Biology subjects

Chou, G. M.

Publications and source records attributed to Chou, G. M..

4 recordsLinked to original sources

bioRxiv2025

Connectome simulations identify a central pattern generator circuit for fly walking

Animal locomotion relies on rhythmic body movements driven by central pattern generators (CPGs): neural circuits that produce oscillating output without oscillating input. However, the circuit structure of a CPG for walking is not known in any animal. To identify the cells and synapses that underlie rhythmic leg movement in walking flies, we developed dynamic simulations of the Drosophila ventral nerve cord (VNC) connectomes. A computational activation screen of descending neurons from the central brain identified DNg100--a known command neuron for walking--as the top driver of rhythmic leg motor activity. Simulated network pruning isolated a minimal rhythm-generating circuit consisting of one inhibitory and two excitatory interneurons; this three-neuron circuit was necessary and sufficient for motor rhythms across all six legs and in four connectome datasets. Simulations also predicted that a separate descending pathway (DNb08) drives rhythmic leg movements, which we confirmed experimentally using optogenetics in behaving flies. Our results reveal the cellular identity and synaptic structure of a putative CPG circuit for walking and other rhythmic leg movements in flies.

neuroscience↗

bioRxiv2025

Proprioceptive limit detectors mediate sensorimotor control of the Drosophila leg

Many animals possess mechanosensory neurons that fire when a limb nears the limit of its physical range, but the function of these proprioceptive limit detectors remains poorly understood. Here, we investigate a class of proprioceptors on the Drosophila leg called hair plates. Using calcium imaging in behaving flies, we find that a hair plate on the fly coxa (CxHP8) detects the limits of anterior leg movement. Reconstructing CxHP8 axons in the connectome, we found that they are wired to excite posterior leg movement and inhibit anterior leg movement. Consistent with this connectivity, optogenetic activation of CxHP8 neurons elicited posterior postural reflexes, while silencing altered the swing-to-stance transition during walking. Finally, we use comprehensive reconstruction of peripheral morphology and downstream connectivity to predict the function of other hair plates distributed across the fly leg. Our results suggest that each hair plate is specialized to control specific sensorimotor reflexes that are matched to the joint limit it detects. They also illustrate the feasibility of predicting sensorimotor reflexes from a connectome with identified proprioceptive inputs and motor outputs.

neuroscience↗

bioRxiv2024

Sensorimotor delays constrain robust locomotion in a 3D kinematic model of fly walking

Walking animals must maintain stability in the presence of external perturbations, despite significant temporal delays in neural signaling and muscle actuation. Here, we develop a 3D kinematic model with a layered control architecture to investigate how sensorimotor delays constrain robustness of walking behavior in the fruit fly, Drosophila. Motivated by the anatomical architecture of insect locomotor control circuits, our model consists of three component layers: a neural network that generates realistic 3D joint kinematics for each leg, an optimal controller that executes the joint kinematics while accounting for delays, and an inter-leg coordinator. The model generates realistic simulated walking that resembles real fly walking kinematics and sustains walking even when subjected to unexpected perturbations, generalizing beyond its training data. However, we found that the models robustness to perturbations deteriorates when sensorimotor delay parameters exceed the physiological range. These results suggest that fly sensorimotor control circuits operate close to the temporal limit at which they can detect and respond to external perturbations. More broadly, we show how a modular, layered model architecture can be used to investigate physiological constraints on animal behavior.

neuroscience↗

bioRxiv2023

Modeling effects of variable preBötzinger Complex network topology and cellular properties on opioid-induced respiratory depression and recovery

The pre-Botzinger complex (preBotC), located in the medulla, is the essential rhythm-generating neural network for breathing. The actions of opioids on this network impair its ability to generate robust, rhythmic output, contributing to life-threatening opioid-induced respiratory depression (OIRD). The occurrence of OIRD varies across individuals and internal and external states, increasing the risk of opioid use, yet the mechanisms of this variability are largely unknown. In this study, we utilize a computational model of the preBotC to perform several in silico experiments exploring how differences in network topology and the intrinsic properties of preBotC neurons influence the sensitivity of the network rhythm to opioids. We find that rhythms produced by preBotC networks in silico exhibit variable responses to simulated opioids, similar to the preBotC network in vitro. This variability is primarily due to random differences in network topology and can be manipulated by imposed changes in network connectivity and intrinsic neuronal properties. Our results identify features of the preBotC network that may regulate its susceptibility to opioids. Significance StatementThe neural network in the brainstem that generates the breathing rhythm is disrupted by opioid drugs. However, this response can be surprisingly unpredictable. By constructing computational models of this rhythm-generating network, we illustrate how random differences in the distribution of biophysical properties and connectivity patterns within individual networks can predict their response to opioids, and we show how modulation of these network features can make breathing more susceptible or resistant to the effects of opioids.

neuroscience↗