A machine-learning tool to identify bistable states from calcium imaging data.

Mapping neuronal activation using calcium imaging in vivo during behavioural tasks has advanced our understanding of nervous system function. In almost all of these studies, calcium imaging is used to infer spike probabilities because action potentials activate voltage-gated calcium channels and increase intracellular calcium levels. However, neurons not only fire action potentials, but also convey information via intrinsic dynamics such as by generating bistable membrane potential states. Although a number of tools for spike inference have been developed and are currently being used, no tool exists for converting calcium imaging signals to maps of cellular state in bistable neurons. Purkinje neurons in the larval zebrafish cerebellum exhibit membrane potential bistability, firing either tonically or in bursts. Several studies have implicated the role of a population code in cerebellar function, with bistability adding an extra layer of complexity to this code. In the present study, we develop a tool, CaMLSort, which uses convolutional recurrent neural networks to classify calcium imaging traces as arising from either tonic or bursting cells. We validate this classifier using a number of different methods and find that it performs well on simulated event rasters as well as real biological data that it had not previously seen. Moreover, we find that CaMLsort generalizes to other bistable neurons, such as dopaminergic neurons in the ventral tegmental area of mice. Thus, this tool offers a new way of analysing calcium imaging data from bistable neurons to understand how they participate in network computation and natural behaviours. KEY POINTS: Calcium imaging, compriising the gold standard of inferring neuronal activity, does not report cellular state in neurons that are bistable, such as Purkinje neurons in the cerebellum of larval zebrafish. We model the relationship between Purkinje neuron electrical activity and its corresponding calcium signal to compile a dataset of state-labelled simulated calcium signals. We apply machine-learning methods to this dataset to develop a tool that can classify the state of a Purkinje neuron using only its calcium signal, which works well on real data even though it was trained only on simulated data. CaMLsort (Calcium imaging and Machine Learning based tool to sort intracellular state) also generalizes well to bistable neurons in a different brain region (ventral tegmental area) in a different model organism (mouse). This tool can facilitate our understanding of how these neurons carry out their functions in a circuit.

Spinal Interneurons: Diversity and Connectivity in Motor Control.

The spinal cord is home to the intrinsic networks for locomotion. An animal in which the spinal cord has been fully severed from the brain can still produce rhythmic, patterned locomotor movements as long as some excitatory drive is provided, such as physical, pharmacological, or electrical stimuli. Yet it remains a challenge to define the underlying circuitry that produces these movements because the spinal cord contains a wide variety of neuron classes whose patterns of interconnectivity are still poorly understood. Computational models of locomotion accordingly rely on untested assumptions about spinal neuron network element identity and connectivity. In this review, we consider the classes of spinal neurons, their interconnectivity, and the significance of their circuit connections along the long axis of the spinal cord. We suggest several lines of analysis to move toward a definitive understanding of the spinal network.

Modeling spinal locomotor circuits for movements in developing zebrafish.

Many spinal circuits dedicated to locomotor control have been identified in the developing zebrafish. How these circuits operate together to generate the various swimming movements during development remains to be clarified. In this study, we iteratively built models of developing zebrafish spinal circuits coupled to simplified musculoskeletal models that reproduce coiling and swimming movements. The neurons of the models were based upon morphologically or genetically identified populations in the developing zebrafish spinal cord. We simulated intact spinal circuits as well as circuits with silenced neurons or altered synaptic transmission to better understand the role of specific spinal neurons. Analysis of firing patterns and phase relationships helped to identify possible mechanisms underlying the locomotor movements of developing zebrafish. Notably, our simulations demonstrated how the site and the operation of rhythm generation could transition between coiling and swimming. The simulations also underlined the importance of contralateral excitation to multiple tail beats. They allowed us to estimate the sensitivity of spinal locomotor networks to motor command amplitude, synaptic weights, length of ascending and descending axons, and firing behavior. These models will serve as valuable tools to test and further understand the operation of spinal circuits for locomotion.

The spinal cord is a column of nerve tissue that connects the brain to the rest of the body in vertebrate animals. Nerve cells in the spinal cord, called neurons, help to control and coordinate the body's movements. As the spinal cord develops, new neurons are born and new connections are made between neurons and muscles, resulting in more coordinated and skillful movements as time goes on. Zebrafish, for example, display body-bending maneuvers called coils within 24 hours of the egg being fertilized. Next, bursts of swimming movements emerge, which are driven by sporadic tail beats. These tail maneuvers become more consistent as the fish develops, and eventually result in smooth movements called beat-and-glide swimming. The groups of spinal cord neurons that appear at each stage of zebrafish development have been characterized, but it remains unclear how newly formed circuits (groups of neurons recently connected to each other) work together to produce swimming maneuvers. To answer this question, Roussel et al. simulated changes in the spinal cord that help zebrafish acquire new swimming movements as they grow. The computer models encoded neural circuits based on cell populations identified in experimental studies, and replicated swimming behaviors that emerge during the first few days of zebrafish development. Simulations tested how specific neural circuits generate the characteristic swimming movements that represent key developmental milestones in zebrafish. The results showed that adding new neurons and more cell-to-cell connections led to increasingly sophisticated swimming maneuvers. As the zebrafish spinal cord matured, the fish were better able to control the pace and duration of their swimming movements. Roussel et al. also identified specific patterns of neural activity linked to particular maneuvers. For example, tail beats switch direction when neurons on one side of the spinal cord excite neurons on the opposite side. This activity, which becomes more rhythmic, also needs to be exquisitely timed to produce and coordinate the right motion. Roussel et al.'s modelling of developmental milestones in growing zebrafish provides insights into how neural networks control movement. The computer models are among the first to accurately reproduce swimming behaviors in developing zebrafish. More experimental data could be added to the models to capture the full range of early zebrafish movements, and to further investigate how maturing spinal cord circuits control swimming. Since zebrafish and mammals have many spinal neurons in common, further research may aid our understanding of movement disorders in humans.

Spinal V1 neurons inhibit motor targets locally and sensory targets distally.

Rostro-caudal coordination of spinal motor output is essential for locomotion. Most spinal interneurons project axons longitudinally to govern locomotor output, yet their connectivity along this axis remains unclear. In this study, we use larval zebrafish to map synaptic outputs of a major inhibitory population, V1 (Eng1+) neurons, which are implicated in dual sensory and motor functions. We find that V1 neurons exhibit long axons extending rostrally and exclusively ipsilaterally for an average of 6 spinal segments; however, they do not connect uniformly with their post-synaptic targets along the entire length of their axon. Locally, V1 neurons inhibit motor neurons (both fast and slow) and other premotor targets, including V2a, V2b, and commissural premotor neurons. In contrast, V1 neurons make robust long-range inhibitory contacts onto a dorsal horn sensory population, the commissural primary ascending neurons (CoPAs). In a computational model of the ipsilateral spinal network, we show that this pattern of short-range V1 inhibition to motor and premotor neurons underlies burst termination, which is critical for coordinated rostro-caudal propagation of the locomotor wave. We conclude that spinal network architecture in the longitudinal axis can vary dramatically, with differentially targeted local and distal connections, yielding important consequences for function.

Spinal V2b neurons reveal a role for ipsilateral inhibition in speed control.

The spinal cord contains a diverse array of interneurons that govern motor output. Traditionally, models of spinal circuits have emphasized the role of inhibition in enforcing reciprocal alternation between left and right sides or flexors and extensors. However, recent work has shown that inhibition also increases coincident with excitation during contraction. Here, using larval zebrafish, we investigate the V2b (Gata3+) class of neurons, which contribute to flexor-extensor alternation but are otherwise poorly understood. Using newly generated transgenic lines we define two stable subclasses with distinct neurotransmitter and morphological properties. These V2b subclasses synapse directly onto motor neurons with differential targeting to speed-specific circuits. In vivo, optogenetic manipulation of V2b activity modulates locomotor frequency: suppressing V2b neurons elicits faster locomotion, whereas activating V2b neurons slows locomotion. We conclude that V2b neurons serve as a brake on axial motor circuits. Together, these results indicate a role for ipsilateral inhibition in speed control.

Loss of the Habenula Intrinsic Neuromodulator Kisspeptin1 Affects Learning in Larval Zebrafish.

Learning how to actively avoid a predictable threat involves two steps: recognizing the cue that predicts upcoming punishment and learning a behavioral response that will lead to avoidance. In zebrafish, ventral habenula (vHb) neurons have been proposed to participate in both steps by encoding the expected aversiveness of a stimulus. vHb neurons increase their firing rate as expectation of punishment grows but reduce their activity as avoidance learning occurs. This leads to changes in the activity of raphe neurons, which are downstream of the vHb, during learning. How vHb activity is regulated is not known. Here, we ask whether the neuromodulator Kisspeptin1, which is expressed in the ventral habenula together with its receptor, could be involved. Kiss1 mutants were generated with CRISPR/Cas9 using guide RNAs targeted to the signal sequence. Mutants, which have a stop codon upstream of the active Kisspeptin1 peptide, have a deficiency in learning to avoid a shock that is predicted by light. Electrophysiology indicates that Kisspeptin1 has a concentration-dependent effect on vHb neurons: depolarizing at low concentrations and hyperpolarizing at high concentrations. Two-photon calcium imaging shows that mutants have reduced raphe response to shock. These data are consistent with the hypothesis that Kisspeptin1 modulates habenula neurons as the fish learns to cope with a threat. Learning a behavioral strategy to overcome a stressor may thus be accompanied by physiological change in the habenula, mediated by intrinsic neuromodulation.

AMPA receptor mediated synaptic excitation drives state-dependent bursting in Purkinje neurons of zebrafish larvae.

Purkinje neurons are central to cerebellar function and show membrane bistability when recorded in vitro or in vivo under anesthesia. The existence of bistability in vivo in awake animals is disputed. Here, by recording intracellularly from Purkinje neurons in unanesthetized larval zebrafish (Danio rerio), we unequivocally demonstrate bistability in these neurons. Tonic firing was seen in depolarized regimes and bursting at hyperpolarized membrane potentials. In addition, Purkinje neurons could switch from one state to another spontaneously or with current injection. While GABAAR or NMDAR were not required for bursting, activation of AMPARs by climbing fibers (CFs) was sufficient to trigger bursts. Further, by recording Purkinje neuron membrane potential intracellularly, and motor neuron spikes extracellularly, we show that initiation of motor neuron spiking is correlated with increased incidence of CF EPSPs and membrane depolarization. Developmentally, bistability was observed soon after Purkinje neuron specification and persists at least until late larval stages.