Dynamic Ligand Discrimination in the Notch Signaling Pathway.

The Notch signaling pathway comprises multiple ligands that are used in distinct biological contexts. In principle, different ligands could activate distinct target programs in signal-receiving cells, but it is unclear how such ligand discrimination could occur. Here, we show that cells use dynamics to discriminate signaling by the ligands Dll1 and Dll4 through the Notch1 receptor. Quantitative single-cell imaging revealed that Dll1 activates Notch1 in discrete, frequency-modulated pulses that specifically upregulate the Notch target gene Hes1. By contrast, Dll4 activates Notch1 in a sustained, amplitude-modulated manner that predominantly upregulates Hey1 and HeyL. Ectopic expression of Dll1 or Dll4 in chick neural crest produced opposite effects on myogenic differentiation, showing that ligand discrimination can occur in vivo. Finally, analysis of chimeric ligands suggests that ligand-receptor clustering underlies dynamic encoding of ligand identity. The ability of the pathway to utilize ligands as distinct communication channels has implications for diverse Notch-dependent processes.

Sensory Processing at Ribbon Synapses in the Retina and the Cochlea.

In recent years, sensory neuroscientists have made major efforts to dissect the structure and function of ribbon synapses which process sensory information in the eye and ear. This review aims to summarize our current understanding of two key aspects of ribbon synapses: 1) their mechanisms of exocytosis and endocytosis and 2) their molecular anatomy and physiology. Our comparison of ribbon synapses in the cochlea and the retina reveals convergent signaling mechanisms, as well as divergent strategies in different sensory systems.

Gene-Environment Interactions: My Unique Journey.

I am deeply honored to be invited to write this scientific autobiography. As a physician-scientist, pediatrician, molecular biologist, and geneticist, I have authored/coauthored more than 600 publications in the fields of clinical medicine, biochemistry, biophysics, pharmacology, drug metabolism, toxicology, molecular biology, cancer, standardized gene nomenclature, developmental toxicology and teratogenesis, mouse genetics, human genetics, and evolutionary genomics. Looking back, I think my career can be divided into four distinct research areas, which I summarize mostly chronologically in this article: (a) discovery and characterization of the AHR/CYP1 axis, (b) pharmacogenomics and genetic prediction of response to drugs and other environmental toxicants, (c) standardized drug-metabolizing gene nomenclature based on evolutionary divergence, and (d) discovery and characterization of the SLC39A8 gene encoding the ZIP8 metal cation influx transporter. Collectively, all four topics embrace gene-environment interactions, hence the title of my autobiography.

Peeling the Onion of Brain Representations.

The brain's function is to enable adaptive behavior in the world. To this end, the brain processes information about the world. The concept of representation links the information processed by the brain back to the world and enables us to understand what the brain does at a functional level. The appeal of making the connection between brain activity and what it represents has been irresistible to neuroscience, despite the fact that representational interpretations pose several challenges: We must define which aspects of brain activity matter, how the code works, and how it supports computations that contribute to adaptive behavior. It has been suggested that we might drop representational language altogether and seek to understand the brain, more simply, as a dynamical system. In this review, we argue that the concept of representation provides a useful link between dynamics and computational function and ask which aspects of brain activity should be analyzed to achieve a representational understanding. We peel the onion of brain representations in search of the layers (the aspects of brain activity) that matter to computation. The article provides an introduction to the motivation and mathematics of representational models, a critical discussion of their assumptions and limitations, and a preview of future directions in this area.

Eye blinks as a visual processing stage.

Humans blink their eyes frequently during normal viewing, more often than it seems necessary for keeping the cornea well lubricated. Since the closure of the eyelid disrupts the image on the retina, eye blinks are commonly assumed to be detrimental to visual processing. However, blinks also provide luminance transients rich in spatial information to neural pathways highly sensitive to temporal changes. Here, we report that the luminance modulations from blinks enhance visual sensitivity. By coupling high-resolution eye tracking in human observers with modeling of blink transients and spectral analysis of visual input signals, we show that blinking increases the power of retinal stimulation and that this effect significantly enhances visibility despite the time lost in exposure to the external scene. We further show that, as predicted from the spectral content of input signals, this enhancement is selective for stimuli at low spatial frequencies and occurs irrespective of whether the luminance transients are actively generated or passively experienced. These findings indicate that, like eye movements, blinking acts as a computational component of a visual processing strategy that uses motor behavior to reformat spatial information into the temporal domain.

Population encoding of stimulus features along the visual hierarchy.

The retina and primary visual cortex (V1) both exhibit diverse neural populations sensitive to diverse visual features. Yet it remains unclear how neural populations in each area partition stimulus space to span these features. One possibility is that neural populations are organized into discrete groups of neurons, with each group signaling a particular constellation of features. Alternatively, neurons could be continuously distributed across feature-encoding space. To distinguish these possibilities, we presented a battery of visual stimuli to the mouse retina and V1 while measuring neural responses with multi-electrode arrays. Using machine learning approaches, we developed a manifold embedding technique that captures how neural populations partition feature space and how visual responses correlate with physiological and anatomical properties of individual neurons. We show that retinal populations discretely encode features, while V1 populations provide a more continuous representation. Applying the same analysis approach to convolutional neural networks that model visual processing, we demonstrate that they partition features much more similarly to the retina, indicating they are more like big retinas than little brains.

Modeling and dissociation of intrinsic and input-driven neural population dynamics underlying behavior.

Neural dynamics can reflect intrinsic dynamics or dynamic inputs, such as sensory inputs or inputs from other brain regions. To avoid misinterpreting temporally structured inputs as intrinsic dynamics, dynamical models of neural activity should account for measured inputs. However, incorporating measured inputs remains elusive in joint dynamical modeling of neural-behavioral data, which is important for studying neural computations of behavior. We first show how training dynamical models of neural activity while considering behavior but not input or input but not behavior may lead to misinterpretations. We then develop an analytical learning method for linear dynamical models that simultaneously accounts for neural activity, behavior, and measured inputs. The method provides the capability to prioritize the learning of intrinsic behaviorally relevant neural dynamics and dissociate them from both other intrinsic dynamics and measured input dynamics. In data from a simulated brain with fixed intrinsic dynamics that performs different tasks, the method correctly finds the same intrinsic dynamics regardless of the task while other methods can be influenced by the task. In neural datasets from three subjects performing two different motor tasks with task instruction sensory inputs, the method reveals low-dimensional intrinsic neural dynamics that are missed by other methods and are more predictive of behavior and/or neural activity. The method also uniquely finds that the intrinsic behaviorally relevant neural dynamics are largely similar across the different subjects and tasks, whereas the overall neural dynamics are not. These input-driven dynamical models of neural-behavioral data can uncover intrinsic dynamics that may otherwise be missed.

Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction.

One major question in neuroscience is how to relate connectomes to neural activity, circuit function, and learning. We offer an answer in the peripheral olfactory circuit of the Drosophila larva, composed of olfactory receptor neurons (ORNs) connected through feedback loops with interconnected inhibitory local neurons (LNs). We combine structural and activity data and, using a holistic normative framework based on similarity-matching, we formulate biologically plausible mechanistic models of the circuit. In particular, we consider a linear circuit model, for which we derive an exact theoretical solution, and a nonnegative circuit model, which we examine through simulations. The latter largely predicts the ORN [Formula: see text] LN synaptic weights found in the connectome and demonstrates that they reflect correlations in ORN activity patterns. Furthermore, this model accounts for the relationship between ORN [Formula: see text] LN and LN-LN synaptic counts and the emergence of different LN types. Functionally, we propose that LNs encode soft cluster memberships of ORN activity, and partially whiten and normalize the stimulus representations in ORNs through inhibitory feedback. Such a synaptic organization could, in principle, autonomously arise through Hebbian plasticity and would allow the circuit to adapt to different environments in an unsupervised manner. We thus uncover a general and potent circuit motif that can learn and extract significant input features and render stimulus representations more efficient. Finally, our study provides a unified framework for relating structure, activity, function, and learning in neural circuits and supports the conjecture that similarity-matching shapes the transformation of neural representations.

Scalable Gaussian process inference of neural responses to natural images.

Predicting the responses of sensory neurons is a long-standing neuroscience goal. However, while there has been much progress in modeling neural responses to simple and/or artificial stimuli, predicting responses to natural stimuli remains an ongoing challenge. On the one hand, deep neural networks perform very well on certain datasets but can fail when data are limited. On the other hand, Gaussian processes (GPs) perform well on limited data but are poor at predicting responses to high-dimensional stimuli, such as natural images. Here, we show how structured priors, e.g., for local and smooth receptive fields, can be used to scale up GPs to model neural responses to high-dimensional stimuli. With this addition, GPs largely outperform a deep neural network trained to predict retinal responses to natural images, with the largest differences observed when both models are trained on a small dataset. Further, since they allow us to quantify the uncertainty in their predictions, GPs are well suited to closed-loop experiments, where stimuli are chosen actively so as to collect "informative" neural data. We show how GPs can be used to actively select which stimuli to present, so as to i) efficiently learn a model of retinal responses to natural images, using few data, and ii) rapidly distinguish between competing models (e.g., a linear vs. a nonlinear model). In the future, our approach could be applied to other sensory areas, beyond the retina.

Intersubject similarity in neural representations underlies shared episodic memory content.

Individuals generally form their unique memories from shared experiences, yet the neural representational mechanisms underlying this subjectiveness of memory are poorly understood. The current study addressed this important question from the cross-subject neural representational perspective, leveraging a large functional magnetic resonance imaging dataset (n = 415) of a face-name associative memory task. We found that individuals' memory abilities were predicted by their synchronization to the group-averaged, canonical trial-by-trial activation level and, to a lesser degree, by their similarity to the group-averaged representational patterns during encoding. More importantly, the memory content shared between pairs of participants could be predicted by their shared local neural activation pattern, particularly in the angular gyrus and ventromedial prefrontal cortex, even after controlling for differences in memory abilities. These results uncover neural representational mechanisms for individualized memory and underscore the constructive nature of episodic memory.

Identifying causal subsequent memory effects.

Over 40 y of accumulated research has detailed associations between neuroimaging signals measured during a memory encoding task and later memory performance, across a variety of brain regions, measurement tools, statistical approaches, and behavioral tasks. But the interpretation of these subsequent memory effects (SMEs) remains unclear: if the identified signals reflect cognitive and neural mechanisms of memory encoding, then the underlying neural activity must be causally related to future memory. However, almost all previous SME analyses do not control for potential confounders of this causal interpretation, such as serial position and item effects. We collect a large fMRI dataset and use an experimental design and analysis approach that allows us to statistically adjust for nearly all known exogenous confounding variables. We find that, using standard approaches without adjustment, we replicate several univariate and multivariate subsequent memory effects and are able to predict memory performance across people. However, we are unable to identify any signal that reliably predicts subsequent memory after adjusting for confounding variables, bringing into doubt the causal status of these effects. We apply the same approach to subjects' judgments of learning collected following an encoding period and show that these behavioral measures of mnemonic status do predict memory after adjustments, suggesting that it is possible to measure signals near the time of encoding that reflect causal mechanisms but that existing neuroimaging measures, at least in our data, may not have the precision and specificity to do so.

Distinct early and late neural mechanisms regulate feature-specific sensory adaptation in the human visual system.

A canonical feature of sensory systems is that they adapt to prolonged or repeated inputs, suggesting the brain encodes the temporal context in which stimuli are embedded. Sensory adaptation has been observed in the central nervous systems of many animal species, using techniques sensitive to a broad range of spatiotemporal scales of neural activity. Two competing models have been proposed to account for the phenomenon. One assumes that adaptation reflects reduced neuronal sensitivity to sensory inputs over time (the "fatigue" account); the other posits that adaptation arises due to increased neuronal selectivity (the "sharpening" account). To adjudicate between these accounts, we exploited the well-known "tilt aftereffect", which reflects adaptation to orientation information in visual stimuli. We recorded whole-brain activity with millisecond precision from human observers as they viewed oriented gratings before and after adaptation, and used inverted encoding modeling to characterize feature-specific neural responses. We found that both fatigue and sharpening mechanisms contribute to the tilt aftereffect, but that they operate at different points in the sensory processing cascade to produce qualitatively distinct outcomes. Specifically, fatigue operates during the initial stages of processing, consistent with tonic inhibition of feedforward responses, whereas sharpening occurs ~200 ms later, consistent with feedback or local recurrent activity. Our findings reconcile two major accounts of sensory adaptation, and reveal how this canonical process optimizes the detection of change in sensory inputs through efficient neural coding.

Ca2+ regulation of glutamate release from inner hair cells of hearing mice.

In our hearing organ, sound is encoded at ribbon synapses formed by inner hair cells (IHCs) and spiral ganglion neurons (SGNs). How the underlying synaptic vesicle (SV) release is controlled by Ca2+ in IHCs of hearing animals remained to be investigated. Here, we performed patch-clamp SGN recordings of the initial rate of release evoked by brief IHC Ca2+-influx in an ex vivo cochlear preparation from hearing mice. We aimed to closely mimic physiological conditions by perforated-patch recordings from IHCs kept at the physiological resting potential and at body temperature. We found release to relate supralinearly to Ca2+-influx (power, m: 4.3) when manipulating the [Ca2+] available for SV release by Zn2+-flicker-blocking of the single Ca2+-channel current. In contrast, a near linear Ca2+ dependence (m: 1.2 to 1.5) was observed when varying the number of open Ca2+-channels during deactivating Ca2+-currents and by dihydropyridine channel-inhibition. Concurrent changes of number and current of open Ca2+-channels over the range of physiological depolarizations revealed m: 1.8. These findings indicate that SV release requires ~4 Ca2+-ions to bind to their Ca2+-sensor of fusion. We interpret the near linear Ca2+-dependence of release during manipulations that change the number of open Ca2+-channels to reflect control of SV release by the high [Ca2+] in the Ca2+-nanodomain of one or few nearby Ca2+-channels. We propose that a combination of Ca2+ nanodomain control and supralinear intrinsic Ca2+-dependence of fusion optimally links SV release to the timing and amplitude of the IHC receptor potential and separates it from other IHC Ca2+-signals unrelated to afferent synaptic transmission.

Enabling witnesses to actively explore faces and reinstate study-test pose during a lineup increases discriminability.

Accurate witness identification is a cornerstone of police inquiries and national security investigations. However, witnesses can make errors. We experimentally tested whether an interactive lineup, a recently introduced procedure that enables witnesses to dynamically view and explore faces from different angles, improves the rate at which witnesses identify guilty over innocent suspects compared to procedures traditionally used by law enforcement. Participants encoded 12 target faces, either from the front or in profile view, and then attempted to identify the targets from 12 lineups, half of which were target present and the other half target absent. Participants were randomly assigned to a lineup condition: simultaneous interactive, simultaneous photo, or sequential video. In the front-encoding and profile-encoding conditions, Receiver Operating Characteristics analysis indicated that discriminability was higher in interactive compared to both photo and video lineups, demonstrating the benefit of actively exploring the lineup members' faces. Signal-detection modeling suggested interactive lineups increase discriminability because they afford the witness the opportunity to view more diagnostic features such that the nondiagnostic features play a proportionally lesser role. These findings suggest that eyewitness errors can be reduced using interactive lineups because they create retrieval conditions that enable witnesses to actively explore faces and more effectively sample features.

Antigen perception in T cells by long-term Erk and NFAT signaling dynamics.

Immune system threat detection hinges on T cells' ability to perceive varying peptide-major histocompatibility complex (pMHC) antigens. As the Erk and NFAT pathways link T cell receptor engagement to gene regulation, their signaling dynamics may convey information about pMHC inputs. To test this idea, we developed a dual reporter mouse strain and a quantitative imaging assay that, together, enable simultaneous monitoring of Erk and NFAT dynamics in live T cells over day-long timescales as they respond to varying pMHC inputs. Both pathways initially activate uniformly across various pMHC inputs but diverge only over longer (9+ h) timescales, enabling independent encoding of pMHC affinity and dose. These late signaling dynamics are decoded via multiple temporal and combinatorial mechanisms to generate pMHC-specific transcriptional responses. Our findings underscore the importance of long timescale signaling dynamics in antigen perception and establish a framework for understanding T cell responses under diverse contexts.

Cholinergic-Sensitive Theta Oscillations in Memory Encoding in Mice.

Cholinergic regulation of hippocampal theta oscillations has long been proposed to be a potential mechanism underlying hippocampus-dependent memory encoding processes. However, cholinergic transmission has been traditionally associated with type II theta under urethane anesthesia. The mechanisms and behavioral significance of cholinergic regulation of type I theta in freely exploring animals is much less clear. In this study, we examined the potential behavioral significance of cholinergic regulation of theta oscillations in the object location task in male mice that involves training and testing trials and provides an ideal behavioral task to study the underlying memory encoding and retrieval processes, respectively. Cholinergic regulation of hippocampal theta oscillations and the behavioral outcomes was examined by either intrahippocampal infusion of cholinergic receptor antagonists or knocking out cholinergic receptors in excitatory neurons or interneurons. We found that both muscarinic acetylcholine receptors (mAChRs) and α7 nicotinic AChRs (α7 nAChRs) regulated memory encoding by engaging excitatory neurons and interneurons, respectively. There is a transient upregulated theta oscillation at the beginning of individual object exploration events that only occurred in the training trials, but not in the testing trials. This transient upregulated theta is also the only theta component that significantly differed between training and testing trials and was sensitive to mAChR and α7 nAChR antagonists. Thus, our study has revealed a transient cholinergic-sensitive theta component that is specifically associated with memory encoding, but not memory retrieval, in the object location task, providing direct experimental evidence supporting a role for cholinergic-regulated theta oscillations in hippocampus-dependent memory encoding processes.

Human Hippocampal Ripples Signal Encoding of Episodic Memories.

Direct human brain recordings have confirmed the presence of high-frequency oscillatory events, termed ripples, during awake behavior. While many prior studies have focused on medial temporal lobe (MTL) ripples during memory retrieval, here we investigate ripples during memory encoding. Specifically, we ask whether ripples during encoding predict whether and how memories are subsequently recalled. Detecting ripples from MTL electrodes implanted in 116 neurosurgical participants (n = 61 male) performing a verbal episodic memory task, we find that encoding ripples do not distinguish recalled from not recalled items in any MTL region, even as high-frequency activity during encoding predicts recall in these same regions. Instead, hippocampal ripples increase during encoding of items that subsequently lead to recall of temporally and semantically associated items during retrieval, a phenomenon known as clustering. This subsequent clustering effect arises specifically when hippocampal ripples co-occur during encoding and retrieval, suggesting that ripples mediate both encoding and reinstatement of episodic memories.

Event Integration and Temporal Differentiation: How Hierarchical Knowledge Emerges in Hippocampal Subfields through Learning.

Everyday life is composed of events organized by changes in contexts, with each event containing an unfolding sequence of occurrences. A major challenge facing our memory systems is how to integrate sequential occurrences within events while also maintaining their details and avoiding over-integration across different contexts. We asked if and how distinct hippocampal subfields come to hierarchically and, in parallel, represent both event context and subevent occurrences with learning. Female and male human participants viewed sequential events defined as sequences of objects superimposed on shared color frames while undergoing high-resolution fMRI. Importantly, these events were repeated to induce learning. Event segmentation, as indexed by increased reaction times at event boundaries, was observed in all repetitions. Temporal memory decisions were quicker for items from the same event compared to across different events, indicating that events shaped memory. With learning, hippocampal CA3 multivoxel activation patterns clustered to reflect the event context, with more clustering correlated with behavioral facilitation during event transitions. In contrast, in the dentate gyrus (DG), temporally proximal items that belonged to the same event became associated with more differentiated neural patterns. A computational model explained these results by dynamic inhibition in the DG. Additional similarity measures support the notion that CA3 clustered representations reflect shared voxel populations, while DG's distinct item representations reflect different voxel populations. These findings suggest an interplay between temporal differentiation in the DG and attractor dynamics in CA3. They advance our understanding of how knowledge is structured through integration and separation across time and context.

Functional diversification of cell signaling by GPCR localization.

G protein-coupled receptors (GPCRs) are the largest family of cell surface receptors and a critical class of regulators of mammalian physiology. Also known as seven transmembrane receptors (7TMs), GPCRs are ubiquitously expressed and versatile, detecting a diverse set of endogenous stimuli, including odorants, neurotransmitters, hormones, peptides, and lipids. Accordingly, GPCRs have emerged as the largest class of drug targets, accounting for upward of 30% of all prescription drugs. The view that ligand-induced GPCR responses originate exclusively from the cell surface has evolved to reflect accumulating evidence that receptors can elicit additional waves of signaling from intracellular compartments. These events in turn shape unique cellular and physiological outcomes. Here, we discuss our current understanding of the roles and regulation of compartmentalized GPCR signaling.

Efficient prediction of peptide self-assembly through sequential and graphical encoding.

In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides. Molecular dynamics has enabled the efficient collection of large peptide datasets, providing reliable training data for deep learning. However, the lack of systematic analysis of the peptide encoding, which is essential for artificial intelligence-assisted peptide-related tasks, makes it an urgent problem to be solved for the improvement of prediction accuracy. To address this issue, we first collect a high-quality, colossal simulation dataset of peptide self-assembly containing over 62 000 samples generated by coarse-grained molecular dynamics. Then, we systematically investigate the effect of peptide encoding of amino acids into sequences and molecular graphs using state-of-the-art sequential (i.e. recurrent neural network, long short-term memory and Transformer) and structural deep learning models (i.e. graph convolutional network, graph attention network and GraphSAGE), on the accuracy of peptide self-assembly prediction, an essential physiochemical process prior to any peptide-related applications. Extensive benchmarking studies have proven Transformer to be the most powerful sequence-encoding-based deep learning model, pushing the limit of peptide self-assembly prediction to decapeptides. In summary, this work provides a comprehensive benchmark analysis of peptide encoding with advanced deep learning models, serving as a guide for a wide range of peptide-related predictions such as isoelectric points, hydration free energy, etc.