within a reaching movement, reducing the gain of the sensory-motor feedback loop. By contrast, anodal M1 stimulation had no significant effects on these variables. During sham stimulation, early in training the acquired motor memory exhibited rapid decay in error-clamp trials. With further training the rate of decay decreased, suggesting that with training the motor memory was transformed from a labile to a more stable state. Surprisingly, neither cerebellar nor M1 stimulation altered these decay patterns. Participants returned 24hours later and were re-tested in error-clamp trials without stimulation. The cerebellar group that had learned the task with cathodal stimulation exhibited significantly impaired retention, and retention was not improved by M1 anodal stimulation. In summary, non-invasive cerebellar stimulation resulted in polarity-dependent up- or down-regulation of error-dependent motor learning. In addition, cathodal cerebellar stimulation during acquisition impaired the ability to retain the motor memory overnight. Thus, in the force field task we found a critical role for the cerebellum in both formation of motor memory and its retention.

Cerebellum estimates the sensory state of the body.

A recent neurophysiology study provides data from the cerebellar vermis/nodulus, where neurons encode translation of the head, even when these translations are induced via an illusion. These data provide new neurophysiological evidence that the cerebellum is important for computations involving internal models of motion, estimating the state of the body.

Synaptic weighting for physiological responses in recurrent spiking neural networks.

Recurrently connected neural networks have been used extensively in the literature to describe various neuro-physiological phenomena, such as coordinate transformations during sensorimotor integration. Due to the directed cycles that can exist in recurrent networks, there is no well-known way to a priori specify synaptic weights to elicit neuron spiking responses to stimuli based on available neurophysiology. Using a common mean field assumption in which synaptic inputs are uncorrelated for sufficiently large populations of neurons, we show that the connection topology and a neuron's response characteristics can be decoupled. This allows specification of neuron steady-state responses independent of the connection topology. We provide evidence from two case studies which serve to validate this synaptic weighting approach.