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Learned olfactory-guided navigation is a powerful platform for studying how a brain generates goal-directed behaviors. However, the quantitative changes that occur in sensorimotor transformations and the underlying neural circuit substrates to generate such learning-dependent navigation is still unclear. Here we investigate learned sensorimotor processing for navigation in the nematodeCaenorhabditis elegansby measuring and modeling experience-dependent odor and salt chemotaxis. We then explore the neural basis of learned odor navigation through perturbation experiments. We develop a novel statistical model to characterize how the worm employs two behavioral strategies: a biased random walk and weathervaning. We infer weights on these strategies and characterize sensorimotor kernels that govern them by fitting our model to the worm’s time-varying navigation trajectories and precise sensory experiences. After olfactory learning, the fitted odor kernels reflect how appetitive and aversive trained worms up- and down-regulate both strategies, respectively. The model predicts an animal’s past olfactory learning experience with  > 90%accuracy given finite observations, outperforming a classical chemotaxis metric. The model trained on natural odors further predicts the animals’ learning-dependent response to optogenetically induced odor perception. Our measurements and model show that behavioral variability is altered by learning—trained worms exhibit less variable navigation than naive ones. Genetically disrupting individual interneuron classes downstream of an odor-sensing neuron reveals that learned navigation strategies are distributed in the network. Together, we present a flexible navigation algorithm that is supported by distributed neural computation in a compact brain. 

An animal’s current behavior influences its response to sensory stimuli, but the molecular and circuitlevel mechanisms of this context-dependent decision-making are not well understood. Caenorhabditis elegans are less likely to respond to a mechanosensory stimulus by reversing if the stimuli is received while the animal turns. Inhibitory feedback from turning associated neurons are needed for this gating. But until now, it has remained unknown precisely where in the circuit gating occurs and which specific neurons and receptors receive inhibition from the turning circuitry. Here, we use genetic manipulations, single-cell rescue experiments, and high-throughput closed-loop optogenetic perturbations during behavior to reveal the specific neuron and receptor responsible for receiving inhibition and altering sensorimotor processing. Our measurements show that an inhibitory acetylcholine-gated chloride channel comprising LGC-47 and ACC-1 expressed in neuron type RIM disrupts mechanosensory evoked reversals during turns, presumably in response to inhibitory signals from turning-associated neuron SAA. 

Abstract Establishing how neural function emerges from network properties is a fundamental problem in neuroscience¹. Here, to better understand the relationship between the structure and the function of a nervous system, we systematically measure signal propagation in 23,433 pairs of neurons across the head of the nematodeCaenorhabditis elegansby direct optogenetic activation and simultaneous whole-brain calcium imaging. We measure the sign (excitatory or inhibitory), strength, temporal properties and causal direction of signal propagation between these neurons to create a functional atlas. We find that signal propagation differs from model predictions that are based on anatomy. Using mutants, we show that extrasynaptic signalling not visible from anatomy contributes to this difference. We identify many instances of dense-core-vesicle-dependent signalling, including on timescales of less than a second, that evoke acute calcium transients—often where no direct wired connection exists but where relevant neuropeptides and receptors are expressed. We propose that, in such cases, extrasynaptically released neuropeptides serve a similar function to that of classical neurotransmitters. Finally, our measured signal propagation atlas better predicts the neural dynamics of spontaneous activity than do models based on anatomy. We conclude that both synaptic and extrasynaptic signalling drive neural dynamics on short timescales, and that measurements of evoked signal propagation are crucial for interpreting neural function. 

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. 

We present a high-throughput optogenetic illumination system capable of simultaneous closed-loop light delivery to specified targets in populations of moving Caenorhabditis elegans . The instrument addresses three technical challenges: It delivers targeted illumination to specified regions of the animal’s body such as its head or tail; it automatically delivers stimuli triggered upon the animal’s behavior; and it achieves high throughput by targeting many animals simultaneously. The instrument was used to optogenetically probe the animal’s behavioral response to competing mechanosensory stimuli in the the anterior and posterior gentle touch receptor neurons. Responses to more than 43,418 stimulus events from a range of anterior–posterior intensity combinations were measured. The animal’s probability of sprinting forward in response to a mechanosensory stimulus depended on both the anterior and posterior stimulation intensity, while the probability of reversing depended primarily on the anterior stimulation intensity. We also probed the animal’s response to mechanosensory stimulation during the onset of turning, a relatively rare behavioral event, by delivering stimuli automatically when the animal began to turn. Using this closed-loop approach, over 9,700 stimulus events were delivered during turning onset at a rate of 9.2 events per worm hour, a greater than 25-fold increase in throughput compared to previous investigations. These measurements validate with greater statistical power previous findings that turning acts to gate mechanosensory evoked reversals. Compared to previous approaches, the current system offers targeted optogenetic stimulation to specific body regions or behaviors with many fold increases in throughput to better constrain quantitative models of sensorimotor processing. 

Understanding the intricacies of the brain often requires spotting and tracking specific neurons over time and across different individuals. For instance, scientists may need to precisely monitor the activity of one neuron even as the brain moves and deforms; or they may want to find universal patterns by comparing signals from the same neuron across different individuals. Both tasks require matching which neuron is which in different images and amongst a constellation of cells. This is theoretically possible in certain ‘model’ animals where every single neuron is known and carefully mapped out. Still, it remains challenging: neurons move relative to one another as the animal changes posture, and the position of a cell is also slightly different between individuals. Sophisticated computer algorithms are increasingly used to tackle this problem, but they are far too slow to track neural signals as real-time experiments unfold. To address this issue, Yu et al. designed a new algorithm based on the Transformer, an artificial neural network originally used to spot relationships between words in sentences. To learn relationships between neurons, the algorithm was fed hundreds of thousands of ‘semi-synthetic’ examples of constellations of neurons. Instead of painfully collated actual experimental data, these datasets were created by a simulator based on a few simple measurements. Testing the new algorithm on the tiny worm Caenorhabditis elegans revealed that it was faster and more accurate, finding corresponding neurons in about 10ms. The work by Yu et al. demonstrates the power of using simulations rather than experimental data to train artificial networks. The resulting algorithm can be used immediately to help study how the brain of C. elegans makes decisions or controls movements. Ultimately, this research could allow brain-machine interfaces to be developed. 

We investigated the neural representation of locomotion in the nematode C. elegans by recording population calcium activity during movement. We report that population activity more accurately decodes locomotion than any single neuron. Relevant signals are distributed across neurons with diverse tunings to locomotion. Two largely distinct subpopulations are informative for decoding velocity and curvature, and different neurons’ activities contribute features relevant for different aspects of a behavior or different instances of a behavioral motif. To validate our measurements, we labeled neurons AVAL and AVAR and found that their activity exhibited expected transients during backward locomotion. Finally, we compared population activity during movement and immobilization. Immobilization alters the correlation structure of neural activity and its dynamics. Some neurons positively correlated with AVA during movement become negatively correlated during immobilization and vice versa. This work provides needed experimental measurements that inform and constrain ongoing efforts to understand population dynamics underlying locomotion in C. elegans . 

A worm called Caenorhabditis elegans has a nervous system made up of only 302 neurons, far fewer than the billions of cells that comprise our own brains. And yet these few hundred neurons are enough for these worms to detect and respond to their surroundings. C. elegans is thus a popular choice for studying how nervous systems process sensory information and use it to control behavior. Yet, most experiments to date have used only simple stimuli, such as taps or pokes, and studied a handful of behaviors, such as whether or not a worm stops moving or backs up. This limits the conclusions it has been possible to draw. Liu et al. therefore set out to determine how the worm’s nervous system responds to more complex stimuli. These included physical stimuli, such as taps on the side of the dish containing the worms, as well as simulated stimuli. To generate the latter, Liu et al. used a technique called optogenetics to directly activate the neurons in the worm’s body that would normally detect information from the senses, by simply shining a light on the worms. Doing so gives the worm the sensation of a physical stimulus, even though none was present. Liu et al. then used mathematics to examine the relationships between the stimuli and the worms’ responses. The results confirmed that worms usually respond to simple stimuli, such as taps on the side of their dish, by backing up. But they also revealed more advanced forms of stimulus processing. The worms responded differently to stimuli that increased over time versus decreased, for example. A worm's response to a stimulus also varied depending on what the worm was doing at the time. Worms that were in the middle of turns, for instance, ignored stimuli to which they would normally respond. This suggests that an animal’s current behavior influences how its nervous system interprets sensory information. The discovery of relatively sophisticated responses to sensory stimuli in C. elegans indicates that even simple nervous systems are capable of flexible sensory processing. This lays a foundation for understanding how neural circuits interpret sensory signals. Building on this work will ultimately help us understand how more complicated nervous systems interpret and respond to the world.