Synaptic weight dynamics underlying systems consolidation of a memory.

Systems consolidation is a common feature of learning and memory systems, in which a long-term memory initially stored in one brain region becomes persistently stored in another region. We studied the dynamics of systems consolidation in simple circuit architectures modeling core features of many memory systems: an early- and late-learning brain region and two sites of plasticity. We show that the synaptic dynamics of the circuit during consolidation of an analog memory can be understood as a temporal integration process, by which transient changes in activity driven by plasticity in the early-learning area are accumulated into persistent synaptic changes at the late-learning site. This simple principle leads to two constraints on the circuit operation for consolidation to be implemented successfully. First, the plasticity rule at the late-learning site must stably support a continuum of possible outputs for a given input. We show that this is readily achieved by heterosynaptic but not standard Hebbian rules, that it naturally leads to a speed-accuracy tradeoff in systems consolidation, and that it provides a concrete circuit instantiation for how systems consolidation solves the stability-plasticity dilemma. Second, to turn off the consolidation process and prevent erroneous changes at the late-learning site, neural activity in the early-learning area must be reset to its baseline activity. We propose two biologically plausible implementations for this reset that suggest novel roles for core elements of the cerebellar circuit.

Interactions between circuit architecture and plasticity in a closed-loop cerebellar system.

Determining the sites and directions of plasticity underlying changes in neural activity and behavior is critical for understanding mechanisms of learning. Identifying such plasticity from neural recording data can be challenging due to feedback pathways that impede reasoning about cause and effect. We studied interactions between feedback, neural activity, and plasticity in the context of a closed-loop motor learning task for which there is disagreement about the loci and directions of plasticity: vestibulo-ocular reflex learning. We constructed a set of circuit models that differed in the strength of their recurrent feedback, from no feedback to very strong feedback. Despite these differences, each model successfully fit a large set of neural and behavioral data. However, the patterns of plasticity predicted by the models fundamentally differed, with the direction of plasticity at a key site changing from depression to potentiation as feedback strength increased. Guided by our analysis, we suggest how such models can be experimentally disambiguated. Our results address a long-standing debate regarding cerebellum-dependent motor learning, suggesting a reconciliation in which learning-related changes in the strength of synaptic inputs to Purkinje cells are compatible with seemingly oppositely directed changes in Purkinje cell spiking activity. More broadly, these results demonstrate how changes in neural activity over learning can appear to contradict the sign of the underlying plasticity when either internal feedback or feedback through the environment is present.

A Lyapunov theory demonstrating a fundamental limit on the speed of systems consolidation.

The nervous system reorganizes memories from an early site to a late site, a commonly observed feature of learning and memory systems known as systems consolidation. Previous work has suggested learning rules by which consolidation may occur. Here, we provide conditions under which such rules are guaranteed to lead to stable convergence of learning and consolidation. We use the theory of Lyapunov functions, which enforces stability by requiring learning rules to decrease an energy-like (Lyapunov) function. We present the theory in the context of a simple circuit architecture motivated by classic models of learning in systems consolidation mediated by the cerebellum. Stability is only guaranteed if the learning rate in the late stage is not faster than the learning rate in the early stage. Further, the slower the learning rate at the late stage, the larger the perturbation the system can tolerate with a guarantee of stability. We provide intuition for this result by mapping the consolidation model to a damped driven oscillator system, and showing that the ratio of early-to late-stage learning rates in the consolidation model can be directly identified with the (square of the) oscillator's damping ratio. This work suggests the power of the Lyapunov approach to provide constraints on nervous system function.

An oscillatory mechanism for multi-level storage in short-term memory.

Oscillatory activity is commonly observed during the maintenance of information in short-term memory, but its role remains unclear. Non-oscillatory models of short-term memory storage are able to encode stimulus identity through their spatial patterns of activity, but are typically limited to either an all-or-none representation of stimulus amplitude or exhibit a biologically implausible exact-tuning condition. Here we demonstrate a simple mechanism by which oscillatory input enables a circuit to generate persistent or sequential activity that encodes information not only in the spatial pattern of activity, but also in the amplitude of activity. This is accomplished through a phase-locking phenomenon that permits many different amplitudes of persistent activity to be stored without requiring exact tuning of model parameters. Altogether, this work proposes a class of models for the storage of information in working memory, a potential role for brain oscillations, and a dynamical mechanism for maintaining multi-stable neural representations.

Cyclic structure with cellular precision in a vertebrate sensorimotor neural circuit.

Neuronal wiring diagrams reconstructed by electron microscopy1,2,3,4,5 pose new questions about the organization of nervous systems following the time-honored tradition of cross-species comparisons.6,7 The C. elegans connectome has been conceptualized as a sensorimotor circuit that is approximately feedforward,8,9,10,11 starting from sensory neurons proceeding to interneurons and ending with motor neurons. Overrepresentation of a 3-cell motif often known as the "feedforward loop" has provided further evidence for feedforwardness.10,12 Here, we contrast with another sensorimotor wiring diagram that was recently reconstructed from a larval zebrafish brainstem.13 We show that the 3-cycle, another 3-cell motif, is highly overrepresented in the oculomotor module of this wiring diagram. This is a first for any neuronal wiring diagram reconstructed by electron microscopy, whether invertebrate12,14 or mammalian.15,16,17 The 3-cycle of cells is "aligned" with a 3-cycle of neuronal groups in a stochastic block model (SBM)18 of the oculomotor module. However, the cellular cycles exhibit more specificity than can be explained by the group cycles-recurrence to the same neuron is surprisingly common. Cyclic structure could be relevant for theories of oculomotor function that depend on recurrent connectivity. The cyclic structure coexists with the classic vestibulo-ocular reflex arc for horizontal eye movements,19 and could be relevant for recurrent network models of temporal integration by the oculomotor system.20,21.

Neural circuit models for evidence accumulation through choice-selective sequences.

Decision making is traditionally thought to be mediated by populations of neurons whose firing rates persistently accumulate evidence across time. However, recent decision-making experiments in rodents have observed neurons across the brain that fire sequentially as a function of spatial position or time, rather than persistently, with the subset of neurons in the sequence depending on the animal's choice. We develop two new candidate circuit models, in which evidence is encoded either in the relative firing rates of two competing chains of neurons or in the network location of a stereotyped pattern ("bump") of neural activity. Encoded evidence is then faithfully transferred between neuronal populations representing different positions or times. Neural recordings from four different brain regions during a decision-making task showed that, during the evidence accumulation period, different brain regions displayed tuning curves consistent with different candidate models for evidence accumulation. This work provides mechanistic models and potential neural substrates for how graded-value information may be precisely accumulated within and transferred between neural populations, a set of computations fundamental to many cognitive operations.

Oculomotor plant and neural dynamics suggest gaze control requires integration on distributed timescales.

A fundamental principle of biological motor control is that the neural commands driving movement must conform to the response properties of the motor plants they control. In the oculomotor system, characterizations of oculomotor plant dynamics traditionally supported models in which the plant responds to neural drive to extraocular muscles on exclusively short, subsecond timescales. These models predict that the stabilization of gaze during fixations between saccades requires neural drive that approximates eye position on longer timescales and is generated through the temporal integration of brief eye velocity-encoding signals that cause saccades. However, recent measurements of oculomotor plant behaviour have revealed responses on longer timescales. Furthermore, measurements of firing patterns in the oculomotor integrator have revealed a more complex encoding of eye movement dynamics. Yet, the link between these observations has remained unclear. Here we use measurements from the larval zebrafish to link dynamics in the oculomotor plant to dynamics in the neural integrator. The oculomotor plant in both anaesthetized and awake larval zebrafish was characterized by a broad distribution of response timescales, including those much longer than 1 s. Analysis of the firing patterns of oculomotor integrator neurons, which exhibited a broadly distributed range of decay time constants, demonstrates the sufficiency of this activity for stabilizing gaze given an oculomotor plant with distributed response timescales. This work suggests that leaky integration on multiple, distributed timescales by the oculomotor integrator reflects an inverse model for generating oculomotor commands, and that multi-timescale dynamics may be a general feature of motor circuitry. KEY POINTS: Recent observations of oculomotor plant response properties and neural activity across the oculomotor system have called into question classical formulations of both the oculomotor plant and the oculomotor integrator. Here we use measurements from new and published experiments in the larval zebrafish together with modelling to reconcile recent oculomotor plant observations with oculomotor integrator function. We developed computational techniques to characterize oculomotor plant responses over several seconds in awake animals, demonstrating that long timescale responses seen in anaesthetized animals extend to the awake state. Analysis of firing patterns of oculomotor integrator neurons demonstrates the sufficiency of this activity for stabilizing gaze given an oculomotor plant with multiple, distributed response timescales. Our results support a formulation of gaze stabilization by the oculomotor system in which commands for stabilizing gaze are generated through integration on multiple, distributed timescales.

Choice-selective sequences dominate in cortical relative to thalamic inputs to NAc to support reinforcement learning.

How are actions linked with subsequent outcomes to guide choices? The nucleus accumbens, which is implicated in this process, receives glutamatergic inputs from the prelimbic cortex and midline regions of the thalamus. However, little is known about whether and how representations differ across these input pathways. By comparing these inputs during a reinforcement learning task in mice, we discovered that prelimbic cortical inputs preferentially represent actions and choices, whereas midline thalamic inputs preferentially represent cues. Choice-selective activity in the prelimbic cortical inputs is organized in sequences that persist beyond the outcome. Through computational modeling, we demonstrate that these sequences can support the neural implementation of reinforcement-learning algorithms, in both a circuit model based on synaptic plasticity and one based on neural dynamics. Finally, we test and confirm a prediction of our circuit models by direct manipulation of nucleus accumbens input neurons.

Combining Microbial Culturing With Mathematical Modeling in an Introductory Course-Based Undergraduate Research Experience.

Quantitative techniques are a critical part of contemporary biology research, but students interested in biology enter college with widely varying quantitative skills and attitudes toward mathematics. Course-based undergraduate research experiences (CUREs) may be an early way to build student competency and positive attitudes. Here we describe the design, implementation, and assessment of an introductory quantitative CURE focused on halophilic microbes. In this CURE, students culture and isolate halophilic microbes from environmental and food samples, perform growth assays, then use mathematical modeling to quantify the growth rate of strains in different salinities. To assess how the course may impact students' future academic plans and attitudes toward the use of math in biology, we used pre- and post-quarter surveys. Students who completed the course showed more positive attitudes toward science learning and an increased interest in pursuing additional quantitative biology experiences. We argue that the classroom application of microbiology methods, combined with mathematical modeling using student-generated data, provides a degree of student ownership, collaboration, iteration, and discovery that makes quantitative learning both relevant and exciting to students.

Predictors of aging out of heavy episodic drinking in emerging adults: a systematic review protocol.

BACKGROUND: Heavy episodic drinking (HED) refers to alcohol consumption that exceeds the recommended threshold for a given episode and increases risk for diverse negative alcohol-related consequences. A pattern of weekly HED is most prevalent in emerging adults (i.e., age 18-25). However, rates of HED consistently decline in the mid to late twenties, referred to as 'aging out' or 'maturing out' of HED. Although many individual studies have followed changes in drinking behaviour over the transition to adulthood, there has yet to be a systematic review to identify consistent factors contributing to risk (i.e. failure to age out) and protection (i.e. successful aging out). The objective of this review will be to summarize and critically appraise the literature on factors contributing to aging out of HED among emerging adults. METHODS: A systematic search of observational cohort studies following drinking behaviours in age cohorts overlapping with the emerging adulthood period will be conducted in MEDLINE, EMBASE, PsychInfo, and CINAHL. Two independent reviewers will evaluate identified studies for inclusion eligibility, extract study data, and assess the quality of included studies. Primary outcomes will be quantity/frequency of alcohol use (e.g. drinks/week) and severity of alcohol-related problems. Predictors of maturing out of HED will be reported narratively, and where appropriate, random effects meta-analyses will be conducted to provide pooled effect sizes. An evidence map will be created to characterize the overall pattern of findings. DISCUSSION: This systematic review will provide a timely and warranted summary of published work contributing to understanding aging out of heavy episodic drinking. Our findings will provide critical commentary on the developmental course of HED during the transition from adolescence to adulthood and will be the first review to consider both protective and risk factors for maturing out of frequent binge drinking. By highlighting factors identifying those at-risk for prolonged heavy episodic drinking, our conclusions will have important treatment implications for primary, secondary, and tertiary intervention strategies. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42017078436 .

Using future-oriented expectancy associates to probe real-time variations in motivation to consume alcohol.

An extensive empirical and theoretical literature has characterized anticipatory/expectancy processes as integral to motivation, including motivation to consume alcohol. To examine whether these processes could be probed on a moment to moment basis as they activate to motivate near term drinking, we sampled future-oriented expectancy verbal associates (i.e., self-generated words) using an ecological momentary assessment (EMA) paradigm every 3 hr on 3 days of the week (2 days of likely drinking and 1 of low drinking likelihood). Expectancy associates were chosen because cognitive psychologists consider verbal items collected in this manner a part-way approach to measurement of automatic/implicit processes. Consistent with predictions, more positively valenced alcohol expectancies activated within a few hours preceding increased alcohol consumption, and this activation could be statistically distinguished from the influence of a wide array of other variables known to predict consumption, including autocorrelation. As previously observed, more positively valenced alcohol expectancies were activated in alcohol-related environments. These findings provide further evidence that anticipatory information processing is engaged for the direction of future behavior, and that probing expectancies in real-time can be useful for predicting near-term alcohol consumption. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience.

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 datas. 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.

Synchronization of Electrically Coupled Resonate-and-Fire Neurons.

Electrical coupling between neurons is broadly present across brain areas and is typically assumed to synchronize network activity. However, intrinsic properties of the coupled cells can complicate this simple picture. Many cell types with electrical coupling show a diversity of post-spike subthreshold fluctuations, often linked to subthreshold resonance, which are transmitted through electrical synapses in addition to action potentials. Using the theory of weakly coupled oscillators, we explore the effect of both subthreshold and spike-mediated coupling on synchrony in small networks of electrically coupled resonate-and-fire neurons, a hybrid neuron model with damped subthreshold oscillations and a range of post-spike voltage dynamics. We calculate the phase response curve using an extension of the adjoint method that accounts for the discontinuous post-spike reset rule. We find that both spikes and subthreshold fluctuations can jointly promote synchronization. The subthreshold contribution is strongest when the voltage exhibits a significant post-spike elevation in voltage, or plateau potential. Additionally, we show that the geometry of trajectories approaching the spiking threshold causes a "reset-induced shear" effect that can oppose synchrony in the presence of network asymmetry, despite having no effect on the phase-locking of symmetrically coupled pairs.

Competing inhibition-stabilized networks in sensory and memory processing.

In simplified models of neocortical circuits, inhibition is either modeled in a feedforward manner or through mutual inhibitory interactions that provide for competition between neuronal populations. By contrast, recent work has suggested a critical role for recurrent inhibition as a negative feedback element that stabilizes otherwise unstable recurrent excitation. Here, we show how models based upon a motif of recurrently connected "E-I" pairs of excitatory and inhibitory units can be used to describe experimental observations in sensory and memory networks. In a sensory network model of binocular rivalry, a model based on competing E-I motifs captures psychophysical observations about how incongruous images presented to the two eyes compete. In a model of cortical working memory, an architecturally similar model with modified synaptic time constants can mathematically accumulate signals into a working memory buffer in a manner that is robust to the abrupt removal of cells. These results suggest the inhibition-stabilized E-I motif as a fundamental building block for models of a wide array of neocortical dynamics.

Exposure to male sexual scents (androstenone) influences women's drinking.

In a demonstration of a heretofore unknown motivational pathway for alcohol consumption, we recently showed that exposure to scents emitted by human females during the fertile phase of their menstrual cycle could increase men's drinking. The current study examined the reverse: whether exposure to male sexual scents (androstenone) would increase women's drinking. One hundred three female participants were primed with either androstenone or a control prime (plain water) camouflaged as a men's "cologne." They then completed a laboratory assessment of beer consumption and related measures. (Nonalcoholic beer was used for methodological and safety reasons.) Results indicated that females exposed to the androstenone prime drank significantly more than those exposed to the control prime. Social and sexual expectancies taken subsequent to drinking (to avoid unwanted manipulation influences) were correlated with drinking in the primed group but not in the neutral group, supporting the idea that information-processing pathways related to alcohol use had been engaged in the primed group. Few females were ovulating, precluding assessment of the effects of fertility on this process. Because of the centrality of sexual signaling to fundamental evolutionary/biological forces, these results indicate a potentially powerful influence on alcohol consumption that calls for continued investigation. (PsycINFO Database Record

Population-scale organization of cerebellar granule neuron signaling during a visuomotor behavior.

Granule cells at the input layer of the cerebellum comprise over half the neurons in the human brain and are thought to be critical for learning. However, little is known about granule neuron signaling at the population scale during behavior. We used calcium imaging in awake zebrafish during optokinetic behavior to record transgenically identified granule neurons throughout a cerebellar population. A significant fraction of the population was responsive at any given time. In contrast to core precerebellar populations, granule neuron responses were relatively heterogeneous, with variation in the degree of rectification and the balance of positive versus negative changes in activity. Functional correlations were strongest for nearby cells, with weak spatial gradients in the degree of rectification and the average sign of response. These data open a new window upon cerebellar function and suggest granule layer signals represent elementary building blocks under-represented in core sensorimotor pathways, thereby enabling the construction of novel patterns of activity for learning.

Computational training for the next generation of neuroscientists.

Neuroscience research has become increasingly reliant upon quantitative and computational data analysis and modeling techniques. However, the vast majority of neuroscientists are still trained within the traditional biology curriculum, in which computational and quantitative approaches beyond elementary statistics may be given little emphasis. Here we provide the results of an informal poll of computational and other neuroscientists that sought to identify critical needs, areas for improvement, and educational resources for computational neuroscience training. Motivated by this survey, we suggest steps to facilitate quantitative and computational training for future neuroscientists.

Effect of Interactions between Harvester Ants on Forager Decisions.

Harvester ant colonies adjust their foraging activity to day-to-day changes in food availability and hour-to-hour changes in environmental conditions. This collective behavior is regulated through interactions, in the form of brief antennal contacts, between outgoing foragers and returning foragers with food. Here we consider how an ant, waiting in the entrance chamber just inside the nest entrance, uses its accumulated experience of interactions to decide whether to leave the nest to forage. Using videos of field observations, we tracked the interactions and foraging decisions of ants in the entrance chamber. Outgoing foragers tended to interact with returning foragers at higher rates than ants that returned to the deeper nest and did not forage. To provide a mechanistic framework for interpreting these results, we develop a decision model in which ants make decisions based upon a noisy accumulation of individual contacts with returning foragers. The model can reproduce core trends and realistic distributions for individual ant interaction statistics, and suggests possible mechanisms by which foraging activity may be regulated at an individual ant level.

Exposure to female fertility pheromones influences men's drinking.

Research has shown that humans consciously use alcohol to encourage sexual activity. In the current study, we investigated whether decision making about alcohol use and sex can be cued outside of awareness by recently revealed sexual signaling mechanisms. Specifically, we examined if males exposed without their knowledge to pheromones emitted by fertile females would increase their alcohol consumption, presumably via neurobehavioral information pathways that link alcohol to sex and mating. We found that men who smelled a T-shirt worn by a fertile female drank significantly more (nonalcoholic) beer, and exhibited significantly greater approach behavior toward female cues, than those who smelled a T-shirt worn by a nonfertile female. These findings reveal previously unknown influences on human alcohol consumption, augment the research base for pheromone cuing of sexual behavior in humans, and raise the possibility that other, as yet unknown, pathways of behavioral influence may be operating hidden from view.