Zebrafish dscaml1 Deficiency Impairs Retinal Patterning and Oculomotor Function.

Down syndrome cell adhesion molecules (dscam and dscaml1) are essential regulators of neural circuit assembly, but their roles in vertebrate neural circuit function are still mostly unexplored. We investigated the functional consequences of dscaml1 deficiency in the larval zebrafish (sexually undifferentiated) oculomotor system, where behavior, circuit function, and neuronal activity can be precisely quantified. Genetic perturbation of dscaml1 resulted in deficits in retinal patterning and light adaptation, consistent with its known roles in mammals. Oculomotor analyses revealed specific deficits related to the dscaml1 mutation, including severe fatigue during gaze stabilization, reduced saccade amplitude and velocity in the light, greater disconjugacy, and impaired fixation. Two-photon calcium imaging of abducens neurons in control and dscaml1 mutant animals confirmed deficits in saccade-command signals (indicative of an impairment in the saccadic premotor pathway), whereas abducens activation by the pretectum-vestibular pathway was not affected. Together, we show that loss of dscaml1 resulted in impairments in specific oculomotor circuits, providing a new animal model to investigate the development of oculomotor premotor pathways and their associated human ocular disorders.SIGNIFICANCE STATEMENTDscaml1 is a neural developmental gene with unknown behavioral significance. Using the zebrafish model, this study shows that dscaml1 mutants have a host of oculomotor (eye movement) deficits. Notably, the oculomotor phenotypes in dscaml1 mutants are reminiscent of human ocular motor apraxia, a neurodevelopmental disorder characterized by reduced saccade amplitude and gaze stabilization deficits. Population-level recording of neuronal activity further revealed potential subcircuit-specific requirements for dscaml1 during oculomotor behavior. These findings underscore the importance of dscaml1 in the development of visuomotor function and characterize a new model to investigate potential circuit deficits underlying human oculomotor disorders.

Mechanical Contact Spectroscopy: Characterizing Nanoscale Adhesive Contacts via Thermal Forces.

The adhesion of micro- and nanoparticles to solid substrates immersed in liquids is a problem of great scientific and technological importance. However, the quantitative characterization of such nanoscale adhesive contacts without rupturing them still presents a major experimental challenge. In this article, we introduce mechanical contact spectroscopy (MCS), an experimental technique for the nondestructive probing of particle adhesion in liquid environments. With MCS, the strength of adhesive contacts is inferred from residual position fluctuations of adherent particles excited by thermal forces. In particular, the strength of adhesion is correlated with the standard deviation of the particle lateral position x, with smaller position standard deviations [Formula: see text] indicating higher adhesive strength. For a given combination of particles, substrate, and immersion medium, the adhesion is characterized by the mechanical contact spectrum, which is a histogram of ξ values obtained from tracking an ensemble of adherent particles. Because the energy of thermal excitation at room temperature is very small in comparison to the typical total energy of adhesive contacts, the studied contacts remain in equilibrium during the measurement. Using MCS, we study the adhesion of micrometer-sized particles to planar solid substrates under a wide range of environmental conditions, including liquid immersion media of varying ionic strength and adhesion substrates with different chemical functionality of their surfaces. These experiments provide evidence that MCS is capable of reproducibly detecting minute changes in the particle-substrate work of adhesion while at the same time covering the range of adhesive contact strength relevant in the context of surface chemistry, biology, and microfabrication.

Fast inference in generalized linear models via expected log-likelihoods.

Generalized linear models play an essential role in a wide variety of statistical applications. This paper discusses an approximation of the likelihood in these models that can greatly facilitate computation. The basic idea is to replace a sum that appears in the exact log-likelihood by an expectation over the model covariates; the resulting "expected log-likelihood" can in many cases be computed significantly faster than the exact log-likelihood. In many neuroscience experiments the distribution over model covariates is controlled by the experimenter and the expected log-likelihood approximation becomes particularly useful; for example, estimators based on maximizing this expected log-likelihood (or a penalized version thereof) can often be obtained with orders of magnitude computational savings compared to the exact maximum likelihood estimators. A risk analysis establishes that these maximum EL estimators often come with little cost in accuracy (and in some cases even improved accuracy) compared to standard maximum likelihood estimates. Finally, we find that these methods can significantly decrease the computation time of marginal likelihood calculations for model selection and of Markov chain Monte Carlo methods for sampling from the posterior parameter distribution. We illustrate our results by applying these methods to a computationally-challenging dataset of neural spike trains obtained via large-scale multi-electrode recordings in the primate retina.

Incorporating naturalistic correlation structure improves spectrogram reconstruction from neuronal activity in the songbird auditory midbrain.

Birdsong is comprised of rich spectral and temporal organization, which might be used for vocal perception. To quantify how this structure could be used, we have reconstructed birdsong spectrograms by combining the spike trains of zebra finch auditory midbrain neurons with information about the correlations present in song. We calculated maximum a posteriori estimates of song spectrograms using a generalized linear model of neuronal responses and a series of prior distributions, each carrying different amounts of statistical information about zebra finch song. We found that spike trains from a population of mesencephalicus lateral dorsalis (MLd) neurons combined with an uncorrelated Gaussian prior can estimate the amplitude envelope of song spectrograms. The same set of responses can be combined with Gaussian priors that have correlations matched to those found across multiple zebra finch songs to yield song spectrograms similar to those presented to the animal. The fidelity of spectrogram reconstructions from MLd responses relies more heavily on prior knowledge of spectral correlations than temporal correlations. However, the best reconstructions combine MLd responses with both spectral and temporal correlations.

Ramp-to-threshold dynamics in a hindbrain population controls the timing of spontaneous saccades.

Organisms have the capacity to make decisions based solely on internal drives. However, it is unclear how neural circuits form decisions in the absence of sensory stimuli. Here we provide a comprehensive map of the activity patterns underlying the generation of saccades made in the absence of visual stimuli. We perform calcium imaging in the larval zebrafish to discover a range of responses surrounding spontaneous saccades, from cells that display tonic discharge only during fixations to neurons whose activity rises in advance of saccades by multiple seconds. When we lesion cells in these populations we find that ablation of neurons with pre-saccadic rise delays saccade initiation. We analyze spontaneous saccade initiation using a ramp-to-threshold model and are able to predict the times of upcoming saccades using pre-saccadic activity. These findings suggest that ramping of neuronal activity to a bound is a critical component of self-initiated saccadic movements.

Electron Microscopic Reconstruction of Functionally Identified Cells in a Neural Integrator.

Neural integrators are involved in a variety of sensorimotor and cognitive behaviors. The oculomotor system contains a simple example, a hindbrain neural circuit that takes velocity signals as inputs and temporally integrates them to control eye position. Here we investigated the structural underpinnings of temporal integration in the larval zebrafish by first identifying integrator neurons using two-photon calcium imaging and then reconstructing the same neurons through serial electron microscopic analysis. Integrator neurons were identified as those neurons with activities highly correlated with eye position during spontaneous eye movements. Three morphological classes of neurons were observed: ipsilaterally projecting neurons located medially, contralaterally projecting neurons located more laterally, and a population at the extreme lateral edge of the hindbrain for which we were not able to identify axons. Based on their somatic locations, we inferred that neurons with only ipsilaterally projecting axons are glutamatergic, whereas neurons with only contralaterally projecting axons are largely GABAergic. Dendritic and synaptic organization of the ipsilaterally projecting neurons suggests a broad sampling from inputs on the ipsilateral side. We also observed the first conclusive evidence of synapses between integrator neurons, which have long been hypothesized by recurrent network models of integration via positive feedback.

Population-scale organization of cerebellar granule neuron signaling during a visuomotor behavior.

Granule cells at the input layer of the cerebellum comprise over half the neurons in the human brain and are thought to be critical for learning. However, little is known about granule neuron signaling at the population scale during behavior. We used calcium imaging in awake zebrafish during optokinetic behavior to record transgenically identified granule neurons throughout a cerebellar population. A significant fraction of the population was responsive at any given time. In contrast to core precerebellar populations, granule neuron responses were relatively heterogeneous, with variation in the degree of rectification and the balance of positive versus negative changes in activity. Functional correlations were strongest for nearby cells, with weak spatial gradients in the degree of rectification and the average sign of response. These data open a new window upon cerebellar function and suggest granule layer signals represent elementary building blocks under-represented in core sensorimotor pathways, thereby enabling the construction of novel patterns of activity for learning.