State-dependent architecture of thalamic reticular subnetworks.

Behavioral state is known to influence interactions between thalamus and cortex, which are important for sensation, action, and cognition. The thalamic reticular nucleus (TRN) is hypothesized to regulate thalamo-cortical interactions, but the underlying functional architecture of this process and its state dependence are unknown. By combining the first TRN ensemble recording with psychophysics and connectivity-based optogenetic tagging, we found reticular circuits to be composed of distinct subnetworks. While activity of limbic-projecting TRN neurons positively correlates with arousal, sensory-projecting neurons participate in spindles and show elevated synchrony by slow waves during sleep. Sensory-projecting neurons are suppressed by attentional states, demonstrating that their gating of thalamo-cortical interactions is matched to behavioral state. Bidirectional manipulation of attentional performance was achieved through subnetwork-specific optogenetic stimulation. Together, our findings provide evidence for differential inhibition of thalamic nuclei across brain states, where the TRN separately controls external sensory and internal limbic processing facilitating normal cognitive function. PAPERFLICK:

Animal-to-Animal Variability in Partial Hippocampal Remapping in Repeated Environments.

Hippocampal place cells form a map of the environment of an animal. Changes in the hippocampal map can be brought about in a number of ways, including changes to the environment, task, internal state of the subject, and the passage of time. These changes in the hippocampal map have been called remapping. In this study, we examine remapping during repeated exposure to the same environment. Different animals can have different remapping responses to the same changes. This variability across animals in remapping behavior is not well understood. In this work, we analyzed electrophysiological recordings from the CA3 region of the hippocampus performed by Alme et al. (2014), in which five male rats were exposed to 11 different environments, including a variety of repetitions of those environments. To compare the hippocampal maps between two experiences, we computed average rate map correlation coefficients. We found changes in the hippocampal maps between different sessions in the same environment. These changes consisted of partial remapping, a form of remapping in which some place cells maintain their place fields, whereas other place cells remap their place fields. Each animal exhibited partial remapping differently. We discovered that the heterogeneity in hippocampal representational changes across animals is structured; individual animals had consistently different levels of partial remapping across a range of independent comparisons. Our findings highlight that partial hippocampal remapping between repeated environments depends on animal-specific factors.SIGNIFICANCE STATEMENT Context identification is a difficult problem. Animals are not provided with objective context identity labels, so they must infer which experiences come from which contexts. Different animals may have different strategies for performing this inference. We find that different animals have stereotypically different extents of partial hippocampal remapping, a neural correlate of subjective assessment of context identity.

How our understanding of memory replay evolves.

Memory reactivations and replay, widely reported in the hippocampus and cortex across species, have been implicated in memory consolidation, planning, and spatial and skill learning. Technological advances in electrophysiology, calcium imaging, and human neuroimaging techniques have enabled neuroscientists to measure large-scale neural activity with increasing spatiotemporal resolution and have provided opportunities for developing robust analytic methods to identify memory replay. In this article, we first review a large body of historically important and representative memory replay studies from the animal and human literature. We then discuss our current understanding of memory replay functions in learning, planning, and memory consolidation and further discuss the progress in computational modeling that has contributed to these improvements. Next, we review past and present analytic methods for replay analyses and discuss their limitations and challenges. Finally, looking ahead, we discuss some promising analytic methods for detecting nonstereotypical, behaviorally nondecodable structures from large-scale neural recordings. We argue that seamless integration of multisite recordings, real-time replay decoding, and closed-loop manipulation experiments will be essential for delineating the role of memory replay in a wide range of cognitive and motor functions.

Thalamocortical synchronization during induction and emergence from propofol-induced unconsciousness.

General anesthesia (GA) is a reversible drug-induced state of altered arousal required for more than 60,000 surgical procedures each day in the United States alone. Sedation and unconsciousness under GA are associated with stereotyped electrophysiological oscillations that are thought to reflect profound disruptions of activity in neuronal circuits that mediate awareness and cognition. Computational models make specific predictions about the role of the cortex and thalamus in these oscillations. In this paper, we provide in vivo evidence in rats that alpha oscillations (10-15 Hz) induced by the commonly used anesthetic drug propofol are synchronized between the thalamus and the medial prefrontal cortex. We also show that at deep levels of unconsciousness where movement ceases, coherent thalamocortical delta oscillations (1-5 Hz) develop, distinct from concurrent slow oscillations (0.1-1 Hz). The structure of these oscillations in both cortex and thalamus closely parallel those observed in the human electroencephalogram during propofol-induced unconsciousness. During emergence from GA, this synchronized activity dissipates in a sequence different from that observed during loss of consciousness. A possible explanation is that recovery from anesthesia-induced unconsciousness follows a "boot-up" sequence actively driven by ascending arousal centers. The involvement of medial prefrontal cortex suggests that when these oscillations (alpha, delta, slow) are observed in humans, self-awareness and internal consciousness would be impaired if not abolished. These studies advance our understanding of anesthesia-induced unconsciousness and altered arousal and further establish principled neurophysiological markers of these states.

Temporal coding and rate remapping: Representation of nonspatial information in the hippocampus.

Hippocampal place cells represent nonspatial information through a process called rate remapping, which involves a change in the firing rate of a place cell without changes in its spatial specificity. However, many hippocampal phenomena occur on very short time scales over which long-term average firing rates are not an appropriate description of activity. To understand how rate remapping relates to fine-scale temporal firing phenomena, we asked how rate remapping affected burst firing and trial-to-trial spike count variability. In addition, we looked at how rate remapping relates to the theta-frequency oscillations of the hippocampus, which are thought to temporally organize firing on time scales faster than 100 ms. We found that theta phase coding was preserved through changes in firing rate due to rate remapping. Interestingly, rate remapping in CA1 in response to task demands preferentially occurred during the first half of the theta cycle. The other half of the theta cycle contained preferential expression of phase precession, a phenomenon associated with place cell sequences, in agreement with previous results. This difference of place cell coding during different halves of the theta cycle supports recent theoretical suggestions that different processes occur during the two halves of the theta cycle. The differentiation between the halves of the theta cycle was not clear in recordings from CA3 during rate remapping induced by task-irrelevant sensory changes. These findings provide new insight into the way that temporal coding is utilized in the hippocampus and how rate remapping is expressed through that temporal code.

Damage-associated molecular patterns in resuscitated hemorrhagic shock are mitigated by peritoneal fluid administration.

Conventional resuscitation (CR) of hemorrhagic shock (HS), a significant cause of trauma mortality, is intravenous blood and fluids. CR restores central hemodynamics, but vital organ flow can drop, causing hypoperfusion, hypoxia, damage-associated molecular patterns (DAMPs), and remote organ dysfunction (i.e., lung). CR plus direct peritoneal resuscitation (DPR) prevents intestinal and hepatic hypoperfusion. We hypothesized that DPR prevents lung injury in HS/CR by altering DAMPs. Anesthetized male Sprague-Dawley rats were randomized to groups ( n = 8/group) in one of two sets: 1) sham (no HS, CR, or DPR), 2) HS/CR (HS = 40% mean arterial pressure (MAP) for 60 min, CR = shed blood + 2 volumes normal saline), or 3) HS/CR + DPR. The first set underwent whole lung blood flow by colorimetric microspheres. The second set underwent tissue collection for Luminex, ELISAs, and histopathology. Lipopolysaccharide (LPS) and DAMPs were measured in serum and/or lung, including cytokines, hyaluronic acid (HA), high-mobility group box 1 (HMGB1), Toll-like receptor 4 (TLR4), myeloid differentiation primary response 88 protein (MYD88), and TIR-domain-containing adapter-inducing interferon-ß (TRIF). Statistics were by ANOVA and Tukey-Kramer test with a priori P < 0.05. HS/CR increased serum LPS, HA, HMGB1, and some cytokines [interleukin (IL)-1α, IL-1ß, IL-6, and interferon-γ]. Lung TLR4 and MYD88 were increased but not TRIF compared with Shams. HS/CR + DPR decreased LPS, HA, cytokines, HMGB1, TLR4, and MYD88 levels but did not alter TRIF compared with HS/CR. The data suggest that gut-derived DAMPs can be modulated by adjunctive DPR to prevent activation of lung TLR-4-mediated processes. Also, DPR improved lung blood flow and reduced lung tissue injury. Adjunctive DPR in HS/CR potentially improves morbidity and mortality by downregulating the systemic DAMP response.

Optogenetic activation of cholinergic neurons in the PPT or LDT induces REM sleep.

Rapid eye movement (REM) sleep is an important component of the natural sleep/wake cycle, yet the mechanisms that regulate REM sleep remain incompletely understood. Cholinergic neurons in the mesopontine tegmentum have been implicated in REM sleep regulation, but lesions of this area have had varying effects on REM sleep. Therefore, this study aimed to clarify the role of cholinergic neurons in the pedunculopontine tegmentum (PPT) and laterodorsal tegmentum (LDT) in REM sleep generation. Selective optogenetic activation of cholinergic neurons in the PPT or LDT during non-REM (NREM) sleep increased the number of REM sleep episodes and did not change REM sleep episode duration. Activation of cholinergic neurons in the PPT or LDT during NREM sleep was sufficient to induce REM sleep.

A Novel Nonparametric Approach for Neural Encoding and Decoding Models of Multimodal Receptive Fields.

Pyramidal neurons recorded from the rat hippocampus and entorhinal cortex, such as place and grid cells, have diverse receptive fields, which are either unimodal or multimodal. Spiking activity from these cells encodes information about the spatial position of a freely foraging rat. At fine timescales, a neuron's spike activity also depends significantly on its own spike history. However, due to limitations of current parametric modeling approaches, it remains a challenge to estimate complex, multimodal neuronal receptive fields while incorporating spike history dependence. Furthermore, efforts to decode the rat's trajectory in one- or two-dimensional space from hippocampal ensemble spiking activity have mainly focused on spike history-independent neuronal encoding models. In this letter, we address these two important issues by extending a recently introduced nonparametric neural encoding framework that allows modeling both complex spatial receptive fields and spike history dependencies. Using this extended nonparametric approach, we develop novel algorithms for decoding a rat's trajectory based on recordings of hippocampal place cells and entorhinal grid cells. Results show that both encoding and decoding models derived from our new method performed significantly better than state-of-the-art encoding and decoding models on 6 minutes of test data. In addition, our model's performance remains invariant to the apparent modality of the neuron's receptive field.

Bayesian decoding using unsorted spikes in the rat hippocampus.

A fundamental task in neuroscience is to understand how neural ensembles represent information. Population decoding is a useful tool to extract information from neuronal populations based on the ensemble spiking activity. We propose a novel Bayesian decoding paradigm to decode unsorted spikes in the rat hippocampus. Our approach uses a direct mapping between spike waveform features and covariates of interest and avoids accumulation of spike sorting errors. Our decoding paradigm is nonparametric, encoding model-free for representing stimuli, and extracts information from all available spikes and their waveform features. We apply the proposed Bayesian decoding algorithm to a position reconstruction task for freely behaving rats based on tetrode recordings of rat hippocampal neuronal activity. Our detailed decoding analyses demonstrate that our approach is efficient and better utilizes the available information in the nonsortable hash than the standard sorting-based decoding algorithm. Our approach can be adapted to an online encoding/decoding framework for applications that require real-time decoding, such as brain-machine interfaces.

Neural representation of spatial topology in the rodent hippocampus.

Pyramidal cells in the rodent hippocampus often exhibit clear spatial tuning in navigation. Although it has been long suggested that pyramidal cell activity may underlie a topological code rather than a topographic code, it remains unclear whether an abstract spatial topology can be encoded in the ensemble spiking activity of hippocampal place cells. Using a statistical approach developed previously, we investigate this question and related issues in greater detail. We recorded ensembles of hippocampal neurons as rodents freely foraged in one- and two-dimensional spatial environments and used a "decode-to-uncover" strategy to examine the temporally structured patterns embedded in the ensemble spiking activity in the absence of observed spatial correlates during periods of rodent navigation or awake immobility. Specifically, the spatial environment was represented by a finite discrete state space. Trajectories across spatial locations ("states") were associated with consistent hippocampal ensemble spiking patterns, which were characterized by a state transition matrix. From this state transition matrix, we inferred a topology graph that defined the connectivity in the state space. In both one- and two-dimensional environments, the extracted behavior patterns from the rodent hippocampal population codes were compared against randomly shuffled spike data. In contrast to a topographic code, our results support the efficiency of topological coding in the presence of sparse sample size and fuzzy space mapping. This computational approach allows us to quantify the variability of ensemble spiking activity, examine hippocampal population codes during off-line states, and quantify the topological complexity of the environment.

Pix2HDR - A pixel-wise acquisition and deep learning-based synthesis approach for high-speed HDR videos.

Accurately capturing dynamic scenes with wideranging motion and light intensity is crucial for many vision applications. However, acquiring high-speed high dynamic range (HDR) video is challenging because the camera's frame rate restricts its dynamic range. Existing methods sacrifice speed to acquire multi-exposure frames. Yet, misaligned motion in these frames can still pose complications for HDR fusion algorithms, resulting in artifacts. Instead of frame-based exposures, we sample the videos using individual pixels at varying exposures and phase offsets. Implemented on a monochrome pixel-wise programmable image sensor, our sampling pattern captures fast motion at a high dynamic range. We then transform pixel-wise outputs into an HDR video using end-to-end learned weights from deep neural networks, achieving high spatiotemporal resolution with minimized motion blurring. We demonstrate aliasing-free HDR video acquisition at 1000 FPS, resolving fast motion under low-light conditions and against bright backgrounds - both challenging conditions for conventional cameras. By combining the versatility of pixel-wise sampling patterns with the strength of deep neural networks at decoding complex scenes, our method greatly enhances the vision system's adaptability and performance in dynamic conditions.

Spike-wave discharges during low-current thalamic deep brain stimulation in mice.

BACKGROUND: Deep brain stimulation of central thalamus (CT-DBS) has potential for modulating states of consciousness, but it can also trigger spike-wave discharges (SWDs). OBJECTIVES: To report the probability of inducing SWDs during CT-DBS in awake mice. METHODS: Mice were implanted with electrodes to deliver unilateral and bilateral CT-DBS at different frequencies while recording EEG. We titrated stimulation current by gradually increasing it at each frequency until an SWD appeared. Subsequent stimulations to test arousal modulation were performed at the current one step below the current that caused an SWD during titration. RESULTS: In 2.21% of the test stimulations (10 out of 12 mice), CT-DBS caused SWDs at currents lower than the titrated current, at currents as low as 20 uA. CONCLUSION: Our study found a small but significant probability of inducing SWDs even after titration and at relatively low currents. EEG should be closely monitored for SWDs when performing CT-DBS in both research and clinical settings.

Imaging the voltage of neurons distributed across entire brains of larval zebrafish.

Neurons interact in networks distributed throughout the brain. Although much effort has focused on whole-brain calcium imaging, recent advances in genetically encoded voltage indicators (GEVIs) raise the possibility of imaging voltage of neurons distributed across brains. To achieve this, a microscope must image at high volumetric rate and signal-to-noise ratio. We present a remote scanning light-sheet microscope capable of imaging GEVI-expressing neurons distributed throughout entire brains of larval zebrafish at a volumetric rate of 200.8 Hz. We measured voltage of ∼1/3 of the neurons of the brain, distributed throughout. We observed that neurons firing at different times during a sequence were located at different brain locations, for sequences elicited by a visual stimulus, which mapped onto locations throughout the optic tectum, as well as during stimulus-independent bursts, which mapped onto locations in the cerebellum and medulla. Whole-brain voltage imaging may open up frontiers in the fundamental operation of neural systems.

A unified open-source platform for multimodal neural recording and perturbation during naturalistic behavior.

Behavioral neuroscience faces two conflicting demands: long-duration recordings from large neural populations and unimpeded animal behavior. To meet this challenge, we developed ONIX, an open-source data acquisition system with high data throughput (2GB/sec) and low closed-loop latencies (<1ms) that uses a novel 0.3 mm thin tether to minimize behavioral impact. Head position and rotation are tracked in 3D and used to drive active commutation without torque measurements. ONIX can acquire from combinations of passive electrodes, Neuropixels probes, head-mounted microscopes, cameras, 3D-trackers, and other data sources. We used ONIX to perform uninterrupted, long (~7 hours) neural recordings in mice as they traversed complex 3-dimensional terrain. ONIX allowed exploration with similar mobility as non-implanted animals, in contrast to conventional tethered systems which restricted movement. By combining long recordings with full mobility, our technology will enable new progress on questions that require high-quality neural recordings during ethologically grounded behaviors.

Uncovering spatial topology represented by rat hippocampal population neuronal codes.

Hippocampal population codes play an important role in representation of spatial environment and spatial navigation. Uncovering the internal representation of hippocampal population codes will help understand neural mechanisms of the hippocampus. For instance, uncovering the patterns represented by rat hippocampus (CA1) pyramidal cells during periods of either navigation or sleep has been an active research topic over the past decades. However, previous approaches to analyze or decode firing patterns of population neurons all assume the knowledge of the place fields, which are estimated from training data a priori. The question still remains unclear how can we extract information from population neuronal responses either without a priori knowledge or in the presence of finite sampling constraint. Finding the answer to this question would leverage our ability to examine the population neuronal codes under different experimental conditions. Using rat hippocampus as a model system, we attempt to uncover the hidden "spatial topology" represented by the hippocampal population codes. We develop a hidden Markov model (HMM) and a variational Bayesian (VB) inference algorithm to achieve this computational goal, and we apply the analysis to extensive simulation and experimental data. Our empirical results show promising direction for discovering structural patterns of ensemble spike activity during periods of active navigation. This study would also provide useful insights for future exploratory data analysis of population neuronal codes during periods of sleep.

Reverse replay of behavioural sequences in hippocampal place cells during the awake state.

The hippocampus has long been known to be involved in spatial navigational learning in rodents, and in memory for events in rodents, primates and humans. A unifying property of both navigation and event memory is a requirement for dealing with temporally sequenced information. Reactivation of temporally sequenced memories for previous behavioural experiences has been reported in sleep in rats. Here we report that sequential replay occurs in the rat hippocampus during awake periods immediately after spatial experience. This replay has a unique form, in which recent episodes of spatial experience are replayed in a temporally reversed order. This replay is suggestive of a role in the evaluation of event sequences in the manner of reinforcement learning models. We propose that such replay might constitute a general mechanism of learning and memory.

Reversal learning: It's just a phase.

Being able to let go of behaviors that are no longer valuable and adopt actions that achieve the same outcome is fundamental for animal survival. A new study offers clues on the neural mechanisms that allow animals to reverse their behavior as needed.

Artificial intelligence insights into hippocampal processing.

Advances in artificial intelligence, machine learning, and deep neural networks have led to new discoveries in human and animal learning and intelligence. A recent artificial intelligence agent in the DeepMind family, muZero, can complete a variety of tasks with limited information about the world in which it is operating and with high uncertainty about features of current and future space. To perform, muZero uses only three functions that are general yet specific enough to allow learning across a variety of tasks without overgeneralization across different contexts. Similarly, humans and animals are able to learn and improve in complex environments while transferring learning from other contexts and without overgeneralizing. In particular, the mammalian extrahippocampal system (eHPCS) can guide spatial decision making while simultaneously encoding and processing spatial and contextual information. Like muZero, the eHPCS is also able to adjust contextual representations depending on the degree and significance of environmental changes and environmental cues. In this opinion, we will argue that the muZero functions parallel those of the hippocampal system. We will show that the different components of the muZero model provide a framework for thinking about generalizable learning in the eHPCS, and that the evaluation of how transitions in cell representations occur between similar and distinct contexts can be informed by advances in artificial intelligence agents such as muZero. We additionally explain how advances in AI agents will provide frameworks and predictions by which to investigate the expected link between state changes and neuronal firing. Specifically, we will discuss testable predictions about the eHPCS, including the functions of replay and remapping, informed by the mechanisms behind muZero learning. We conclude with additional ways in which agents such as muZero can aid in illuminating prospective questions about neural functioning, as well as how these agents may shed light on potential expected answers.

A consensus statement on detection of hippocampal sharp wave ripples and differentiation from other fast oscillations.

Decades of rodent research have established the role of hippocampal sharp wave ripples (SPW-Rs) in consolidating and guiding experience. More recently, intracranial recordings in humans have suggested their role in episodic and semantic memory. Yet, common standards for recording, detection, and reporting do not exist. Here, we outline the methodological challenges involved in detecting ripple events and offer practical recommendations to improve separation from other high-frequency oscillations. We argue that shared experimental, detection, and reporting standards will provide a solid foundation for future translational discovery.

Computing confidence intervals for point process models.

Characterizing neural spiking activity as a function of intrinsic and extrinsic factors is important in neuroscience. Point process models are valuable for capturing such information; however, the process of fully applying these models is not always obvious. A complete model application has four broad steps: specification of the model, estimation of model parameters given observed data, verification of the model using goodness of fit, and characterization of the model using confidence bounds. Of these steps, only the first three have been applied widely in the literature, suggesting the need to dedicate a discussion to how the time-rescaling theorem, in combination with parametric bootstrap sampling, can be generally used to compute confidence bounds of point process models. In our first example, we use a generalized linear model of spiking propensity to demonstrate that confidence bounds derived from bootstrap simulations are consistent with those computed from closed-form analytic solutions. In our second example, we consider an adaptive point process model of hippocampal place field plasticity for which no analytical confidence bounds can be derived. We demonstrate how to simulate bootstrap samples from adaptive point process models, how to use these samples to generate confidence bounds, and how to statistically test the hypothesis that neural representations at two time points are significantly different. These examples have been designed as useful guides for performing scientific inference based on point process models.