C. elegans

Rani Borbara presents brain-wide recordings from more than 150 freely moving C. elegans to examine how anatomical connectivity becomes functional organization during behavior. Cross-animal models map cell-class conditional dependencies and test how synapses, gap junctions, neuromodulation and shared brain states shape circuit dynamics.

…Drawing on his work with C. elegans, he explains research into how experiences can influence descendants through RNA-mediated inheritance. The conversation also covers ancient DNA and the Dead Sea Scrolls, automation in biology and the distinction between supporting researchers and replacing scientific judgment.

…The nematode worm C. elegans is known to exhibit distinct “steering” and “turning” strategies during navigation, where steering involves continuous, gradual changes in orientation, and turning involves infrequent, major reorientation events. It is commonly assumed that the worm’s responses to sensory input, whether steering or turning, are independent behavioral actio

Artifical neural network (ANN) approaches to motor control commonly adopt generic architectures like fully connected multi-layer perceptrons (MLPs) and recurrent neural networks (RNNs). As such architectures contain few inductive biases, it is common to require priors in the form of rewards, training curricula, imitation data, or trajectory generators. In nature, animals are born with priors in the form of their nervous system's architecture, which has been shaped by evolution to confer innate ability and efficient learning. For instance, a horse can walk within hours of birth and quickly impr

A fundamental problem in neuroscience is how neuronal activity in the brain generates organized and stable behaviors across multiple timescales. Recent research in many model organisms, e.g. in monkeys, led to the observations that the neuronal encoding of individual actions, such as preparation for and execution of arm movements, is achieved by large neuronal populations but often lies on low-dimensional manifolds. However, it is largely unclear if such manifolds also coordinate higher dimensional activity patterns that correspond to simultaneously executed fine-tuned movements. In order to a

A long-standing premise in theoretical neuroscience holds that the complex interactions between neurons or, at a different systems level, between networks in the nervous system can be expressed in terms of an underlying system of nonlinear dynamics. A classical approach has been dynamical systems theory, whereby biophysical processes in the brain can be naturally formalized in terms of differential equations (ODEs) that may be capable of offering an explicit and interpretable account of how different biochemical and physiological processes in the brain evolve to produce the complex repertoire

Understanding how neuronal populations interact to process information and generate behaviour is a central goal of neuroscience. This can be challenging due to the high dimensionality of the neuronal populations, the dense interactions between them and the unobserved factors that influence the dynamics. The neuronal manifold hypothesis tackles high dimensionality by positing that the relevant dynamics occurs on a lower-dimensional manifold. While the neuronal manifold may be useful in visualising global dynamics, it tells us very little about how various subsystems interact to produce these dy

…experiments in the nematode worm C. elegans. These source datasets, while collected under different experimental protocols, all measure neural activity via changes in calcium fluorescence in labeled subsets of the hermaphrodite worm’s 300 neurons. Our contribution is to standardize these disparate datasets into a unified framework including ordering the neural data from labeled neurons consistently, resampling traces to a common timestep, and including a feature mask to indicate which neurons were labeled in e

A fundamental problem in neuroscience is how neuronal activity in the brain generates organized and stable behaviors at multiple timescales. Recent research in many model organisms, e.g. in monkeys, led to the observations that the neuronal encoding of individual actions is achieved by large neuronal populations but often lies on low-dimensional manifolds. However, it is unclear if such manifolds extend beyond the single-action level into coordinating entire action sequences or fine-tuned movements. In order to address these questions, we established a whole brain single-cell resolution record

…were conducted in transgenic C. elegans.Results: T

…Previously, whole-brain recordings in C. elegans uncovered that a large proportion of neuronal activity relates to motor commands. However, the role of these distributed motor commands remains elusive.Here, we investigate changes in a C. elegans motor behavior and the corresponding distributed dynamics upon an environmental change. We find that environments that make movement more difficult provoke longer backwards movement, so-called “reversals”. Importantly, this prolongati

Gaucher’s disease (GD) is a genetic disorder characterized by loss of glucocerebrosidase activity in cells resulting in accumulation of glucocerebroside. Mutation of GBA1, which encodes the lysosomal glucocerebrosidase (GCase) in human, will lead to GD whose symptoms include deficits in liver function, skeletal disorders, and in some cases severe neurological complications. Recent human genome sequencing studies have demonstrated that genetic variations in GBA1 constitute the largest known risk factor for Parkinson’s disease. To better understand the functional consequence of glucocerebrosidas

The value of being able to adapt lies in the potential of an animal to navigate and thrive in an ever-changing environment. In order to survive, animals need to encode experiences molecularly and modulate their behavior to adverse or favorable conditions. Neuropeptides are a well-known, diverse class of molecular messengers that regulate neuronal activity synaptically and non-synaptically to modulate behavior. Neuropeptides and their receptors constitute a widespread signaling network, but how these networks are functionally organized and modulate behavior remains largely unexplored. The nemat

Konrad Kording discusses what it would take to reverse engineer the nervous system of C. elegans and what this could teach neuroscience.

…The nematode C. elegans modulates its preference towards an odor source depending on its pairing with food or starvation. However, precisely how the worm alters its navigation strategy in response to olfactory learning has not been characterized, in part due to challenges with measuring the exact odor cues in the environment. Here, we used a novel apparatus and optogenetics to measure and then model learned odor-navigation strategies in worms. Specifically, we modelled navigation using a Mixture of

…activity and behavior of freely-moving C. elegans, wrote new image analysis software, and developed a probabilistic encoder model to delineate how each neuron encodes specific behavioral features. Extr

…which we apply to freely moving C. elegans. For a traditional CNN to track neurons across i

Recent work has indicated that functional connections between C. elegans neurons can be clustered by their role in behavior, i.e. forward vs. reverse crawling vs. turning (Linderman et al. 2019). Furthermore they show how data-driven dynamical systems, e.g., the recurrent switching linear dynamical systems (rSLDS), can be learned in an unsupervised way from whole-brain recordings (Kato et al. 2015). However, these approaches ascribe a discrete overall state to the system at each time point, potentially missing different dimensions of variability in the data. For example, such models cannot fle

…from a food-responsive neuron in C. elegans alters neural activity at whole-brain scale to induce adaptive behaviors, such slow locomotion and increased pharyngeal pumping. A comprehensive in vivo genetic analysis of the six C. elegans serotonin receptors identifies three core serotonin receptors that sense serotonin re

All behaviors, even simple actions, result from the interplay of multiple neural computations, and understanding how a given behavior is generated requires delineating these computations from one another. Traditionally, identifying these critical ‘computational building blocks’ requires a researcher to impose structure on the data: specific task epochs are analyzed separately or specific population-level structure is sought using supervised dimensionality reduction methods. While these approaches can be successful, they have obvious drawbacks – many behaviors cannot be easily parsed into disti

…processing in the nematode C. elegans. When C. elegans detects gentle touch from the environment, it usually responds by moving backward, also known as reversals. The likelihood o

…motor programs of freely-moving C. elegans and built probabilistic models that explain how each neuron encodes quantitative features of the animal’s behavior. By determining the identities of the recorded neurons, we created, for the first time, an atlas of how the defined neuron classes in the C. elegans connectome encod

…different developmental stages in C. elegans allows for characterization of their development. We present a class

Switching linear dynamical system (SLDS) models are widely used for modeling neural and behavioral time series data [Glaser et al., 2020, Linderman et al., 2019, Nair et al., 2022, Zoltowski et al., 2022]. These models characterize high-dimensional time series through a combination of discrete and continuous latent states. The continuous states offer a low-dimensional representation of the data, while the discrete state defines how the continuous states evolve over time. Together, they provide quantitative and interpretable summaries of multidimensional time series. Unfortunately, fitting thes