Short-term memories link events separated in time, such as past sensation and future actions. Short-term memories are correlated with slow neural dynamics, including selective persistent activity, which can be maintained over seconds. In a delayed response task that requires short-term memory, neurons in the mouse anterior lateral motor cortex (ALM) show persistent activity that instructs future actions. To determine the principles that underlie this persistent activity, here we combined intracellular and extracellular electrophysiology with optogenetic perturbations and network modelling. We show that during the delay epoch, the activity of ALM neurons moved towards discrete end points that correspond to specific movement directions. These end points were robust to transient shifts in ALM activity caused by optogenetic perturbations. Perturbations occasionally switched the population dynamics to the other end point, followed by incorrect actions. Our results show that discrete attractor dynamics underlie short-term memory related to motor planning.

Each training step for a variational autoencoder (VAE) requires us to sample from the approximate posterior, so we usually choose simple (e.g. factorised) approximate posteriors in which sampling is an efficient computation that fully exploits GPU parallelism. However, such simple approximate posteriors are often insufficient, as they eliminate statistical dependencies in the posterior. While it is possible to use normalizing flow approximate posteriors for continuous latents, some problems have discrete latents and strong statistical dependencies. The most natural approach to model these dependencies is an autoregressive distribution, but sampling from such distributions is inherently sequential and thus slow. We develop a fast, parallel sampling procedure for autoregressive distributions based on fixed-point iterations which enables efficient and accurate variational inference in discrete state-space latent variable dynamical systems. To optimize the variational bound, we considered two ways to evaluate probabilities: inserting the relaxed samples directly into the pmf for the discrete distribution, or converting to continuous logistic latent variables and interpreting the K-step fixed-point iterations as a normalizing flow. We found that converting to continuous latent variables gave considerable additional scope for mismatch between the true and approximate posteriors, which resulted in biased inferences, we thus used the former approach. Using our fast sampling procedure, we were able to realize the benefits of correlated posteriors, including accurate uncertainty estimates for one cell, and accurate connectivity estimates for multiple cells, in an order of magnitude less time.

Animals smelling in the real world use a small number of receptors to sense a vast number of natural molecular mixtures, and proceed to learn arbitrary associations between odors and valences. Here, we propose how the architecture of olfactory circuits leverages disorder, diffuse sensing and redundancy in representation to meet these immense complementary challenges. First, the diffuse and disordered binding of receptors to many molecules compresses a vast but sparsely-structured odor space into a small receptor space, yielding an odor code that preserves similarity in a precise sense. Introducing any order/structure in the sensing degrades similarity preservation. Next, lateral interactions further reduce the correlation present in the low-dimensional receptor code. Finally, expansive disordered projections from the periphery to the central brain reconfigure the densely packed information into a high-dimensional representation, which contains multiple redundant subsets from which downstream neurons can learn flexible associations and valences. Moreover, introducing any order in the expansive projections degrades the ability to recall the learned associations in the presence of noise. We test our theory empirically using data from . Our theory suggests that the neural processing of sparse but high-dimensional olfactory information differs from the other senses in its fundamental use of disorder.

Signaling pathways induce stereotyped transcriptional changes as stem cells progress into mature cell types during embryogenesis. Signaling perturbations are necessary to discover which genes are responsive or insensitive to pathway activity. However, gene regulation is additionally dependent on cell state-specific factors like chromatin modifications or transcription factor binding. Thus, transcriptional profiles need to be assayed in single cells to identify potentially multiple, distinct perturbation responses among heterogeneous cell states in an embryo. In perturbation studies, comparing heterogeneous transcriptional states among experimental conditions often requires samples to be collected over multiple independent experiments. Datasets produced in such complex experimental designs can be confounded by batch effects. We present Design-Aware Integration of Single Cell ExpEriments (DAISEE), a new algorithm that models perturbation responses in single-cell datasets with a complex experimental design. We demonstrate that DAISEE improves upon a previously available integrative non-negative matrix factorization framework, more efficiently separating perturbation responses from confounding variation. We use DAISEE to integrate newly collected single-cell RNA-sequencing datasets from 5-hour old zebrafish embryos expressing optimized photoswitchable MEK (psMEK), which globally activates the extracellular signal-regulated kinase (ERK), a signaling molecule involved in many cell specification events. psMEK drives some cells that are normally not exposed to ERK signals towards other wild type states and induces novel states expressing a mixture of transcriptional programs, including precociously activated endothelial genes. ERK signaling is therefore capable of introducing profoundly new gene expression states in developing embryos.Significance Statement Signaling perturbations produce heterogeneous transcriptional responses that must be measured at the single-cell level. Data integration techniques allow us to model these responses which, however, can be confounded by batch effects. We present a computational tool (DAISEE) for extracting the common and perturbation-specific features of single-cell datasets representing multiple experimental conditions while achieving efficient batch effect correction. DAISEE outperforms its predecessor and will enable accurate analysis of a broad range of single-cell datasets. DAISEE applied to new single-cell RNA sequencing data from zebrafish embryos shows that gain-of-function signaling perturbations can induce novel states. Our analysis suggests that a wild type endothelial cell-specification program can be activated in abnormal developmental contexts when the extracellular signal-regulated kinase (ERK) pathway is deregulated.

To control reaching, the nervous system must generate large changes in muscle activation to drive the limb toward the target, and must also make smaller adjustments for precise and accurate behavior. Motor cortex controls the arm through projections to diverse targets across the central nervous system, but it has been challenging to identify the roles of cortical projections to specific targets. Here, we selectively disrupt cortico-cerebellar communication in the mouse by optogenetically stimulating the pontine nuclei in a cued reaching task. This perturbation did not typically block movement initiation, but degraded the precision, accuracy, duration, or success rate of the movement. Correspondingly, cerebellar and cortical activity during movement were largely preserved, but differences in hand velocity between control and stimulation conditions predicted from neural activity were correlated with observed velocity differences. These results suggest that while the total output of motor cortex drives reaching, the cortico-cerebellar loop makes small adjustments that contribute to the successful execution of this dexterous movement.

The neuropeptide eclosion hormone (EH) is a key regulator of insect ecdysis. We tested the role of the two EH-producing neurons in Drosophila by using an EH cell-specific enhancer to activate cell death genes reaper and head involution defective to ablate the EH cells. In the EH cell knockout flies, larval and adult ecdyses were disrupted, yet a third of the knockouts emerged as adults, demonstrating that EH has a significant but nonessential role in ecdysis. The EH cell knockouts had discrete behavioral deficits, including slow, uncoordinated eclosion and an insensitivity to ecdysis-triggering hormone. The knockouts lacked the lights-on eclosion response despite having a normal circadian eclosion rhythm. This study represents a novel approach to the dissection of neuropeptide regulation of a complex behavioral program.

Circadian (daily) rhythms are present in almost all plants and animals. In mammals, a brain clock located in the hypothalamic suprachiasmatic nucleus maintains synchrony between environmental light/dark cycles and physiology and behavior. Over the past 100 y, especially with the advent of electric lighting, modern society has resulted in a round-the-clock lifestyle, in which natural connections between rest/activity cycles and environmental light/dark cycles have been degraded or even broken. Instances in which rapid changes to sleep patterns are necessary, such as transmeridian air travel, demonstrate negative effects of acute circadian disruption on physiology and behavior. However, the ramifications of chronic disruption of the circadian clock for mental and physical health are not yet fully understood. By housing mice in 20-h light/dark cycles, incongruous with their endogenous ∼24-h circadian period, we were able to model the effects of chronic circadian disruption noninvasively. Housing in these conditions results in accelerated weight gain and obesity, as well as changes in metabolic hormones. In the brain, circadian-disrupted mice exhibit a loss of dendritic length and decreased complexity of neurons in the prelimbic prefrontal cortex, a brain region important in executive function and emotional control. Disrupted animals show decreases in cognitive flexibility and changes in emotionality consistent with the changes seen in neural architecture. How our findings translate to humans living and working in chronic circadian disruption is unknown, but we believe that this model can provide a foundation to understand how environmental disruption of circadian rhythms impacts the brain, behavior, and physiology.

Small molecules that alter protein function provide a means to modulate biological networks with temporal resolution. Here we demonstrate a potentially general and scalable method of identifying such molecules by application to a particular protein, Ure2p, which represses the transcription factors Gln3p and Nil1p. By probing a high-density microarray of small molecules generated by diversity-oriented synthesis with fluorescently labelled Ure2p, we performed 3,780 protein-binding assays in parallel and identified several compounds that bind Ure2p. One compound, which we call uretupamine, specifically activates a glucose-sensitive transcriptional pathway downstream of Ure2p. Whole-genome transcription profiling and chemical epistasis demonstrate the remarkable Ure2p specificity of uretupamine and its ability to modulate the glucose-sensitive subset of genes downstream of Ure2p. These results demonstrate that diversity-oriented synthesis and small-molecule microarrays can be used to identify small molecules that bind to a protein of interest, and that these small molecules can regulate specific functions of the protein.

In their classic experiments, Olds and Milner showed that rats learn to lever press to receive an electric stimulus in specific brain regions. This led to the identification of mammalian reward centers. Our interest in defining the neuronal substrates of reward perception in the fruit fly Drosophila melanogaster prompted us to develop a simpler experimental approach wherein flies could implement behavior that induces self-stimulation of specific neurons in their brains. The high-throughput assay employs optogenetic activation of neurons when the fly occupies a specific area of a behavioral chamber, and the flies' preferential occupation of this area reflects their choosing to experience optogenetic stimulation. Flies in which neuropeptide F (NPF) neurons are activated display preference for the illuminated side of the chamber. We show that optogenetic activation of NPF neuron is rewarding in olfactory conditioning experiments and that the preference for NPF neuron activation is dependent on NPF signaling. Finally, we identify a small subset of NPF-expressing neurons located in the dorsomedial posterior brain that are sufficient to elicit preference in our assay. This assay provides the means for carrying out unbiased screens to map reward neurons in flies.