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.

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.

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.

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.

Effect of Pet Dogs on Children's Perceived Stress and Cortisol Stress Response.

The present study tested whether pet dogs have stress-buffering effects for children during a validated laboratory-based protocol, the Trier Social Stress Test for Children (TSST-C). Participants were 101 children aged 7-12 years with their primary caregivers and pet dogs. Children were randomly assigned in the TSST-C to a pet present condition or one of two comparison conditions: parent present or no support figure present. Baseline, response, and recovery indices of perceived stress and cortisol levels were computed based on children's self-reported feelings of stress and salivary cortisol. Results indicated that in the alone (no social support) condition, children showed the expected rise for both perceived stress and cortisol response to stress. Pet dog presence significantly buffered the perceived stress response in comparison to children in the alone and parent present conditions. No main condition effect was observed for cortisol; however, for children experiencing the stressor with their pet present, lower cortisol response to stress was associated with more child-initiated petting and less dog proximity-seeking behavior. The results support the notion that pet dogs can provide socio-emotional benefits for children via stress buffering.

Express saccades and superior colliculus responses are sensitive to short-wavelength cone contrast.

A key structure for directing saccadic eye movements is the superior colliculus (SC). The visual pathways that project to the SC have been reported to carry only luminance information and not color information. Short-wavelength-sensitive cones (S-cones) in the retina make little or no contribution to luminance signals, leading to the conclusion that S-cone stimuli should be invisible to SC neurons. The premise that S-cone stimuli are invisible to the SC has been used in numerous clinical and human psychophysical studies. The assumption that the SC cannot use S-cone stimuli to guide behavior has never been tested. We show here that express saccades, which depend on the SC, can be driven by S-cone input. Further, express saccade reaction times and changes in SC activity depend on the amount of S-cone contrast. These results demonstrate that the SC can use S-cone stimuli to guide behavior. We conclude that the use of S-cone stimuli is insufficient to isolate SC function in psychophysical and clinical studies of human subjects.

Remapping for visual stability.

Visual perception is based on both incoming sensory signals and information about ongoing actions. Recordings from single neurons have shown that corollary discharge signals can influence visual representations in parietal, frontal and extrastriate visual cortex, as well as the superior colliculus (SC). In each of these areas, visual representations are remapped in conjunction with eye movements. Remapping provides a mechanism for creating a stable, eye-centred map of salient locations. Temporal and spatial aspects of remapping are highly variable from cell to cell and area to area. Most neurons in the lateral intraparietal area remap stimulus traces, as do many neurons in closely allied areas such as the frontal eye fields the SC and extrastriate area V3A. Remapping is not purely a cortical phenomenon. Stimulus traces are remapped from one hemifield to the other even when direct cortico-cortical connections are removed. The neural circuitry that produces remapping is distinguished by significant plasticity, suggesting that updating of salient stimuli is fundamental for spatial stability and visuospatial behaviour. These findings provide new evidence that a unified and stable representation of visual space is constructed by redundant circuitry, comprising cortical and subcortical pathways, with a remarkable capacity for reorganization.

Spatial updating in monkey superior colliculus in the absence of the forebrain commissures: dissociation between superficial and intermediate layers.

In previous studies, we demonstrated that the forebrain commissures are the primary pathway for remapping from one hemifield to the other. Nonetheless, remapping in lateral intraparietal cortex (LIP) across hemifield is still present in split brain monkeys. This finding indicates that a subcortical structure must contribute to remapping. The primary goal of the current study was to characterize remapping activity in the superior colliculus in intact and split brain monkeys. We recorded neurons in both the superficial and intermediate layers of the SC. We found that across-hemifield remapping was reduced in magnitude and delayed compared with within-hemifield remapping in the intermediate layers of the SC in split brain monkeys. These results mirror our previous findings in area LIP. In contrast, we found no difference in the magnitude or latency for within- compared with across-hemifield remapping in the superficial layers. At the behavioral level, we compared the performance of the monkeys on two conditions of a double-step task. When the second target remained within a single hemifield, performance remained accurate. When the second target had to be updated across hemifields, the split brain monkeys' performance was impaired. Remapping activity in the intermediate layers was correlated with the accuracy and latency of the second saccade during the across-hemifield trials. Remapping in the superficial layers was correlated with latency of the second saccade during the within- and across-hemifield trials. The differences between the layers suggest that different circuits underlie remapping in the superficial and intermediate layers of the superior colliculus.