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1. Inhibitory feedback from the motor circuit gates mechanosensory processing in Caenorhabditis elegans

   https://doi.org/10.1371/journal.pbio.3002280  

   Kumar, Sandeep; Sharma, Anuj K; Tran, Andrew; Liu, Mochi; Leifer, Andrew M (September 2023, PLOS Biology)

   Sengupta, Piali (Ed.)

   Animals must integrate sensory cues with their current behavioral context to generate a suitable response. How this integration occurs is poorly understood. Previously, we developed high-throughput methods to probe neural activity in populations ofCaenorhabditis elegansand discovered that the animal’s mechanosensory processing is rapidly modulated by the animal’s locomotion. Specifically, we found that when the worm turns it suppresses its mechanosensory-evoked reversal response. Here, we report thatC.elegansuse inhibitory feedback from turning-associated neurons to provide this rapid modulation of mechanosensory processing. By performing high-throughput optogenetic perturbations triggered on behavior, we show that turning-associated neurons SAA, RIV, and/or SMB suppress mechanosensory-evoked reversals during turns. We find that activation of the gentle-touch mechanosensory neurons or of any of the interneurons AIZ, RIM, AIB, and AVE during a turn is less likely to evoke a reversal than activation during forward movement. Inhibiting neurons SAA, RIV, and SMB during a turn restores the likelihood with which mechanosensory activation evokes reversals. Separately, activation of premotor interneuron AVA evokes reversals regardless of whether the animal is turning or moving forward. We therefore propose that inhibitory signals from SAA, RIV, and/or SMB gate mechanosensory signals upstream of neuron AVA. We conclude thatC.elegansrely on inhibitory feedback from the motor circuit to modulate its response to sensory stimuli on fast timescales. This need for motor signals in sensory processing may explain the ubiquity in many organisms of motor-related neural activity patterns seen across the brain, including in sensory processing areas. 

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2. Enhancing reinforcement learning models by including direct and indirect pathways improves performance on striatal dependent tasks

   https://doi.org/10.1371/journal.pcbi.1011385  

   Blackwell, Kim T.; Doya, Kenji (August 2023, PLOS Computational Biology)

   Cai, Ming Bo (Ed.)

   A major advance in understanding learning behavior stems from experiments showing that reward learning requires dopamine inputs to striatal neurons and arises from synaptic plasticity of cortico-striatal synapses. Numerous reinforcement learning models mimic this dopamine-dependent synaptic plasticity by using the reward prediction error, which resembles dopamine neuron firing, to learn the best action in response to a set of cues. Though these models can explain many facets of behavior, reproducing some types of goal-directed behavior, such as renewal and reversal, require additional model components. Here we present a reinforcement learning model, TD2Q, which better corresponds to the basal ganglia with two Q matrices, one representing direct pathway neurons (G) and another representing indirect pathway neurons (N). Unlike previous two-Q architectures, a novel and critical aspect of TD2Q is to update the G and N matrices utilizing the temporal difference reward prediction error. A best action is selected for N and G using a softmax with a reward-dependent adaptive exploration parameter, and then differences are resolved using a second selection step applied to the two action probabilities. The model is tested on a range of multi-step tasks including extinction, renewal, discrimination; switching reward probability learning; and sequence learning. Simulations show that TD2Q produces behaviors similar to rodents in choice and sequence learning tasks, and that use of the temporal difference reward prediction error is required to learn multi-step tasks. Blocking the update rule on the N matrix blocks discrimination learning, as observed experimentally. Performance in the sequence learning task is dramatically improved with two matrices. These results suggest that including additional aspects of basal ganglia physiology can improve the performance of reinforcement learning models, better reproduce animal behaviors, and provide insight as to the role of direct- and indirect-pathway striatal neurons. 

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3. Inhibitory control explains locomotor statistics in walking Drosophila

   https://doi.org/10.1073/pnas.2407626122  

   Gattuso, Hannah C; van_Hassel, Karin A; Freed, Jacob D; Nuñez, Kavin M; de_la_Rea, Beatriz; May, Christina E; Ermentrout, Bard; Victor, Jonathan D; Nagel, Katherine I (April 2025, Proceedings of the National Academy of Sciences)

   In order to forage for food, many animals regulate not only specific limb movements but the statistics of locomotor behavior, switching between long-range dispersal and local search depending on resource availability. How premotor circuits regulate locomotor statistics is not clear. Here, we analyze and model locomotor statistics and their modulation by attractive food odor in walkingDrosophila. Food odor evokes three motor regimes in flies: baseline walking, upwind running during odor, and search behavior following odor loss. During search, we find that flies adopt higher angular velocities and slower ground speeds and turn for longer periods in the same direction. We further find that flies adopt periods of different mean ground speed and that these state changes influence the length of odor-evoked runs. We next developed a simple model of neural locomotor control that suggests that contralateral inhibition plays a key role in regulating the statistical features of locomotion. As the fly connectome predicts decussating inhibitory neurons in the premotor lateral accessory lobe (LAL), we gained genetic access to a subset of these neurons and tested their effects on behavior. We identified one population whose activation induces all three signature of local search and that regulates angular velocity at odor offset. We identified a second population, including a single LAL neuron pair, that bidirectionally regulates ground speed. Together, our work develops a biologically plausible computational architecture that captures the statistical features of fly locomotion across behavioral states and identifies neural substrates of these computations. 

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4. A multilayer circuit architecture for the generation of distinct locomotor behaviors in Drosophila

   https://doi.org/10.7554/eLife.51781  

   Zarin, Aref Arzan; Mark, Brandon; Cardona, Albert; Litwin-Kumar, Ashok; Doe, Chris Q (December 2019, eLife)

   Animals generate diverse motor behaviors, yet how the same motor neurons (MNs) generate two distinct or antagonistic behaviors remains an open question. Here, we characterize Drosophila larval muscle activity patterns and premotor/motor circuits to understand how they generate forward and backward locomotion. We show that all body wall MNs are activated during both behaviors, but a subset of MNs change recruitment timing for each behavior. We used TEM to reconstruct a full segment of all 60 MNs and 236 premotor neurons (PMNs), including differentially-recruited MNs. Analysis of this comprehensive connectome identified PMN-MN ‘labeled line’ connectivity; PMN-MN combinatorial connectivity; asymmetric neuronal morphology; and PMN-MN circuit motifs that could all contribute to generating distinct behaviors. We generated a recurrent network model that reproduced the observed behaviors, and used functional optogenetics to validate selected model predictions. This PMN-MN connectome will provide a foundation for analyzing the full suite of larval behaviors. 

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5. Neuromuscular basis of Drosophila larval rolling escape behavior

   https://doi.org/10.1073/pnas.2303641120  

   Cooney, Patricia C; Huang, Yuhan; Li, Wenze; Perera, Dulanjana M; Hormigo, Richard; Tabachnik, Tanya; Godage, Isuru S; Hillman, Elizabeth_M C; Grueber, Wesley B; Zarin, Aref A (December 2023, Proceedings of the National Academy of Sciences)

   When threatened by dangerous or harmful stimuli, animals engage in diverse forms of rapid escape behaviors. InDrosophilalarvae, one type of escape response involves C-shaped bending and lateral rolling followed by rapid forward crawling. The sensory circuitry that promotes larval escape has been extensively characterized; however, the motor programs underlying rolling are unknown. Here, we characterize the neuromuscular basis of rolling escape behavior. We used high-speed, volumetric, Swept Confocally Aligned Planar Excitation (SCAPE) microscopy to image muscle activity during larval rolling. Unlike sequential peristaltic muscle contractions that progress from segment to segment during forward and backward crawling, muscle activity progresses circumferentially during bending and rolling escape behavior. We propose that progression of muscular contraction around the larva’s circumference results in a transient misalignment between weight and the ground support forces, which generates a torque that induces stabilizing body rotation. Therefore, successive cycles of slight misalignment followed by reactive aligning rotation lead to continuous rolling motion. Supporting our biomechanical model, we found that disrupting the activity of muscle groups undergoing circumferential contraction progression leads to rolling defects. We use EM connectome data to identify premotor to motor connectivity patterns that could drive rolling behavior and perform neural silencing approaches to demonstrate the crucial role of a group of glutamatergic premotor neurons in rolling. Our data reveal body-wide muscle activity patterns and putative premotor circuit organization for execution of the rolling escape response. 

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