Knowledge of connectivity in the nervous system is essential to understanding its function. Here we describe connectomes for both adult sexes of the nematode Caenorhabditis elegans, an important model organism for neuroscience research. We present quantitative connectivity matrices that encompass all connections from sensory input to end-organ output across the entire animal, information that is necessary to model behaviour. Serial electron microscopy reconstructions that are based on the analysis of both new and previously published electron micrographs update previous results and include data on the male head. The nervous system differs between sexes at multiple levels. Several sex-shared neurons that function in circuits for sexual behaviour are sexually dimorphic in structure and connectivity. Inputs from sex-specific circuitry to central circuitry reveal points at which sexual and non-sexual pathways converge. In sex-shared central pathways, a substantial number of connections differ in strength between the sexes. Quantitative connectomes that include all connections serve as the basis for understanding how complex, adaptive behavior is generated.

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Whole-animal connectomes of both Caenorhabditis elegans sexes

Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems, a deep learning method to infer latent dynamics from single-trial neural spiking data. When applied to a variety of macaque and human motor cortical datasets, latent factor analysis via dynamical systems accurately predicts observed behavioral variables, extracts precise firing rate estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics.

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Inferring single-trial neural population dynamics using sequential auto-encoders.

The human brain integrates diverse cognitive processes into a coherent whole, shifting fluidly as a function of changing environmental demands. Despite recent progress, the neurobiological mechanisms responsible for this dynamic system-level integration remain poorly understood. Here we investigated the spatial, dynamic, and molecular signatures of system-wide neural activity across a range of cognitive tasks. We found that neuronal activity converged onto a low-dimensional manifold that facilitates the execution of diverse task states. Flow within this attractor space was associated with dissociable cognitive functions, unique patterns of network-level topology, and individual differences in fluid intelligence. The axes of the low-dimensional neurocognitive architecture aligned with regional differences in the density of neuromodulatory receptors, which in turn relate to distinct signatures of network controllability estimated from the structural connectome. These results advance our understanding of functional brain organization by emphasizing the interface between neural activity, neuromodulatory systems, and cognitive function.

Human cognition involves the dynamic integration of neural activity and neuromodulatory systems

Global Representations of Goal-Directed Behavior in Distinct Cell Types of Mouse Neocortex.

Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected neural populations. An emerging challenge now is to uncover the nature of the associated computations, how they are implemented, and what role they play in driving behavior. We term this computation through neural population dynamics. If successful, this framework will reveal general motifs of neural population activity and quantitatively describe how neural population dynamics implement computations necessary for driving goal-directed behavior. Here, we start with a mathematical primer on dynamical systems theory and analytical tools necessary to apply this perspective to experimental data. Next, we highlight some recent discoveries resulting from successful application of dynamical systems. We focus on studies spanning motor control, timing, decision-making, and working memory. Finally, we briefly discuss promising recent lines of investigation and future directions for the computation through neural population dynamics framework.

Computation Through Neural Population Dynamics.

Global brain dynamics embed the motor command sequence of Caenorhabditis elegans.

Energy Scarcity Promotes a Brain-wide Sleep State Modulated by Insulin Signaling in C. elegans.

https://www.cell.com/cell-reports/pdf/S2211-1247(16)30030-4.pdf

C. elegans Body Cavity Neurons Are Homeostatic Sensors that Integrate Fluctuations in Oxygen Availability and Internal Nutrient Reserves.

A key finding of modern ageing research is that our limitation in lifespan is more than the result of accumulated organismal decay. Lifespan is regulated by genetically defined chemosensory and endocrine pathways, which integrate signals that reflect the internal and external status of the animal. New findings by Liu and Cai unravel a role for the environmental gases oxygen and carbon dioxide in the regulation of lifespan homeostasis and thus a novel function of oxygen‐chemosensory neurons in C. elegans .

Which factors define our lifetime? Along the quest for answers, the soil nematode C. elegans has become a popular model to address this question, as is demonstrated by the rapidly growing number of journal articles on lifespan regulation solely in C. elegan s. Importantly, due to evolutionary conservation of the implicated genes, these findings are transferable to higher organisms including humans.

Environmental circumstances such as nutrient availability and temperature directly feed into endocrine mechanisms that regulate lifespan. The importance of chemosensation in these processes has long been understood and efforts were focused on elucidating its various roles affecting longevity. For instance, ablating specific sensory neurons involved in the detection of stress and starvation pheromones causes pronounced effects on lifespan, both positive and negative (Alcedo and Kenyon, 2004). Specifically, functional loss of neuron classes called ASI or ASG promotes lifespan, an effect which is suppressed by ablation of other neuron classes called ASJ and ASK. This lifespan modulation was found to be dependent on hormonal signalling via the insulin receptor homologue DAF‐2, which negatively regulates the FOXO transcription factor DAF‐16.

The hypoxia‐inducible factor 1 (HIF‐1) transcriptional pathway presents another well‐described mechanism affecting longevity. Although predominantly known for its importance for survival of hypoxic conditions, HIF‐1 has gained recognition as …

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Susanne Skora

Papers

Global brain dynamics embed the motor command sequence of Caenorhabditis elegans.

Energy Scarcity Promotes a Brain-wide Sleep State Modulated by Insulin Signaling in C. elegans.

C. elegans Body Cavity Neurons Are Homeostatic Sensors that Integrate Fluctuations in Oxygen Availability and Internal Nutrient Reserves.

Life(span) in balance: oxygen fuels a sophisticated neural network for lifespan homeostasis in C. elegans.