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Locomotor movements cause visual images to be displaced across the eye, a retinal slip that is counteracted by stabilizing reflexes in many animals. In insects, optomotor turning causes the animal to turn in the direction of rotating visual stimuli, thereby reducing retinal slip and stabilizing trajectories through the world. This behavior has formed the basis for extensive dissections of motion vision. Here, we report that under certain stimulus conditions, two Drosophila species, including the widely studied Drosophila melanogaster , can suppress and even reverse the optomotor turning response over several seconds. Such 'anti-directional turning' is most strongly evoked by long-lasting, high-contrast, slow-moving visual stimuli that are distinct from those that promote syn-directional optomotor turning. Anti-directional turning, like the syn-directional optomotor response, requires the local motion detecting neurons T4 and T5. A subset of lobula plate tangential cells, CH cells, show involvement in these responses. Imaging from a variety of direction-selective cells in the lobula plate shows no evidence of dynamics that match the behavior, suggesting that the observed inversion in turning direction emerges downstream of the lobula plate. Further, anti-directional turning declines with age and exposure to light. These results show that Drosophila optomotor turning behaviors contain rich, stimulus-dependent dynamics that are inconsistent with simple reflexive stabilization responses., Competing Interests: OM, MC, RT, MC, NM, JS, BB, TC No competing interests declared, DC Reviewing editor, eLife, (© 2023, Mano et al.)

Imaging neural activity in a behaving animal presents unique challenges in part because motion from an animal's movement creates artifacts in fluorescence intensity time-series that are difficult to distinguish from neural signals of interest. One approach to mitigating these artifacts is to image two channels simultaneously: one that captures an activity-dependent fluorophore, such as GCaMP, and another that captures an activity-independent fluorophore such as RFP. Because the activity-independent channel contains the same motion artifacts as the activity-dependent channel, but no neural signals, the two together can be used to identify and remove the artifacts. However, existing approaches for this correction, such as taking the ratio of the two channels, do not account for channel-independent noise in the measured fluorescence. Here, we present Two-channel Motion Artifact Correction (TMAC), a method which seeks to remove artifacts by specifying a generative model of the two channel fluorescence that incorporates motion artifact, neural activity, and noise. We use Bayesian inference to infer latent neural activity under this model, thus reducing the motion artifact present in the measured fluorescence traces. We further present a novel method for evaluating ground-truth performance of motion correction algorithms by comparing the decodability of behavior from two types of neural recordings; a recording that had both an activity-dependent fluorophore and an activity-independent fluorophore (GCaMP and RFP) and a recording where both fluorophores were activity-independent (GFP and RFP). A successful motion correction method should decode behavior from the first type of recording, but not the second. We use this metric to systematically compare five models for removing motion artifacts from fluorescent time traces. We decode locomotion from a GCaMP expressing animal 20x more accurately on average than from control when using TMAC inferred activity and outperforms all other methods of motion correction tested, the best of which were ~8x more accurate than control., Competing Interests: The authors have declared that no competing interests exist.

Artificial neural networks trained to solve sensory tasks can develop statistical representations that match those in biological circuits. However, it remains unclear whether they can reproduce properties of individual neurons. Here, we investigated how artificial networks predict individual neuron properties in the visual motion circuits of the fruit fly Drosophila. We trained anatomically constrained networks to predict movement in natural scenes, solving the same inference problem as fly motion detectors. Units in the artificial networks adopted many properties of analogous individual neurons, even though they were not explicitly trained to match these properties. Among these properties was the split into ON and OFF motion detectors, which is not predicted by classical motion detection models. The match between model and neurons was closest when models were trained to be robust to noise. These results demonstrate how anatomical, task, and noise constraints can explain properties of individual neurons in a small neural network., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 Elsevier Inc. All rights reserved.)

We present an automated method to track and identify neurons in C. elegans , called 'fast Deep Neural Correspondence' or fDNC, based on the transformer network architecture. The model is trained once on empirically derived semi-synthetic data and then predicts neural correspondence across held-out real animals. The same pre-trained model both tracks neurons across time and identifies corresponding neurons across individuals. Performance is evaluated against hand-annotated datasets, including NeuroPAL (Yemini et al., 2021). Using only position information, the method achieves 79.1% accuracy at tracking neurons within an individual and 64.1% accuracy at identifying neurons across individuals. Accuracy at identifying neurons across individuals is even higher (78.2%) when the model is applied to a dataset published by another group (Chaudhary et al., 2021). Accuracy reaches 74.7% on our dataset when using color information from NeuroPAL. Unlike previous methods, fDNC does not require straightening or transforming the animal into a canonical coordinate system. The method is fast and predicts correspondence in 10 ms making it suitable for future real-time applications., Competing Interests: XY, MC, FR, AS, SL, AL No competing interests declared, (© 2021, Yu et al.)

In functional imaging, large numbers of neurons are measured during sensory stimulation or behavior. This data can be used to map receptive fields that describe neural associations with stimuli or with behavior. The temporal resolution of these receptive fields has traditionally been limited by image acquisition rates. However, even when acquisitions scan slowly across a population of neurons, individual neurons may be measured at precisely known times. Here, we apply a method that leverages the timing of neural measurements to find receptive fields with temporal resolutions higher than the image acquisition rate. We use this temporal super-resolution method to resolve fast voltage and glutamate responses in visual neurons in Drosophila and to extract calcium receptive fields from cortical neurons in mammals. We provide code to easily apply this method to existing datasets. This method requires no specialized hardware and can be used with any optical indicator of neural activity.

Direction-selective neurons respond to visual motion in a preferred direction. They are direction-opponent if they are also inhibited by motion in the opposite direction. In flies and vertebrates, direction opponency has been observed in second-order direction-selective neurons, which achieve this opponency by subtracting signals from first-order direction-selective cells with opposite directional tunings. Here, we report direction opponency in Drosophila that emerges in first-order direction-selective neurons, the elementary motion detectors T4 and T5. This opponency persists when synaptic output from these cells is blocked, suggesting that it arises from feedforward, not feedback, computations. These observations exclude a broad class of linear-nonlinear models that have been proposed to describe direction-selective computations. However, they are consistent with models that include dynamic nonlinearities. Simulations of opponent models suggest that direction opponency in first-order motion detectors improves motion discriminability by suppressing noise generated by the local structure of natural scenes.

Background: To study visual processing, it is necessary to precisely control visual stimuli while recording neural and behavioral responses. It can be important to present stimuli over a broad area of the visual field, which can be technically difficult., New Method: We present a simple geometry that can be used to display panoramic stimuli. A single digital light projector generates images that are reflected by mirrors onto flat screens that surround an animal. It can be used for behavioral and neurophysiological measurements, so virtually identical stimuli can be presented. Moreover, this geometry permits light from the projector to be used to activate optogenetic tools., Results: Using this geometry, we presented panoramic visual stimulation to Drosophila in three paradigms. We presented drifting contrast gratings while recording walking and turning speed. We used the same projector to activate optogenetic channels during visual stimulation. Finally, we used two-photon microscopy to record responses in direction-selective cells to drifting gratings., Comparison With Existing Method(s): Existing methods have typically required custom hardware or curved screens, while this method requires only flat back projection screens and a digital light projector. The projector generates images in real time and does not require pre-generated images. Finally, while many setups are large, this geometry occupies a 30 × 20 cm footprint with a 25 cm height., Conclusions: This flexible geometry enables measurements of behavioral and neural responses to panoramic stimuli. This allows moderate throughput behavioral experiments with simultaneous optogenetic manipulation, with easy comparisons between behavior and neural activity using virtually identical stimuli., (Copyright © 2019 Elsevier B.V. All rights reserved.)

An animal's self-motion generates optic flow across its retina, and it can use this visual signal to regulate its orientation and speed through the world. While orientation control has been studied extensively in Drosophila and other insects, much less is known about the visual cues and circuits that regulate translational speed. Here, we show that flies regulate walking speed with an algorithm that is tuned to the speed of visual motion, causing them to slow when visual objects are nearby. This regulation does not depend strongly on the spatial structure or the direction of visual stimuli, making it algorithmically distinct from the classic computation that controls orientation. Despite the different algorithms, the visual circuits that regulate walking speed overlap with those that regulate orientation. Taken together, our findings suggest that walking speed is controlled by a hierarchical computation that combines multiple motion detectors with distinct tunings. VIDEO ABSTRACT., (Copyright © 2018 Elsevier Inc. All rights reserved.)

The assembly of functional neuronal circuits requires growth cones to extend in defined directions and recognize the correct synaptic partners. Homophilic adhesion between vertebrate Sidekick proteins promotes synapse formation between retinal neurons involved in visual motion detection. We show here that Drosophila Sidekick accumulates in specific synaptic layers of the developing motion detection circuit and is necessary for normal optomotor behavior. Sidekick is required in photoreceptors, but not in their target lamina neurons, to promote the alignment of lamina neurons into columns and subsequent sorting of photoreceptor axons into synaptic modules based on their precise spatial orientation. Sidekick is also localized to the dendrites of the direction-selective T4 and T5 cells, and is expressed in some of their presynaptic partners. In contrast to its vertebrate homologs, Sidekick is not essential for T4 and T5 to direct their dendrites to the appropriate layers or to receive synaptic contacts. These results illustrate a conserved requirement for Sidekick proteins in establishing visual motion detection circuits that is achieved through distinct cellular mechanisms in Drosophila and vertebrates., Competing Interests: Competing interestsThe authors declare no competing or financial interests., (© 2018. Published by The Company of Biologists Ltd.)

Homophilic binding of immunoglobulin superfamily molecules such as the Aplysia cell adhesion molecule (apCAM) leads to actin filament assembly near nascent adhesion sites. Such actin assembly can generate significant localized forces that have not been characterized in the larger context of axon growth and guidance. We used apCAM-coated bead substrates applied to the surface of neuronal growth cones to characterize the development of forces evoked by varying stiffness of mechanical restraint. Unrestrained bead propulsion matched or exceeded rates of retrograde network flow and was dependent on Arp2/3 complex activity. Analysis of growth cone forces applied to beads at low stiffness of restraint revealed switching between two states: frictional coupling to retrograde flow and Arp2/3-dependent propulsion. Stiff mechanical restraint led to formation of an extensive actin cup matching the geometric profile of the bead target and forward growth cone translocation; pharmacological inhibition of the Arp2/3 complex or Rac attenuated F-actin assembly near bead binding sites, decreased the efficacy of growth responses, and blocked accumulation of signaling molecules associated with nascent adhesions. These studies introduce a new model for regulation of traction force in which local actin assembly forces buffer nascent adhesion sites from the mechanical effects of retrograde flow., (© 2017 Buck et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).)

Like many behaviors, Caenorhabditis elegans egg laying alternates between inactive and active states. To understand how the underlying neural circuit turns the behavior on and off, we optically recorded circuit activity in behaving animals while manipulating circuit function using mutations, optogenetics, and drugs. In the active state, the circuit shows rhythmic activity phased with the body bends of locomotion. The serotonergic HSN command neurons initiate the active state, but accumulation of unlaid eggs also promotes the active state independent of the HSNs. The cholinergic VC motor neurons slow locomotion during egg-laying muscle contraction and egg release. The uv1 neuroendocrine cells mechanically sense passage of eggs through the vulva and release tyramine to inhibit egg laying, in part via the LGC-55 tyramine-gated Cl - channel on the HSNs. Our results identify discrete signals that entrain or detach the circuit from the locomotion central pattern generator to produce active and inactive states., Competing Interests: The authors declare that no competing interests exist.

Animals estimate visual motion by integrating light intensity information over time and space. The integration requires nonlinear processing, which makes motion estimation circuitry sensitive to specific spatiotemporal correlations that signify visual motion. Classical models of motion estimation weight these correlations to produce direction-selective signals. However, the correlational algorithms they describe have not been directly measured in elementary motion-detecting neurons (EMDs). Here, we employed stimuli to directly measure responses to pairwise correlations in Drosophila's EMD neurons, T4 and T5. Activity in these neurons was required for behavioral responses to pairwise correlations and was predictive of those responses. The pattern of neural responses in the EMDs was inconsistent with one classical model of motion detection, and the timescale and selectivity of correlation responses constrained the temporal filtering properties in potential models. These results reveal how neural responses to pairwise correlations drive visual behavior in this canonical motion-detecting circuit., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Proteins in cell signaling networks tend to interact promiscuously through low-affinity interactions. Consequently, evaluating the physiological importance of mapped interactions can be difficult. Attempts to do so have tended to focus on single, measurable physicochemical factors, such as affinity or abundance. For example, interaction importance has been assessed on the basis of the relative affinities of binding partners for a protein of interest, such as a receptor. However, multiple factors can be expected to simultaneously influence the recruitment of proteins to a receptor (and the potential of these proteins to contribute to receptor signaling), including affinity, abundance, and competition, which is a network property. Here, we demonstrate that measurements of protein copy numbers and binding affinities can be integrated within the framework of a mechanistic, computational model that accounts for mass action and competition. We use cell line-specific models to rank the relative importance of protein-protein interactions in the epidermal growth factor receptor (EGFR) signaling network for 11 different cell lines. Each model accounts for experimentally characterized interactions of six autophosphorylation sites in EGFR with proteins containing a Src homology 2 and/or phosphotyrosine-binding domain. We measure importance as the predicted maximal extent of recruitment of a protein to EGFR following ligand-stimulated activation of EGFR signaling. We find that interactions ranked highly by this metric include experimentally detected interactions. Proteins with high importance rank in multiple cell lines include proteins with recognized, well-characterized roles in EGFR signaling, such as GRB2 and SHC1, as well as a protein with a less well-defined role, YES1. Our results reveal potential cell line-specific differences in recruitment., (Copyright © 2015 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

Background: Mathematical/computational models are needed to understand cell signaling networks, which are complex. Signaling proteins contain multiple functional components and multiple sites of post-translational modification. The multiplicity of components and sites of modification ensures that interactions among signaling proteins have the potential to generate myriad protein complexes and post-translational modification states. As a result, the number of chemical species that can be populated in a cell signaling network, and hence the number of equations in an ordinary differential equation model required to capture the dynamics of these species, is prohibitively large. To overcome this problem, the rule-based modeling approach has been developed for representing interactions within signaling networks efficiently and compactly through coarse-graining of the chemical kinetics of molecular interactions., Results: Here, we provide a demonstration that the rule-based modeling approach can be used to specify and simulate a large model for ERBB receptor signaling that accounts for site-specific details of protein-protein interactions. The model is considered large because it corresponds to a reaction network containing more reactions than can be practically enumerated. The model encompasses activation of ERK and Akt, and it can be simulated using a network-free simulator, such as NFsim, to generate time courses of phosphorylation for 55 individual serine, threonine, and tyrosine residues. The model is annotated and visualized in the form of an extended contact map., Conclusions: With the development of software that implements novel computational methods for calculating the dynamics of large-scale rule-based representations of cellular signaling networks, it is now possible to build and analyze models that include a significant fraction of the protein interactions that comprise a signaling network, with incorporation of the site-specific details of the interactions. Modeling at this level of detail is important for understanding cellular signaling.