Retuning of hippocampal representations during sleep.

Hippocampal representations that underlie spatial memory undergo continuous refinement following formation1. Here, to track the spatial tuning of neurons dynamically during offline states, we used a new Bayesian learning approach based on the spike-triggered average decoded position in ensemble recordings from freely moving rats. Measuring these tunings, we found spatial representations within hippocampal sharp-wave ripples that were stable for hours during sleep and were strongly aligned with place fields initially observed during maze exploration. These representations were explained by a combination of factors that included preconfigured structure before maze exposure and representations that emerged during θ-oscillations and awake sharp-wave ripples while on the maze, revealing the contribution of these events in forming ensembles. Strikingly, the ripple representations during sleep predicted the future place fields of neurons during re-exposure to the maze, even when those fields deviated from previous place preferences. By contrast, we observed tunings with poor alignment to maze place fields during sleep and rest before maze exposure and in the later stages of sleep. In sum, the new decoding approach allowed us to infer and characterize the stability and retuning of place fields during offline periods, revealing the rapid emergence of representations following new exploration and the role of sleep in the representational dynamics of the hippocampus.

Closed-loop modulation of remote hippocampal representations with neurofeedback.

Humans can remember specific events without acting on them and can influence which memories are retrieved based on internal goals. However, current animal models of memory typically present sensory cues to trigger retrieval and assess retrieval based on action 1-5 . As a result, it is difficult to determine whether measured patterns of neural activity relate to the cue(s), the retrieved memory, or the behavior. We therefore asked whether we could develop a paradigm to isolate retrieval-related neural activity in animals without retrieval cues or the requirement of a behavioral report. To do this, we focused on hippocampal "place cells." These cells primarily emit spiking patterns that represent the animal's current location (local representations), but they can also generate representations of previously visited locations distant from the animal's current location (remote representations) 6-13 . It is not known whether animals can deliberately engage specific remote representations, and if so, whether this engagement would occur during specific brain states. So, we used a closed-loop neurofeedback system to reward expression of remote representations that corresponded to uncued, experimenter-selected locations, and found that rats could increase the prevalence of these specific remote representations over time; thus, demonstrating memory retrieval modulated by internal goals in an animal model. These representations occurred predominately during periods of immobility but outside of hippocampal sharp-wave ripple (SWR) 13-15 events. This paradigm enables future direct studies of memory retrieval mechanisms in the healthy brain and in models of neurological disorders.

Foraging Under Uncertainty Follows the Marginal Value Theorem with Bayesian Updating of Environment Representations.

Foraging theory has been a remarkably successful approach to understanding the behavior of animals in many contexts. In patch-based foraging contexts, the marginal value theorem (MVT) shows that the optimal strategy is to leave a patch when the marginal rate of return declines to the average for the environment. However, the MVT is only valid in deterministic environments whose statistics are known to the forager; naturalistic environments seldom meet these strict requirements. As a result, the strategies used by foragers in naturalistic environments must be empirically investigated. We developed a novel behavioral task and a corresponding computational framework for studying patch-leaving decisions in head-fixed and freely moving mice. We varied between-patch travel time, as well as within-patch reward depletion rate, both deterministically and stochastically. We found that mice adopt patch residence times in a manner consistent with the MVT and not explainable by simple ethologically motivated heuristic strategies. Critically, behavior was best accounted for by a modified form of the MVT wherein environment representations were updated based on local variations in reward timing, captured by a Bayesian estimator and dynamic prior. Thus, we show that mice can strategically attend to, learn from, and exploit task structure on multiple timescales simultaneously, thereby efficiently foraging in volatile environments. The results provide a foundation for applying the systems neuroscience toolkit in freely moving and head-fixed mice to understand the neural basis of foraging under uncertainty.

Extended Poisson Gaussian-Process Latent Variable Model for Unsupervised Neural Decoding.

Dimension reduction on neural activity paves a way for unsupervised neural decoding by dissociating the measurement of internal neural state repetition from the measurement of external variable tuning. With assumptions only on the smoothness of latent dynamics and of internal tuning curves, the Poisson Gaussian-process latent variable model (P-GPLVM) (Wu et al., 2017) is a powerful tool to discover the low-dimensional latent structure for high-dimensional spike trains. However, when given novel neural data, the original model lacks a method to infer their latent trajectories in the learned latent space, limiting its ability for estimating the internal state repetition. Here, we extend the P-GPLVM to enable the latent variable inference of new data constrained by previously learned smoothness and mapping information. We also describe a principled approach for the constrained latent variable inference for temporally-compressed patterns of activity, such as those found in population burst events (PBEs) during hippocampal sharp-wave ripples, as well as metrics for assessing whether the inferred new latent variables are congruent with a previously learned manifold in the latent space. Applying these approaches to hippocampal ensemble recordings during active maze exploration, we replicate the result that P-GPLVM learns a latent space encoding the animal's position. We further demonstrate that this latent space can differentiate one maze context from another. By inferring the latent variables of new neural data during running, certain internal neural states are observed to repeat, which is in accordance with the similarity of experiences encoded by its nearby neural trajectories in the training data manifold. Finally, repetition of internal neural states can be estimated for neural activity during PBEs as well, allowing the identification for replay events of versatile behaviors and more general experiences. Thus, our extension of the P-GPLVM framework for unsupervised analysis of neural activity can be used to answer critical questions related to scientific discovery.

Spyglass: a framework for reproducible and shareable neuroscience research.

Scientific progress depends on reliable and reproducible results. Progress can also be accelerated when data are shared and re-analyzed to address new questions. Current approaches to storing and analyzing neural data typically involve bespoke formats and software that make replication, as well as the subsequent reuse of data, difficult if not impossible. To address these challenges, we created Spyglass, an open-source software framework that enables reproducible analyses and sharing of data and both intermediate and final results within and across labs. Spyglass uses the Neurodata Without Borders (NWB) standard and includes pipelines for several core analyses in neuroscience, including spectral filtering, spike sorting, pose tracking, and neural decoding. It can be easily extended to apply both existing and newly developed pipelines to datasets from multiple sources. We demonstrate these features in the context of a cross-laboratory replication by applying advanced state space decoding algorithms to publicly available data. New users can try out Spyglass on a Jupyter Hub hosted by HHMI and 2i2c: https://spyglass.hhmi.2i2c.cloud/.

The neural basis of mental navigation in rats.

A brain-machine interface demonstrates volitional control of hippocampal activity.

In vivo lensless microscopy via a phase mask generating diffraction patterns with high-contrast contours.

The simple and compact optics of lensless microscopes and the associated computational algorithms allow for large fields of view and the refocusing of the captured images. However, existing lensless techniques cannot accurately reconstruct the typical low-contrast images of optically dense biological tissue. Here we show that lensless imaging of tissue in vivo can be achieved via an optical phase mask designed to create a point spread function consisting of high-contrast contours with a broad spectrum of spatial frequencies. We built a prototype lensless microscope incorporating the 'contour' phase mask and used it to image calcium dynamics in the cortex of live mice (over a field of view of about 16 mm2) and in freely moving Hydra vulgaris, as well as microvasculature in the oral mucosa of volunteers. The low cost, small form factor and computational refocusing capability of in vivo lensless microscopy may open it up to clinical uses, especially for imaging difficult-to-reach areas of the body.

GhostiPy: An Efficient Signal Processing and Spectral Analysis Toolbox for Large Data.

Recent technological advances have enabled neural recordings consisting of hundreds to thousands of channels. As the pace of these developments continues to grow rapidly, it is imperative to have fast, flexible tools supporting the analysis of neural data gathered by such large-scale modalities. Here we introduce GhostiPy (general hub of spectral techniques in Python), a Python open source software toolbox implementing various signal processing and spectral analyses including optimal digital filters and time-frequency transforms. GhostiPy prioritizes performance and efficiency by using parallelized, blocked algorithms. As a result, it is able to outperform commercial software in both time and space complexity for high-channel count data and can handle out-of-core computation in a user-friendly manner. Overall, our software suite reduces frequently encountered bottlenecks in the experimental pipeline, and we believe this toolset will enhance both the portability and scalability of neural data analysis.

A framework to identify structured behavioral patterns within rodent spatial trajectories.

Animal behavior is highly structured. Yet, structured behavioral patterns-or "statistical ethograms"-are not immediately apparent from the full spatiotemporal data that behavioral scientists usually collect. Here, we introduce a framework to quantitatively characterize rodent behavior during spatial (e.g., maze) navigation, in terms of movement building blocks or motor primitives. The hypothesis that we pursue is that rodent behavior is characterized by a small number of motor primitives, which are combined over time to produce open-ended movements. We assume motor primitives to be organized in terms of two sparsity principles: each movement is controlled using a limited subset of motor primitives (sparse superposition) and each primitive is active only for time-limited, time-contiguous portions of movements (sparse activity). We formalize this hypothesis using a sparse dictionary learning method, which we use to extract motor primitives from rodent position and velocity data collected during spatial navigation, and successively to reconstruct past trajectories and predict novel ones. Three main results validate our approach. First, rodent behavioral trajectories are robustly reconstructed from incomplete data, performing better than approaches based on standard dimensionality reduction methods, such as principal component analysis, or single sparsity. Second, the motor primitives extracted during one experimental session generalize and afford the accurate reconstruction of rodent behavior across successive experimental sessions in the same or in modified mazes. Third, in our approach the number of motor primitives associated with each maze correlates with independent measures of maze complexity, hence showing that our formalism is sensitive to essential aspects of task structure. The framework introduced here can be used by behavioral scientists and neuroscientists as an aid for behavioral and neural data analysis. Indeed, the extracted motor primitives enable the quantitative characterization of the complexity and similarity between different mazes and behavioral patterns across multiple trials (i.e., habit formation). We provide example uses of this computational framework, showing how it can be used to identify behavioural effects of maze complexity, analyze stereotyped behavior, classify behavioral choices and predict place and grid cell displacement in novel environments.

Novel Virtual Reality System for Auditory Tasks in Head-fixed Mice.

An emerging corpus of research seeks to use virtual realities (VRs) to understand the neural mechanisms underlying spatial navigation and decision making in rodents. These studies have primarily used visual stimuli to represent the virtual world. However, auditory cues play an important role in navigation for animals, especially when the visual system cannot detect objects or predators. We have developed a virtual reality environment defined exclusively by free-field acoustic landmarks for head-fixed mice. We trained animals to run in a virtual environment with 3 acoustic landmarks. We present evidence that they can learn to navigate in our context: we observed anticipatory licking and modest anticipatory slowing preceding the reward region. Furthermore, we found that animals were highly aware of changes in landmark cues: licking behavior changed dramatically when the familiar virtual environment was switched to a novel one, and then rapidly reverted to normal when the familiar virtual environment was re-introduced, all within the same session. Finally, while animals executed the task, we performed in-vivo calcium imaging in the CA1 region of the hippocampus using a modified Miniscope.org system. Our experiments point to a future in which auditory virtual reality can be used to expand our understanding of the neural bases of audition in locomoting animals and the variety of sensory cues which anchor spatial representations in a new virtual environment.

Tracing a Path for Memory in the Hippocampus.

The hippocampal activity supporting trace fear conditioning has long been mysterious, but a leading hypothesis posits "time-cell"-like sequential patterns. In this issue of Neuron, Ahmed et al. (2020) present new data suggesting that, at least during the first session of learning, a subset of neurons coalesce to selectively encode the task but without expressing reliable sequences.

Magnetoelectric Materials for Miniature, Wireless Neural Stimulation at Therapeutic Frequencies.

A major challenge for miniature bioelectronics is wireless power delivery deep inside the body. Electromagnetic or ultrasound waves suffer from absorption and impedance mismatches at biological interfaces. On the other hand, magnetic fields do not suffer these losses, which has led to magnetically powered bioelectronic implants based on induction or magnetothermal effects. However, these approaches have yet to produce a miniature stimulator that operates at clinically relevant high frequencies. Here, we show that an alternative wireless power method based on magnetoelectric (ME) materials enables miniature magnetically powered neural stimulators that operate up to clinically relevant frequencies in excess of 100 Hz. We demonstrate that wireless ME stimulators provide therapeutic deep brain stimulation in a freely moving rodent model for Parkinson's disease and that these devices can be miniaturized to millimeter-scale and fully implanted. These results suggest that ME materials are an excellent candidate to enable miniature bioelectronics for clinical and research applications.

Sputtered porous Pt for wafer-scale manufacture of low-impedance flexible microelectrodes.

OBJECTIVE: Recording electrical activity from individual cells in vivo is a key technology for basic neuroscience and has growing clinical applications. To maximize the number of independent recording channels as well as the longevity, and quality of these recordings, researchers often turn to small and flexible electrodes that minimize tissue damage and can isolate signals from individual neurons. One challenge when creating these small electrodes, however, is to maintain a low interfacial impedance by applying a surface coating that is stable in tissue and does not significantly complicate the fabrication process. APPROACH: Here we use a high-pressure Pt sputtering process to create low-impedance electrodes at the wafer scale using standard microfabrication equipment. MAIN RESULTS: We find that direct-sputtered Pt provides a reliable and well-controlled porous coating that reduces the electrode impedance by 5-9 fold compared to flat Pt and is compatible with the microfabrication technologies used to create flexible electrodes. These porous Pt electrodes show reduced thermal noise that matches theoretical predictions. In addition, we show that these electrodes can be implanted into rat cortex, record single unit activity, and be removed all without disrupting the integrity of the coating. We also demonstrate that the shape of the electrode (in addition to the surface area) has a significant effect on the electrode impedance when the feature sizes are on the order of tens of microns. SIGNIFICANCE: Overall, porous Pt represents a promising method for manufacturing low-impedance electrodes that can be seamlessly integrated into existing processes for producing flexible neural probes.

Progress and issues in second-order analysis of hippocampal replay.

Patterns of neural activity that occur spontaneously during sharp-wave ripple (SWR) events in the hippocampus are thought to play an important role in memory formation, consolidation and retrieval. Typical studies examining the content of SWRs seek to determine whether the identity and/or temporal order of cell firing is different from chance. Such 'first-order' analyses are focused on a single time point and template (map), and have been used to show, for instance, the existence of preplay. The major methodological challenge in first-order analyses is the construction and interpretation of different chance distributions. By contrast, 'second-order' analyses involve a comparison of SWR content between different time points, and/or between different templates. Typical second-order questions include tests of experience-dependence (replay) that compare SWR content before and after experience, and comparisons or replay between different arms of a maze. Such questions entail additional methodological challenges that can lead to biases in results and associated interpretations. We provide an inventory of analysis challenges for second-order questions about SWR content, and suggest ways of preventing, identifying and addressing possible analysis biases. Given evolving interest in understanding SWR content in more complex experimental scenarios and across different time scales, we expect these issues to become increasingly pervasive. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

Developing Next-generation Brain Sensing Technologies - A Review.

Advances in sensing technology raise the possibility of creating neural interfaces that can more effectively restore or repair neural function and reveal fundamental properties of neural information processing. To realize the potential of these bioelectronic devices, it is necessary to understand the capabilities of emerging technologies and identify the best strategies to translate these technologies into products and therapies that will improve the lives of patients with neurological and other disorders. Here we discuss emerging technologies for sensing brain activity, anticipated challenges for translation, and perspectives for how to best transition these technologies from academic research labs to useful products for neuroscience researchers and human patients.

Analysis of an open source, closed-loop, realtime system for hippocampal sharp-wave ripple disruption.

OBJECTIVE: The ability to modulate neural activity in a closed-loop fashion enables causal tests of hypotheses which link dynamically-changing neural circuits to specific behavioral functions. One such dynamically-changing neural circuit is the hippocampus, in which momentary sharp-wave ripple (SWR) events-≈ 100 ms periods of large 150-250 Hz oscillations-have been linked to specific mnemonic functions via selective closed-loop perturbation. The limited duration of SWR means that the latency in systems used for closed-loop interaction is of significant consequence compared to other longer-lasting circuit states. While closed-loop SWR perturbation is becoming more wide-spread, the performance trade-offs involved in building a SWR disruption system have not been explored, limiting the design and interpretation of paradigms involving ripple disruption. APPROACH: We developed and evaluated a low-latency closed-loop SWR detection system implemented as a module to an open-source neural data acquisition software suite capable of interfacing with two separate data acquisition hardware platforms. We first use synthetic data to explore the parameter space of our detection algorithm, then proceed to quantify the realtime in vivo performance and limitations of our system. MAIN RESULTS: We evaluate the realtime system performance of two data acquisition platforms, one using USB and one using ethernet for communication. We report that signal detection latency decomposes into a data acquisition component of 7.5-13.8 ms and 1.35-2.6 ms for USB and ethernet hardware respectively, and an algorithmic component which varies depending on the threshold parameter. Using ethernet acquisition hardware, we report that an algorithmic latency in the range of ≈20-66 ms can be achieved while maintaining <10 false detections per minute, and that these values are highly dependent upon algorithmic parameter space trade-offs. SIGNIFICANCE: By characterizing this system in detail, we establish a framework for analyzing other closed-loop neural interfacing systems. Thus, we anticipate this modular, open-source, realtime system will facilitate a wide range of carefully-designed causal closed-loop experiments.

Enhanced Image Sensor Module for Head-Mounted Microscopes.

Several research groups have developed head-mounted fluorescence microscopes as a modality for recording neural activity in freely behaving mice. The current designs have shown exciting results from in vivo imaging of the bright dynamics of genetically encoded calcium indicators (GECI). However, despite their potential, head-mounted microscopes are not in use with genetically encoded voltage indicators (GEVI) or bioluminescence indicators. Due to its ability to match the temporal resolution of neuron spiking, GEVIs offer great benefits to experiments designed to provide feedback after real-time detection of specific neural activity such as the less than 250ms replay events that can occur in the hippocampus. Orthogonally, the emerging bioluminescence activity reporters have the potential to eliminate autofluorescence and photobleaching that can occur in fluorescence imaging. There are two important properties of the head-mounted microscope's image sensor affecting the ability to image GEVIs and bioluminescence indicators. First, the low signal to noise ratio (SNR) characteristics of GEVIs and bioluminescent indicators make signal detection difficult. Second, in order to take advantage of the GEVIs faster fluorescence kinetics, the image sensor must be capable of matching frame rates. Here, we present the design of a new imaging module for head-mounted microscopes incorporating the latest CMOS sensor technology aimed at increasing image sensor sensitivity and frame rates for use in real-time detection experiments. The design builds off an existing open-source project and can integrate into the existing data acquisition hardware and microscope housing.

Uncovering temporal structure in hippocampal output patterns.

Place cell activity of hippocampal pyramidal cells has been described as the cognitive substrate of spatial memory. Replay is observed during hippocampal sharp-wave-ripple-associated population burst events (PBEs) and is critical for consolidation and recall-guided behaviors. PBE activity has historically been analyzed as a phenomenon subordinate to the place code. Here, we use hidden Markov models to study PBEs observed in rats during exploration of both linear mazes and open fields. We demonstrate that estimated models are consistent with a spatial map of the environment, and can even decode animals' positions during behavior. Moreover, we demonstrate the model can be used to identify hippocampal replay without recourse to the place code, using only PBE model congruence. These results suggest that downstream regions may rely on PBEs to provide a substrate for memory. Additionally, by forming models independent of animal behavior, we lay the groundwork for studies of non-spatial memory.