A sensitivity of the circadian clock to light/dark cycles ensures that biological rhythms maintain optimal phase relationships with the external day. In animals, the circadian clock neuron network (CCNN) driving sleep/activity rhythms receives light input from multiple photoreceptors, but how these photoreceptors modulate CCNN components is not well understood. Here we show that the HofbauerBuchner eyelets differentially modulate two classes of ventral lateral neurons (LNvs) within the Drosophila CCNN. The eyelets antagonize Cryptochrome (CRY)-and compound-eye-based photoreception in the large LNvs while synergizing CRY-mediated photoreception in the small LNvs. Furthermore, we show that the large LNvs interact with subsets of "evening cells" to adjust the timing of the evening peak of activity in a day length-dependent manner. Our work identifies a peptidergic connection between the large LNvs and a group of evening cells that is critical for the seasonal adjustment of circadian rhythms.
Circadian clocks coordinate behaviour, physiology and metabolism with Earth's diurnal cycle. These clocks entrain to both light and temperature cycles, and daily environmental temperature oscillations probably contribute to human sleep patterns. However, the neural mechanisms through which circadian clocks monitor environmental temperature and modulate behaviour remain poorly understood. Here we elucidate how the circadian clock neuron network of Drosophila melanogaster processes changes in environmental temperature. In vivo calcium-imaging techniques demonstrate that the posterior dorsal neurons 1 (DN1s), which are a discrete subset of sleep-promoting clock neurons, constantly monitor modest changes in environmental temperature. We find that these neurons are acutely inhibited by heating and excited by cooling; this is an unexpected result when considering the strong correlation between temperature and light, and the fact that light excites clock neurons. We demonstrate that the DN1s rely on peripheral thermoreceptors located in the chordotonal organs and the aristae. We also show that the DN1s and their thermosensory inputs are required for the normal timing of sleep in the presence of naturalistic temperature cycles. These results identify the DN1s as a major gateway for temperature sensation into the circadian neural network, which continuously integrates temperature changes to coordinate the timing of sleep and activity.
Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here, we describe a software toolbox—called seqNMF—with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral data sets. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs.
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