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The compact nervous system of the nematode Caenorhabditis elegans makes it a powerful playground to study how neural dynamics constrained by neuroanatomy generate neural function and behavior. The ability to record neural activity from the whole brain simultaneously in this worm has opened several research avenues and is providing insights into brain-wide neural coding of locomotion, sleep, and other behaviors. We review these findings and the development of new methods, including new microscopes, new genetic tools, and new modeling approaches. We conclude with a discussion of the role of theory in interpreting or driving new experiments in C. elegans and potential paths forward.

Neuroscientists today can measure activity from more neurons than ever before, and are facing the challenge of connecting these brain-wide neural recordings to computation and behavior. Here, we first describe emerging tools and technologies being used to probe large-scale brain activity and new approaches to characterize behavior in the context of such measurements. We next highlight insights obtained from large-scale neural recordings in diverse model systems, and argue that some of these pose a challenge to traditional theoretical frameworks. Finally, we elaborate on existing modelling frameworks to interpret these data, and argue that interpreting brain-wide neural recordings calls for new theoretical approaches that may depend on the desired level of understanding at stake. These advances in both neural recordings and theory development will pave the way for critical advances in our understanding of the brain.

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.

The activity of an animal’s brain contains information about that animal’s actions and movements. We investigated the neural representation of locomotion in the nematode C. elegans by recording population calcium activity during unrestrained movement. We report that a neural population more accurately decodes locomotion than any single neuron. Relevant signals are distributed across neurons with diverse tunings to locomotion. Two distinct subpopulations are informative for decoding velocity and body curvature, and different neurons’ activities contribute features relevant for different instances of behavioral motifs. We labeled neurons AVAL and AVAR and found their activity was highly correlated with one another. They exhibited expected transients during backward locomotion, although they were not always the most informative neurons for decoding velocity. Finally, we compared population neural activity during movement and immobilization. Immobilization alters the correlation structure of neural activity and its dynamics. Some neurons previously correlated with AVA become anti-correlated and vice versa.The activity of an animal’s brain contains information about that animal’s actions and movements. We investigated the neural representation of locomotion in the nematode C. elegans by recording brain-wide neural dynamics in freely moving animals. We report that a population of neurons more accurately decodes the animal’s locomotion than any single neuron. Neural signals are distributed across neurons in the population with a diversity of tuning to locomotion. Two distinct subpopulations are most informative for decoding velocity and body curvature, and different neurons’ activities contribute features relevant for different instances of behavioral motifs within these subpopulations. We additionally labeled the AVA neurons within our population recordings. AVAL and AVAR exhibit activity that is highly correlated with one another, and they exhibit the expected responses to locomotion, although we find that AVA is not always the most informative neuron for decoding velocity. Finally, we compared brain-wide neural activity during movement and immobilization and observe that immobilization alters the correlation structure of neural activity and its dynamics. Some neurons that were previously correlated with AVA become anti-correlated and vice versa during immobilization. We conclude that neural population codes are important for understanding neural dynamics of behavior in moving animals.

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.

Neuroscientists today can measure activity from more neurons than ever before, and are facing the challenge of connecting these brain-wide neural recordings to computation and behavior. In the present review, we first describe emerging tools and technologies being used to probe large-scale brain activity and new approaches to characterize behavior in the context of such measurements. We next highlight insights obtained from large-scale neural recordings in diverse model systems, and argue that some of these pose a challenge to traditional theoretical frameworks. Finally, we elaborate on existing modeling frameworks to interpret these data, and argue that the interpretation of brain-wide neural recordings calls for new theoretical approaches that may depend on the desired level of understanding. These advances in both neural recordings and theory development will pave the way for critical advances in our understanding of the brain.

The brain is worthy of study because it is in charge of behavior. A flurry of recent technical advances in measuring and quantifying naturalistic behaviors provide an important opportunity for advancing brain science. However, the problem of understanding unrestrained behavior in the context of neural recordings and manipulations remains unsolved, and developing approaches to addressing this challenge is critical. Here we discuss considerations in computational neuroethology-the science of quantifying naturalistic behaviors for understanding the brain-and propose strategies to evaluate progress. We point to open questions that require resolution and call upon the broader systems neuroscience community to further develop and leverage measures of naturalistic, unrestrained behavior, which will enable us to more effectively probe the richness and complexity of the brain.

A quantitative understanding of how sensory signals are transformed into motor outputs places useful constraints on brain function and helps to reveal the brain’s underlying computations. We investigate how the nematode Caenorhabditis elegans responds to time-varying mechanosensory signals using a high-throughput optogenetic assay and automated behavior quantification. We find that the behavioral response is tuned to temporal properties of mechanosensory signals, such as their integral and derivative, that extend over many seconds. Mechanosensory signals, even in the same neurons, can be tailored to elicit different behavioral responses. Moreover, we find that the animal’s response also depends on its behavioral context. Most dramatically, the animal ignores all tested mechanosensory stimuli during turns. Finally, we present a linear-nonlinear model that predicts the animal’s behavioral response to stimulus., eLife digest 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.

In large neuronal networks, it is believed that functions emerge through the collective behavior of many interconnected neurons. Recently, the development of experimental techniques that allow simultaneous recording of calcium concentration from a large fraction of all neurons in Caenorhabditis elegans - a nematode with 302 neurons - creates the opportunity to ask if such emergence is universal, reaching down to even the smallest brains. Here, we measure the activity of 50+ neurons in C. elegans, and analyze the data by building the maximum entropy model that matches the mean activity and pairwise correlations among these neurons. To capture the graded nature of the cells' responses, we assign each cell multiple states. These models, which are equivalent to a family of Potts glasses, successfully predict higher statistical structure in the network. In addition, these models exhibit signatures of collective behavior: the state of single cells can be predicted from the state of the rest of the network; the network, despite being sparse in a way similar to the structural connectome, distributes its response globally when locally perturbed; the distribution over network states has multiple local maxima, as in models for memory; and the parameters that describe the real network are close to a critical surface in this family of models., Comment: 14 pages, 12 figures

The identification and differentiation of a large number of distinct molecular species with high temporal and spatial resolution is a major challenge in biomedical science. Fluorescence microscopy is a powerful tool, but its multiplexing ability is limited by the number of spectrally distinguishable fluorophores. Here, we used (deoxy)ribonucleic acid (DNA)-origami technology to construct submicrometre nanorods that act as fluorescent barcodes. We demonstrate that spatial control over the positioning of fluorophores on the surface of a stiff DNA nanorod can produce 216 distinct barcodes that can be decoded unambiguously using epifluorescence or total internal reflection fluorescence microscopy. Barcodes with higher spatial information density were demonstrated via the construction of super-resolution barcodes with features spaced by ∼40 nm. One species of the barcodes was used to tag yeast surface receptors, which suggests their potential applications as in situ imaging probes for diverse biomolecular and cellular entities in their native environments.

We present an optogenetic illumination system capable of real-time light delivery with high spatial resolution to specified targets in freely moving Caenorhabditis elegans. A tracking microscope records the motion of an unrestrained worm expressing Channelrhodopsin-2 or Halorhodopsin/NpHR in specific cell types. Image processing software analyzes the worm’s position within each video frame, rapidly estimates the locations of targeted cells, and instructs a digital micromirror device to illuminate targeted cells with laser light of the appropriate wavelengths to stimulate or inhibit activity. Since each cell in an unrestrained worm is a rapidly moving target, our system operates at high speed (~50 frames per second) to provide high spatial resolution (~30 µm). To demonstrate the accuracy, flexibility, and utility of our system, we present optogenetic analyses of the worm motor circuit, egg-laying circuit, and mechanosensory circuits that were not possible with previous methods.

The ability to acquire large-scale recordings of neuronal activity in awake and unrestrained animals is needed to provide new insights into how populations of neurons generate animal behavior. We present an instrument capable of recording intracellular calcium transients from the majority of neurons in the head of a freely behaving Caenorhabditis elegans with cellular resolution while simultaneously recording the animal’s position, posture, and locomotion. This instrument provides whole-brain imaging with cellular resolution in an unrestrained and behaving animal. We use spinning-disk confocal microscopy to capture 3D volumetric fluorescent images of neurons expressing the calcium indicator GCaMP6s at 6 head-volumes/s. A suite of three cameras monitor neuronal fluorescence and the animal’s position and orientation. Custom software tracks the 3D position of the animal’s head in real time and two feedback loops adjust a motorized stage and objective to keep the animal’s head within the field of view as the animal roams freely. We observe calcium transients from up to 77 neurons for over 4 min and correlate this activity with the animal’s behavior. We characterize noise in the system due to animal motion and show that, across worms, multiple neurons show significant correlations with modes of behavior corresponding to forward, backward, and turning locomotion.

In secondary ion mass spectrometry measurement systems, the count rate of isotopes may vary in time as a particle is consumed during the analysis. Since isotopes are measured sequentially, this drift can introduce systematic error into the estimate of the ratio of any two isotopes. We correct the measurements for drift by aligning the time series of isotopic pairs using a linear interpolation approach. We estimate an isotopic ratio for each of two cases. In one case the time series of the more abundant isotope is aligned with respect to the time series of the less abundant isotope. In the second case the less abundant isotope is aligned with respect to the more abundant one. We average both of these estimates to get a drift-corrected estimate. We present an analytical formula for the random uncertainty of the isotopic ratio that accounts for correlation introduced by interpolation. We also present an approximate hypothesis test procedure to detect and quantify possible temporal variation of the measured isotopic ratio during a single analysis. In a Monte Carlo study, the performance of the methods is quantified based on analysis of simulated data with complexity similar to that of real data generated by a secondary ion mass spectrometer.

A fundamental goal of systems neuroscience is to probe the dynamics of neural activity that drive behavior. Here we present an instrument to simultaneously manipulate neural activity via Channelrhodopsin, monitor neural response via GCaMP3, and observes behavior in freely moving C. elegans. We use the instrument to directly observe the relation between sensory stimuli, interneuron activity and locomotion in the mechanosensory circuit. Now published as: Front Neural Circuits 8:28, doi:10.3389/fncir.2014.00028

The emerging field of optogenetics allows for optical activation or inhibition of neurons and other tissue in the nervous system. In 2005 optogenetic proteins were expressed in the nematode C. elegans for the first time. Since then, C. elegans has served as a powerful platform upon which to conduct optogenetic investigations of synaptic function, circuit dynamics and the neuronal basis of behavior. The C. elegans nervous system, consisting of 302 neurons, whose connectivity and morphology has been mapped completely, drives a rich repertoire of behaviors that are quantifiable by video microscopy. This model organism's compact nervous system, quantifiable behavior, genetic tractability and optical accessibility make it especially amenable to optogenetic interrogation. Channelrhodopsin-2 (ChR2), halorhodopsin (NpHR/Halo) and other common optogenetic proteins have all been expressed in C. elegans. Moreover recent advances leveraging molecular genetics and patterned light illumination have now made it possible to target photoactivation and inhibition to single cells and to do so in worms as they behave freely. Here we describe techniques and methods for optogenetic manipulation in C. elegans. We review recent work using optogenetics and C. elegans for neuroscience investigations at the level of synapses, circuits and behavior., 28 pages, 3 figures. Accepted for publication in Biology of the Cell. Available from publisher at http://onlinelibrary.wiley.com/doi/10.1111/boc.201200069/abstract Princeton University's Open Access Policy is at http://www.princeton.edu/dof/policies/publ/fac/open-access-policy/

Understanding how an organism's nervous system transforms sensory input into behavioral outputs requires recording and manipulating its neural activity during unrestrained behavior. Here we present an instrument to simultaneously monitor and manipulate neural activity while observing behavior in a freely moving textit{Caenorhabditis elegans}. Neural activity is recorded optically from cells expressing a calcium indicator, GCaMP3. Neural activity is manipulated optically by illuminating targeted neurons expressing the optogenetic protein Channelrhodopsin. Real-time computer vision software tracks the animal's behavior and identifies the location of targeted neurons in the nematode as it crawls. Patterned illumination from a digital micromirror device is used to selectively illuminate subsets of neurons for either calcium imaging or optogenetic stimulation. Real-time computer vision software constantly updates the illumination pattern in response to the worm's movement and thereby allows for independent optical recording or activation of different neurons in the worm as it moves freely. We use the instrument to directly observe the relationship between sensory neuron activation, interneuron dynamics and locomotion in the worm's mechanosensory circuit. We record and compare calcium transients in the backward locomotion command interneurons AVA, in response to optical activation of the anterior mechanosensory neurons ALM, AVM or both.

SummaryLocomotion requires coordinated motor activity throughout an animal’s body. In both vertebrates and invertebrates, chains of coupled central pattern generators (CPGs) are commonly evoked to explain local rhythmic behaviors. In C. elegans, we report that proprioception within the motor circuit is responsible for propagating and coordinating rhythmic undulatory waves from head to tail during forward movement. Proprioceptive coupling between adjacent body regions transduces rhythmic movement initiated near the head into bending waves driven along the body by a chain of reflexes. Using optogenetics and calcium imaging to manipulate and monitor motor circuit activity of moving C. elegans held in microfluidic devices, we found that the B-type cholinergic motor neurons transduce the proprioceptive signal. In C. elegans, a sensorimotor feedback loop operating within a specific type of motor neuron both drives and organizes body movement.

A self assembling DNA nanostructure is proposed that will permit expansion and contraction in only one dimension. This actuator restricts two rigid platforms, each in the shape of a hexagonal disk of radius 9 nanometers and of depth 14 nanometers, to move apart from one another with separations between 4 and 18 nanometers. Designed to resist bending, shearing, or torsional stresses, the actuator is composed entirely of DNA and can be self-assembled from two single-stranded DNA plasmids and hundreds of single-stranded DNA oligonucleotides. The actuator is illustrated in the accompanying figure where DNA double helices are represented as cylinders. A detailed schematic is presented and preliminary findings are shown. The creation of nanometer-sized structures that can undergo constrained motion is a critical step towards building nano-machines.View Large Image | View Hi-Res Image | Download PowerPoint Slide