The ability of animals to effectively locate and navigate toward food sources is central for survival. Here, using C. elegans nematodes, we reveal the neural mechanism underlying efficient navigation in chemical gradients. This mechanism relies on the activity of two types of chemosensory neurons: one (AWA) coding gradients via stochastic pulsatile dynamics, and the second (AWCON) coding the gradients deterministically in a graded manner. The pulsatile dynamics of the AWA neuron adapts to the magnitude of the gradient derivative, allowing animals to take trajectories better oriented toward the target. The robust response of AWCON to negative derivatives promotes immediate turns, thus alleviating the costs incurred by erroneous turns dictated by the AWA neuron. This mechanism empowers an efficient navigation strategy that outperforms the classical biased-random walk strategy. This general mechanism thus may be applicable to other sensory modalities for efficient gradient-based navigation.
It is well established that inducible transcription is essential for the consolidation of salient experiences into long-term memory. However, whether inducible transcription relays information about the identity and affective attributes of the experience being encoded, has not been explored. To this end, we analyzed transcription induced by a variety of rewarding and aversive experiences, across multiple brain regions. Our results describe the existence of robust transcriptional signatures uniquely representing distinct experiences, enabling near-perfect decoding of recent experiences. Furthermore, experiences with shared attributes display commonalities in their transcriptional signatures, exemplified in the representation of valence, habituation and reinforcement. This study introduces the concept of a neural transcriptional code, which represents the encoding of experiences in the mouse brain. This code is comprised of distinct transcriptional signatures that correlate to attributes of the experiences that are being committed to long-term memory.
BackgroundAnimals exhibit astonishingly complex behaviors. Studying the subtle features of these behaviors requires quantitative, high-throughput, and accurate systems that can cope with the often rich perplexing data.ResultsHere, we present a Multi-Animal Tracker (MAT) that provides a user-friendly, end-to-end solution for imaging, tracking, and analyzing complex behaviors of multiple animals simultaneously. At the core of the tracker is a machine learning algorithm that provides immense flexibility to image various animals (e.g., worms, flies, zebrafish, etc.) under different experimental setups and conditions. Focusing on C. elegans worms, we demonstrate the vast advantages of using this MAT in studying complex behaviors. Beginning with chemotaxis, we show that approximately 100 animals can be tracked simultaneously, providing rich behavioral data. Interestingly, we reveal that worms’ directional changes are biased, rather than random – a strategy that significantly enhances chemotaxis performance. Next, we show that worms can integrate environmental information and that directional changes mediate the enhanced chemotaxis towards richer environments. Finally, offering high-throughput and accurate tracking, we show that the system is highly suitable for longitudinal studies of aging- and proteotoxicity-associated locomotion deficits, enabling large-scale drug and genetic screens.ConclusionsTogether, our tracker provides a powerful and simple system to study complex behaviors in a quantitative, high-throughput, and accurate manner.Electronic supplementary materialThe online version of this article (doi:10.1186/s12915-017-0363-9) contains supplementary material, which is available to authorized users.
A major goal of systems neuroscience is to decipher the structure-function relationship in neural networks. Here we study network functionality in light of the common-neighbor-rule (CNR) in which a pair of neurons is more likely to be connected the more common neighbors it shares. Focusing on the fully-mapped neural network of C. elegans worms, we establish that the CNR is an emerging property in this connectome. Moreover, sets of common neighbors form homogenous structures that appear in defined layers of the network. Simulations of signal propagation reveal their potential functional roles: signal amplification and short-term memory at the sensory/inter-neuron layer, and synchronized activity at the motoneuron layer supporting coordinated movement. A coarse-grained view of the neural network based on homogenous connected sets alone reveals a simple modular network architecture that is intuitive to understand. These findings provide a novel framework for analyzing larger, more complex, connectomes once these become available.
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