A Distributed Neural Code in the Dentate Gyrus and in CA1

A method to find temporal structure of neuronal coactivity patterns with across-trial correlations

BACKGROUND

The across-trial correlation of neurons' coactivity patterns emerges to be important for information coding, but methods for finding their temporal structures remain largely unexplored.

NEW METHOD

In the present study, we propose a method to find time clusters in which coactivity patterns of neurons are correlated across trials. We transform the multidimensional neural activity at each timing into a coactivity pattern of binary states, and predict the coactivity patterns at different timings. We devise a method suitable for these coactivity pattern predictions, call general event prediction. Cross-temporal prediction accuracy is then used to estimate across-trial correlations between coactivity patterns at two timings. We extract time clusters from the cross-temporal prediction accuracy by a modified k-means algorithm.

RESULTS

The feasibility of the proposed method is verified through simulations based on ground truth. We apply the proposed method to a calcium imaging dataset recorded from the motor cortex of mice, and demonstrate time clusters of motor cortical coactivity patterns during a motor task.

COMPARISON WITH EXISTING METHODS

While the existing cosine similarity method, which does not account for across-trial correlation, shows temporal structures only for contralateral neural responses, the proposed method reveals those for both contralateral and ipsilateral neural responses, demonstrating the effect of across-trial correlations.

CONCLUSIONS

This study introduces a novel method for measuring the temporal structure of neuronal ensemble activity.

Computational role of structure in neural activity and connectivity

One major challenge of neuroscience is identifying structure in seemingly disorganized neural activity. Different types of structure have different computational implications that can help neuroscientists understand the functional role of a particular brain area. Here, we outline a unified approach to characterize structure by inspecting the representational geometry and the modularity properties of the recorded activity and show that a similar approach can also reveal structure in connectivity. We start by setting up a general framework for determining geometry and modularity in activity and connectivity and relating these properties with computations performed by the network. We then use this framework to review the types of structure found in recent studies of model networks performing three classes of computations.

Deciphering brain cellular and behavioral mechanisms: Insights from single-cell and spatial RNA sequencing

The brain is a complex computing system composed of a multitude of interacting neurons. The computational outputs of this system determine the behavior and perception of every individual. Each brain cell expresses thousands of genes that dictate the cell's function and physiological properties. Therefore, deciphering the molecular expression of each cell is of great significance for understanding its characteristics and role in brain function. Additionally, the positional information of each cell can provide crucial insights into their involvement in local brain circuits. In this review, we briefly overview the principles of single-cell RNA sequencing and spatial transcriptomics, the potential issues and challenges in their data processing, and their applications in brain research. We further outline several promising directions in neuroscience that could be integrated with single-cell RNA sequencing, including neurodevelopment, the identification of novel brain microstructures, cognition and behavior, neuronal cell positioning, molecules and cells related to advanced brain functions, sleep-wake cycles/circadian rhythms, and computational modeling of brain function. We believe that the deep integration of these directions with single-cell and spatial RNA sequencing can contribute significantly to understanding the roles of individual cells or cell types in these specific functions, thereby making important contributions to addressing critical questions in those fields. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.

Effects of unilateral hippocampal surgical procedures needed for calcium imaging on mouse behavior and adult hippocampal neurogenesis

Hippocampus is essential for episodic memory formation, lesion studies demonstrating its role especially in processing spatial and temporal information. Further, adult hippocampal neurogenesis (AHN) in the dentate gyrus (DG) has also been linked to learning. To study hippocampal neuronal activity during events like learning, in vivo calcium imaging has become increasingly popular. It relies on the use of adeno-associated viral (AAV) vectors, which seem to lead to a decrease in AHN when applied on the DG. More notably, imaging requires the implantation of a relatively large lens into the tissue. Here, we examined how injection of an AAV vector and implantation of a 1-mm-diameter lens into the dorsal DG routinely used to image calcium activity impact the behavior of adult male C57BL/6 mice. To this aim, we conducted open-field, object-recognition and object-location tasks at baseline, after AAV vector injection, and after lens implantation. Finally, we determined AHN from hippocampal slices using a doublecortin-antibody. According to our results, the operations needed for in vivo imaging of the dorsal DG did not have adverse effects on behavior, although we noticed a decrease in AHN ipsilaterally to the operations. Thus, our results suggest that in vivo imaging can be safely used to, for example, correlate patterns of calcium activity with learned behavior. One should still keep in mind that the defects on the operated side might be functionally compensated by the (hippocampus in the) contralateral hemisphere.

Context and space coding in mossy cell population activity

The dentate gyrus plays a key role in the discrimination of memories by segregating and storing similar episodes. Whether hilar mossy cells, which constitute a major excitatory principal cell type in the mammalian hippocampus, contribute to this decorrelation function has remained largely unclear. Using two-photon calcium imaging of head-fixed mice performing a spatial virtual reality task, we show that mossy cell populations robustly discriminate between familiar and novel environments. The degree of discrimination depends on the extent of visual cue differences between contexts. A context decoder revealed that successful environmental classification is explained mainly by activity difference scores of mossy cells. By decoding mouse position, we reveal that in addition to place cells, the coordinated activity among active mossy cells markedly contributes to the encoding of space. Thus, by decorrelating context information according to the degree of environmental differences, mossy cell populations support pattern separation processes within the dentate gyrus.

Remapping revisited: how the hippocampus represents different spaces

The representation of distinct spaces by hippocampal place cells has been linked to changes in their place fields (the locations in the environment where the place cells discharge strongly), a phenomenon that has been termed 'remapping'. Remapping has been assumed to be accompanied by the reorganization of subsecond cofiring relationships among the place cells, potentially maximizing hippocampal information coding capacity. However, several observations challenge this standard view. For example, place cells exhibit mixed selectivity, encode non-positional variables, can have multiple place fields and exhibit unreliable discharge in fixed environments. Furthermore, recent evidence suggests that, when measured at subsecond timescales, the moment-to-moment cofiring of a pair of cells in one environment is remarkably similar in another environment, despite remapping. Here, I propose that remapping is a misnomer for the changes in place fields across environments and suggest instead that internally organized manifold representations of hippocampal activity are actively registered to different environments to enable navigation, promote memory and organize knowledge.

Mixed selectivity: Cellular computations for complexity

The property of mixed selectivity has been discussed at a computational level and offers a strategy to maximize computational power by adding versatility to the functional role of each neuron. Here, we offer a biologically grounded implementational-level mechanistic explanation for mixed selectivity in neural circuits. We define pure, linear, and nonlinear mixed selectivity and discuss how these response properties can be obtained in simple neural circuits. Neurons that respond to multiple, statistically independent variables display mixed selectivity. If their activity can be expressed as a weighted sum, then they exhibit linear mixed selectivity; otherwise, they exhibit nonlinear mixed selectivity. Neural representations based on diverse nonlinear mixed selectivity are high dimensional; hence, they confer enormous flexibility to a simple downstream readout neural circuit. However, a simple neural circuit cannot possibly encode all possible mixtures of variables simultaneously, as this would require a combinatorially large number of mixed selectivity neurons. Gating mechanisms like oscillations and neuromodulation can solve this problem by dynamically selecting which variables are mixed and transmitted to the readout.

Modulation of aggression by social novelty recognition memory in the hippocampal CA2 region

The dorsal CA2 subregion (dCA2) of the hippocampus exerts a critical role in social novelty recognition (SNR) memory and in the promotion of social aggression. Whether the social aggression and SNR memory functions of dCA2 are related or represent independent processes is unknown. Here we investigated the hypotheses that an animal is more likely to attack a novel compared to familiar animal and that dCA2 promotes social aggression through its ability to discriminate between novel and familiar conspecifics. To test these ideas, we conducted a multi-day resident intruder (R-I) test of aggression towards novel and familiar conspecifics. We found that mice were more likely to attack a novel compared to familiarized intruder and that silencing of dCA2 caused a more profound inhibition of aggression towards a novel than familiarized intruder. To explore whether and how dCA2 pyramidal neurons encode aggression, we recorded their activity using microendoscopic calcium imaging throughout the days of the R-I test. We found that a fraction of dCA2 neurons were selectively activated or inhibited during exploration, dominance, and attack behaviors and that these signals were enhanced during interaction with a novel compared to familiarized conspecific. Based on dCA2 population activity, a set of binary linear classifiers accurately decoded whether an animal was engaged in each of these forms of social behavior. Of particular interest, the accuracy of decoding aggression was greater with novel compared to familiarized intruders, with significant cross-day decoding using the same familiar animal on each day but not for a familiar-novel pair. Together, these findings demonstrate that dCA2 integrates information about social novelty with signals of behavioral state to promote aggression towards novel conspecifics.

Differential disruptions in population coding along the dorsal-ventral axis of CA1 in the APP/PS1 mouse model of Aβ pathology

Alzheimer's Disease (AD) is characterized by a range of behavioral alterations, including memory loss and psychiatric symptoms. While there is evidence that molecular pathologies, such as amyloid beta (Aβ), contribute to AD, it remains unclear how this histopathology gives rise to such disparate behavioral deficits. One hypothesis is that Aβ exerts differential effects on neuronal circuits across brain regions, depending on the neurophysiology and connectivity of different areas. To test this, we recorded from large neuronal populations in dorsal CA1 (dCA1) and ventral CA1 (vCA1), two hippocampal areas known to be structurally and functionally diverse, in the APP/PS1 mouse model of amyloidosis. Despite similar levels of Aβ pathology, dCA1 and vCA1 showed distinct disruptions in neuronal population activity as animals navigated a virtual reality environment. In dCA1, pairwise correlations and entropy, a measure of the diversity of activity patterns, were decreased in APP/PS1 mice relative to age-matched C57BL/6 controls. However, in vCA1, APP/PS1 mice had increased pair-wise correlations and entropy as compared to age matched controls. Finally, using maximum entropy models, we connected the microscopic features of population activity (correlations) to the macroscopic features of the population code (entropy). We found that the models' performance increased in predicting dCA1 activity, but decreased in predicting vCA1 activity, in APP/PS1 mice relative to the controls. Taken together, we found that Aβ exerts distinct effects across different hippocampal regions, suggesting that the various behavioral deficits of AD may reflect underlying heterogeneities in neuronal circuits and the different disruptions that Aβ pathology causes in those circuits.

Control of working memory by phase-amplitude coupling of human hippocampal neurons

Retaining information in working memory is a demanding process that relies on cognitive control to protect memoranda-specific persistent activity from interference1,2. However, how cognitive control regulates working memory storage is unclear. Here we show that interactions of frontal control and hippocampal persistent activity are coordinated by theta-gamma phase-amplitude coupling (TG-PAC). We recorded single neurons in the human medial temporal and frontal lobe while patients maintained multiple items in their working memory. In the hippocampus, TG-PAC was indicative of working memory load and quality. We identified cells that selectively spiked during nonlinear interactions of theta phase and gamma amplitude. The spike timing of these PAC neurons was coordinated with frontal theta activity when cognitive control demand was high. By introducing noise correlations with persistently active neurons in the hippocampus, PAC neurons shaped the geometry of the population code. This led to higher-fidelity representations of working memory content that were associated with improved behaviour. Our results support a multicomponent architecture of working memory1,2, with frontal control managing maintenance of working memory content in storage-related areas3-5. Within this framework, hippocampal TG-PAC integrates cognitive control and working memory storage across brain areas, thereby suggesting a potential mechanism for top-down control over sensory-driven processes.

Encoding of Predictive Associations in Human Prefrontal and Medial Temporal Neurons During Pavlovian Appetitive Conditioning

Pavlovian conditioning is thought to involve the formation of learned associations between stimuli and values, and between stimuli and specific features of outcomes. Here, we leveraged human single neuron recordings in ventromedial prefrontal, dorsomedial frontal, hippocampus, and amygdala while patients of both sexes performed an appetitive Pavlovian conditioning task probing both stimulus-value and stimulus-stimulus associations. Ventromedial prefrontal cortex encoded predictive value along with the amygdala, and also encoded predictions about the identity of stimuli that would subsequently be presented, suggesting a role for neurons in this region in encoding predictive information beyond value. Unsigned error signals were found in dorsomedial frontal areas and hippocampus, potentially supporting learning of non-value related outcome features. Our findings implicate distinct human prefrontal and medial temporal neuronal populations in mediating predictive associations which could partially support model-based mechanisms during Pavlovian conditioning.

Tuned geometries of hippocampal representations meet the computational demands of social memory

Social memory consists of two processes: the detection of familiar compared with novel conspecifics and the detailed recollection of past social episodes. We investigated the neural bases for these processes using calcium imaging of dorsal CA2 hippocampal pyramidal neurons, known to be important for social memory, during social/spatial encounters with novel conspecifics and familiar littermates. Whereas novel individuals were represented in a low-dimensional geometry that allows for generalization of social identity across different spatial locations and of location across different identities, littermates were represented in a higher-dimensional geometry that supports high-capacity memory storage. Moreover, familiarity was represented in an abstract format, independent of individual identity. The degree to which familiarity increased the dimensionality of CA2 representations for individual mice predicted their performance in a social novelty recognition memory test. Thus, by tuning the geometry of structured neural activity, CA2 is able to meet the demands of distinct social memory processes.

Efficient encoding of aversive location by CA3 long-range projections

Memorizing locations that are harmful or dangerous is a key capability of all organisms and requires an integration of affective and spatial information. In mammals, the dorsal hippocampus mainly processes spatial information, while the intermediate to ventral hippocampal divisions receive affective information via the amygdala. However, how spatial and aversive information is integrated is currently unknown. To address this question, we recorded the activity of hippocampal long-range CA3 axons at single-axon resolution in mice forming an aversive spatial memory. We show that intermediate CA3 to dorsal CA3 (i-dCA3) projections rapidly overrepresent areas preceding the location of an aversive stimulus due to a spatially selective addition of new place-coding axons followed by spatially non-specific stabilization. This sequence significantly improves the encoding of location by the i-dCA3 axon population. These results suggest that i-dCA3 axons transmit a precise, denoised, and stable signal indicating imminent danger to the dorsal hippocampus.

End-to-end deep learning approach to mouse behavior classification from cortex-wide calcium imaging

Deep learning is a powerful tool for neural decoding, broadly applied to systems neuroscience and clinical studies. Interpretable and transparent models that can explain neural decoding for intended behaviors are crucial to identifying essential features of deep learning decoders in brain activity. In this study, we examine the performance of deep learning to classify mouse behavioral states from mesoscopic cortex-wide calcium imaging data. Our convolutional neural network (CNN)-based end-to-end decoder combined with recurrent neural network (RNN) classifies the behavioral states with high accuracy and robustness to individual differences on temporal scales of sub-seconds. Using the CNN-RNN decoder, we identify that the forelimb and hindlimb areas in the somatosensory cortex significantly contribute to behavioral classification. Our findings imply that the end-to-end approach has the potential to be an interpretable deep learning method with unbiased visualization of critical brain regions.

Sequential activity of CA1 hippocampal cells constitutes a temporal memory map for associative learning in mice

Sequential neural dynamics encoded by time cells play a crucial role in hippocampal function. However, the role of hippocampal sequential neural dynamics in associative learning is an open question. We used two-photon Ca2+ imaging of dorsal CA1 (dCA1) neurons in the stratum pyramidale (SP) in head-fixed mice performing a go-no go associative learning task to investigate how odor valence is temporally encoded in this area of the brain. We found that SP cells responded differentially to the rewarded or unrewarded odor. The stimuli were decoded accurately from the activity of the neuronal ensemble, and accuracy increased substantially as the animal learned to differentiate the stimuli. Decoding the stimulus from individual SP cells responding differentially revealed that decision-making took place at discrete times after stimulus presentation. Lick prediction decoded from the ensemble activity of cells in dCA1 correlated linearly with lick behavior. Our findings indicate that sequential activity of SP cells in dCA1 constitutes a temporal memory map used for decision-making in associative learning. VIDEO ABSTRACT.

Scene-selectivity in CA1/subicular complex: Multivoxel pattern analysis at 7T

Prior univariate functional magnetic resonance imaging (fMRI) studies in humans suggest that the anteromedial subicular complex of the hippocampus is a hub for scene-based cognition. However, it is possible that univariate approaches were not sufficiently sensitive to detect scene-related activity in other subfields that have been implicated in spatial processing (e.g., CA1). Further, as connectivity-based functional gradients in the hippocampus do not respect classical subfield boundary definitions, category selectivity may be distributed across anatomical subfields. Region-of-interest approaches, therefore, may limit our ability to observe category selectivity across discrete subfield boundaries. To address these issues, we applied searchlight multivariate pattern analysis to 7T fMRI data of healthy adults who undertook a simultaneous visual odd-one-out discrimination task for scene and non-scene (including face) visual stimuli, hypothesising that scene classification would be possible in multiple hippocampal regions within, but not constrained to, anteromedial subicular complex and CA1. Indeed, we found that the scene-selective searchlight map overlapped not only with anteromedial subicular complex (distal subiculum, pre/para subiculum), but also inferior CA1, alongside posteromedial (including retrosplenial) and parahippocampal cortices. Probabilistic overlap maps revealed gradients of scene category selectivity, with the strongest overlap located in the medial hippocampus, converging with searchlight findings. This was contrasted with gradients of face category selectivity, which had stronger overlap in more lateral hippocampus, supporting ideas of parallel processing streams for these two categories. Our work helps to map the scene, in contrast to, face processing networks within, and connected to, the human hippocampus.

Multi-region calcium imaging in freely behaving mice with ultra-compact head-mounted fluorescence microscopes

To investigate the circuit-level neural mechanisms of behavior, simultaneous imaging of neuronal activity in multiple cortical and subcortical regions is highly desired. Miniature head-mounted microscopes offer the capability of calcium imaging in freely behaving animals. However, implanting multiple microscopes on a mouse brain remains challenging due to space constraints and the cumbersome weight of the equipment. Here, we present TINIscope, a Tightly Integrated Neuronal Imaging microscope optimized for electronic and opto-mechanical design. With its compact and lightweight design of 0.43 g, TINIscope enables unprecedented simultaneous imaging of behavior-relevant activity in up to four brain regions in mice. Proof-of-concept experiments with TINIscope recorded over 1000 neurons in four hippocampal subregions and revealed concurrent activity patterns spanning across these regions. Moreover, we explored potential multi-modal experimental designs by integrating additional modules for optogenetics, electrical stimulation or local field potential recordings. Overall, TINIscope represents a timely and indispensable tool for studying the brain-wide interregional coordination that underlies unrestrained behaviors.

Adult Neurogenesis, Context Encoding, and Pattern Separation: A Pathway for Treating Overgeneralization

In mammals, the subgranular zone of the dentate gyrus is one of two brain regions (with the subventricular zone of the olfactory bulb) that continues to generate new neurons throughout adulthood, a phenomenon known as adult hippocampal neurogenesis (AHN) (Eriksson et al., Nat Med 4:1313-1317, 1998; García-Verdugo et al., J Neurobiol 36:234-248, 1998). The integration of these new neurons into the dentate gyrus (DG) has implications for memory encoding, with unique firing and wiring properties of immature neurons that affect how the hippocampal network encodes and stores attributes of memory. In this chapter, we will describe the process of AHN and properties of adult-born cells as they integrate into the hippocampal circuit and mature. Then, we will discuss some methodological considerations before we review evidence for the role of AHN in two major processes supporting memory that are performed by the DG. First, we will discuss encoding of contextual information for episodic memories and how this is facilitated by AHN. Second, will discuss pattern separation, a major role of the DG that reduces interference for the formation of new memories. Finally, we will review clinical and translational considerations, suggesting that stimulation of AHN may help decrease overgeneralization-a common endophenotype of mood, anxiety, trauma-related, and age-related disorders.

Direct cortical inputs to hippocampal area CA1 transmit complementary signals for goal-directed navigation

The subregions of the entorhinal cortex (EC) are conventionally thought to compute dichotomous representations for spatial processing, with the medial EC (MEC) providing a global spatial map and the lateral EC (LEC) encoding specific sensory details of experience. Yet, little is known about the specific types of information EC transmits downstream to the hippocampus. Here, we exploit in vivo sub-cellular imaging to record from EC axons in CA1 while mice perform navigational tasks in virtual reality (VR). We uncover distinct yet overlapping representations of task, location, and context in both MEC and LEC axons. MEC transmitted highly location- and context-specific codes; LEC inputs were biased by ongoing navigational goals. However, during tasks with reliable reward locations, the animals' position could be accurately decoded from either subregion. Our results revise the prevailing dogma about EC information processing, revealing novel ways spatial and non-spatial information is routed and combined upstream of the hippocampus.

The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience

Although much is known about how single neurons in the hippocampus represent an animal's position, how circuit interactions contribute to spatial coding is less well understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured CA1 cell-cell interactions in male rats during open field exploration. The statistics of these interactions depend on whether the animal is in a familiar or novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the informativeness of their spatial inputs. This structure facilitates linear decodability, making the information easy to read out by downstream circuits. Overall, our findings suggest that the efficient coding hypothesis is not only applicable to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain.SIGNIFICANCE STATEMENT Local circuit interactions play a key role in neural computation and are dynamically shaped by experience. However, measuring and assessing their effects during behavior remains a challenge. Here, we combine techniques from statistical physics and machine learning to develop new tools for determining the effects of local network interactions on neural population activity. This approach reveals highly structured local interactions between hippocampal neurons, which make the neural code more precise and easier to read out by downstream circuits, across different levels of experience. More generally, the novel combination of theory and data analysis in the framework of maximum entropy models enables traditional neural coding questions to be asked in naturalistic settings.

A neural code for time and space in the human brain

Time and space are primary dimensions of human experience. Separate lines of investigation have identified neural correlates of time and space, yet little is known about how these representations converge during self-guided experience. Here, 10 subjects with intracranially implanted microelectrodes play a timed, virtual navigation game featuring object search and retrieval tasks separated by fixed delays. Time cells and place cells activate in parallel during timed navigation intervals, whereas a separate time cell sequence spans inter-task delays. The prevalence, firing rates, and behavioral coding strengths of time cells and place cells are indistinguishable-yet time cells selectively remap between search and retrieval tasks, while place cell responses remain stable. Thus, the brain can represent time and space as overlapping but dissociable dimensions. Time cells and place cells may constitute a biological basis for the cognitive map of spatiotemporal context onto which memories are written.

High-fat diet negatively affects brain markers, cognitive behaviors, and noncognitive behaviors in the rTg4510 tau mouse model

Alzheimer's disease (AD) drastically impacts cognitive and noncognitive behaviors in both humans and animal models. Two hallmark proteins in AD, amyloid-β plaques and tau neurofibrillary tangles, accumulate in regions of the brain critical for learning and memory, including the hippocampus. Poor dietary choices have been shown to exacerbate cognitive deficits seen in AD. In this study, we assessed the effects of a high-fat diet (HFD - 60 kcal% fat) on cognitive & noncognitive behaviors as well as on brain markers in the rTg4510 tau mouse model. While all mice learned the Morris Water Maze (MWM) task, it was noted that on the last day of acquisition female tau mice had a significantly higher latency to find the platform than male tau mice (p < 0.01). Mice given the HFD spent significantly less time in the target quadrant than those given a control diet (CD) (p < 0.05). Tau mice showed impaired burrowing (p < 0.05) and nesting behaviors (p < 0.001) compared to WT mice and HFD administration worsened burrowing in tau mice. Tau mice exhibited greater levels of glial fibrillary acidic protein (GFAP) (p < 0.05) and significantly less hippocampal cell density than WT mice (p < 0.001). We observed trends of HFD mice having greater levels of GFAP and greater average tangle size than CD mice. These results highlight the importance of dietary choices, especially in older populations more susceptible to AD and its effects.

A manifold neural population code for space in hippocampal coactivity dynamics independent of place fields

Hippocampus place cell discharge is temporally unreliable across seconds and days, and place fields are multimodal, suggesting an "ensemble cofiring" spatial coding hypothesis with manifold dynamics that does not require reliable spatial tuning, in contrast to hypotheses based on place field (spatial tuning) stability. We imaged mouse CA1 (cornu ammonis 1) ensembles in two environments across three weeks to evaluate these coding hypotheses. While place fields "remap," being more distinct between than within environments, coactivity relationships generally change less. Decoding location and environment from 1-s ensemble location-specific activity is effective and improves with experience. Decoding environment from cell-pair coactivity relationships is also effective and improves with experience, even after removing place tuning. Discriminating environments from 1-s ensemble coactivity relies crucially on the cells with the most anti-coactive cell-pair relationships because activity is internally organized on a low-dimensional manifold of non-linear coactivity relationships that intermittently reregisters to environments according to the anti-cofiring subpopulation activity.

Decision-Making Time Cells in Hippocampal Dorsal CA1

Sequential neural dynamics encoded by "time cells" play a crucial role in hippocampal function. However, the role of hippocampal sequential neural dynamics in associative learning is an open question. In this manuscript, we used two-photon Ca2+ imaging of dorsal CA1 pyramidal neurons in head-fixed mice performing a go-no-go associative learning task. We found that pyramidal cells responded differentially to the rewarded or unrewarded stimuli. The stimuli were decoded accurately from the activity of the neuronal ensemble, and accuracy increased substantially as the animal learned to differentiate the stimuli. Decoding the stimulus from individual pyramidal cells that responded differentially revealed that decision-making took place at discrete times after stimulus presentation. Lick prediction decoded from the ensemble activity of cells in dCA1 correlated linearly with lick behavior indicating that sequential activity of pyramidal cells in dCA1 constitutes a temporal memory map used for decision-making in associative learning.

A theory of hippocampal theta correlations accounting for extrinsic and intrinsic sequences

Hippocampal place cell sequences have been hypothesized to serve as diverse purposes as the induction of synaptic plasticity, formation and consolidation of long-term memories, or navigation and planning. During spatial behaviors of rodents, sequential firing of place cells at the theta timescale (known as theta sequences) encodes running trajectories, which can be considered as one-dimensional behavioral sequences of traversed locations. In a two-dimensional space, however, each single location can be visited along arbitrary one-dimensional running trajectories. Thus, a place cell will generally take part in multiple different theta sequences, raising questions about how this two-dimensional topology can be reconciled with the idea of hippocampal sequences underlying memory of (one-dimensional) episodes. Here, we propose a computational model of cornu ammonis 3 (CA3) and dentate gyrus (DG), where sensorimotor input drives the direction-dependent (extrinsic) theta sequences within CA3 reflecting the two-dimensional spatial topology, whereas the intrahippocampal CA3-DG projections concurrently produce intrinsic sequences that are independent of the specific running trajectory. Consistent with experimental data, intrinsic theta sequences are less prominent, but can nevertheless be detected during theta activity, thereby serving as running-direction independent landmark cues. We hypothesize that the intrinsic sequences largely reflect replay and preplay activity during non-theta states.

Ventral CA1 Population Codes for Anxiety

The ventral hippocampus is a critical node in the distributed brain network that controls anxiety. Using miniature microscopy and calcium imaging, we recorded ventral CA1 (vCA1) neurons in freely moving mice as they explored variants of classic behavioral assays for anxiety. Unsupervised behavioral segmentation revealed clusters of behavioral motifs that corresponded to exploratory and vigilance-like states. We discovered multiple vCA1 population codes that represented the anxiogenic features of the environment, such as bright light and openness, as well as the moment-to-moment anxiety state of the animals. These population codes possessed distinct generalization properties: neural representations of anxiogenic features were different for open field and elevated plus/zero maze tasks, while neural representations of moment-to-moment anxiety state were similar across both experimental contexts. Our results suggest that anxiety is not tied to the aversive compartments of these mazes but is rather defined by a behavioral state and its corresponding population code that generalizes across environments.

The respective activation and silencing of striatal direct and indirect pathway neurons support behavior encoding

The basal ganglia are known to control actions and modulate movements. Neuronal activity in the two efferent pathways of the dorsal striatum is critical for appropriate behavioral control. Previous evidence has led to divergent conclusions on the respective engagement of both pathways during actions. Using calcium imaging to evaluate how neurons in the direct and indirect pathways encode behaviors during self-paced spontaneous explorations in an open field, we observed that the two striatal pathways exhibit distinct tuning properties. Supervised learning algorithms revealed that direct pathway neurons encode behaviors through their activation, whereas indirect pathway neurons exhibit behavior-specific silencing. These properties remain stable for weeks. Our findings highlight a complementary encoding of behaviors with congruent activations in the direct pathway encoding multiple accessible behaviors in a given context, and in the indirect pathway encoding the suppression of competing behaviors. This model reconciles previous conflicting conclusions on motor encoding in the striatum.

Assessments of dentate gyrus function: discoveries and debates

There has been considerable speculation regarding the function of the dentate gyrus (DG) - a subregion of the mammalian hippocampus - in learning and memory. In this Perspective article, we compare leading theories of DG function. We note that these theories all critically rely on the generation of distinct patterns of activity in the region to signal differences between experiences and to reduce interference between memories. However, these theories are divided by the roles they attribute to the DG during learning and recall and by the contributions they ascribe to specific inputs or cell types within the DG. These differences influence the information that the DG is thought to impart to downstream structures. We work towards a holistic view of the role of DG in learning and memory by first developing three critical questions to foster a dialogue between the leading theories. We then evaluate the extent to which previous studies address our questions, highlight remaining areas of conflict, and suggest future experiments to bridge these theories.

Social odor discrimination and its enhancement by associative learning in the hippocampal CA2 region

Although the hippocampus is crucial for social memory, how social sensory information is combined with contextual information to form episodic social memories remains unknown. Here, we investigated the mechanisms for social sensory information processing using two-photon calcium imaging from hippocampal CA2 pyramidal neurons (PNs)-which are crucial for social memory-in awake head-fixed mice exposed to social and non-social odors. We found that CA2 PNs represent social odors of individual conspecifics and that these representations are refined during associative social odor-reward learning to enhance the discrimination of rewarded compared with unrewarded odors. Moreover, the structure of the CA2 PN population activity enables CA2 to generalize along categories of rewarded versus unrewarded and social versus non-social odor stimuli. Finally, we found that CA2 is important for learning social but not non-social odor-reward associations. These properties of CA2 odor representations provide a likely substrate for the encoding of episodic social memory.

Object-centered population coding in CA1 of the hippocampus

Objects and landmarks are crucial for guiding navigation and must be integrated into the cognitive map of space. Studies of object coding in the hippocampus have primarily focused on activity of single cells. Here, we record simultaneously from large numbers of hippocampal CA1 neurons to determine how the presence of a salient object in the environment alters single-neuron and neural-population activity of the area. The majority of the cells showed some change in their spatial firing patterns when the object was introduced. At the neural-population level, these changes were systematically organized according to the animal's distance from the object. This organization was widely distributed across the cell sample, suggesting that some features of cognitive maps-including object representation-are best understood as emergent properties of neural populations.

Neural dynamics underlying associative learning in the dorsal and ventral hippocampus

Animals associate cues with outcomes and update these associations as new information is presented. This requires the hippocampus, yet how hippocampal neurons track changes in cue-outcome associations remains unclear. Using two-photon calcium imaging, we tracked the same dCA1 and vCA1 neurons across days to determine how responses evolve across phases of odor-outcome learning. Initially, odors elicited robust responses in dCA1, whereas, in vCA1, odor responses primarily emerged after learning and embedded information about the paired outcome. Population activity in both regions rapidly reorganized with learning and then stabilized, storing learned odor representations for days, even after extinction or pairing with a different outcome. Additionally, we found stable, robust signals across CA1 when mice anticipated outcomes under behavioral control but not when mice anticipated an inescapable aversive outcome. These results show how the hippocampus encodes, stores and updates learned associations and illuminates the unique contributions of dorsal and ventral hippocampus.

Control of working memory maintenance by theta-gamma phase amplitude coupling of human hippocampal neurons

Retaining information in working memory (WM) is a demanding process that relies on cognitive control to protect memoranda-specific persistent activity from interference. How cognitive control regulates WM storage, however, remains unknown. We hypothesized that interactions of frontal control and hippocampal persistent activity are coordinated by theta-gamma phase amplitude coupling (TG-PAC). We recorded single neurons in the human medial temporal and frontal lobe while patients maintained multiple items in WM. In the hippocampus, TG-PAC was indicative of WM load and quality. We identified cells that selectively spiked during nonlinear interactions of theta phase and gamma amplitude. These PAC neurons were more strongly coordinated with frontal theta activity when cognitive control demand was high, and they introduced information-enhancing and behaviorally relevant noise correlations with persistently active neurons in the hippocampus. We show that TG-PAC integrates cognitive control and WM storage to improve the fidelity of WM representations and facilitate behavior.

Geometric transformation of cognitive maps for generalization across hippocampal-prefrontal circuits

The ability to abstract information to guide decisions during navigation across changing environments is essential for adaptation and requires the integrity of the hippocampal-prefrontal circuitry. The hippocampus encodes navigational information in a cognitive map, but it remains unclear how cognitive maps are transformed across hippocampal-prefrontal circuits to support abstraction and generalization. Here, we simultaneously record hippocampal-prefrontal ensembles as rats generalize navigational rules across distinct environments. We find that, whereas hippocampal representational maps maintain specificity of separate environments, prefrontal maps generalize across environments. Furthermore, while both maps are structured within a neural manifold of population activity, they have distinct representational geometries. Prefrontal geometry enables abstraction of rule-informative variables, a representational format that generalizes to novel conditions of existing variable classes. Hippocampal geometry lacks such abstraction. Together, these findings elucidate how cognitive maps are structured into distinct geometric representations to support abstraction and generalization while maintaining memory specificity.

Quantitative models of auditory cortical processing

To generate insight from experimental data, it is critical to understand the inter-relationships between individual data points and place them in context within a structured framework. Quantitative modeling can provide the scaffolding for such an endeavor. Our main objective in this review is to provide a primer on the range of quantitative tools available to experimental auditory neuroscientists. Quantitative modeling is advantageous because it can provide a compact summary of observed data, make underlying assumptions explicit, and generate predictions for future experiments. Quantitative models may be developed to characterize or fit observed data, to test theories of how a task may be solved by neural circuits, to determine how observed biophysical details might contribute to measured activity patterns, or to predict how an experimental manipulation would affect neural activity. In complexity, quantitative models can range from those that are highly biophysically realistic and that include detailed simulations at the level of individual synapses, to those that use abstract and simplified neuron models to simulate entire networks. Here, we survey the landscape of recently developed models of auditory cortical processing, highlighting a small selection of models to demonstrate how they help generate insight into the mechanisms of auditory processing. We discuss examples ranging from models that use details of synaptic properties to explain the temporal pattern of cortical responses to those that use modern deep neural networks to gain insight into human fMRI data. We conclude by discussing a biologically realistic and interpretable model that our laboratory has developed to explore aspects of vocalization categorization in the auditory pathway.

The geometry of cortical representations of touch in rodents

Neurons often encode highly heterogeneous non-linear functions of multiple task variables, a signature of a high-dimensional geometry. We studied the representational geometry in the somatosensory cortex of mice trained to report the curvature of objects touched by their whiskers. High-speed videos of the whiskers revealed that the task can be solved by linearly integrating multiple whisker contacts over time. However, the neural activity in somatosensory cortex reflects non-linear integration of spatio-temporal features of the sensory inputs. Although the responses at first appeared disorganized, we identified an interesting structure in the representational geometry: different whisker contacts are disentangled variables represented in approximately, but not fully, orthogonal subspaces of the neural activity space. This geometry allows linear readouts to perform a broad class of tasks of different complexities without compromising the ability to generalize to novel situations.

Electroacupuncture Alleviates Neuroinflammation by Inhibiting the HMGB1 Signaling Pathway in Rats with Sepsis-Associated Encephalopathy

Sepsis-Associated Encephalopathy (SAE) is common in sepsis patients, with high mortality rates. It is believed that neuroinflammation is an important mechanism involved in SAE. High mobility group box 1 protein (HMGB1), as a late pro-inflammatory factor, is significantly increased during sepsis in different brain regions, including the hippocampus. HMGB1 causes neuroinflammation and cognitive impairment through direct binding to advanced glycation end products (RAGE) and Toll-like receptor 4 (TLR4). Electroacupuncture (EA) at Baihui (GV20) and Zusanli (ST36) is beneficial for neurological diseases and experimental sepsis. Our study used EA to treat SAE induced by lipopolysaccharide (LPS) in male Sprague-Dawley rats. The Y maze test was performed to assess working memory. Immunofluorescence (IF) and Western blotting (WB) were used to determine neuroinflammation and the HMGB1 signaling pathway. Results showed that EA could improve working memory impairment in rats with SAE. EA alleviated neuroinflammation by downregulating the hippocampus's HMGB1/TLR4 and HMGB1/RAGE signaling, reducing the levels of pro-inflammatory factors, and relieving microglial and astrocyte activation. However, EA did not affect the tight junctions' expression of the blood-brain barrier (BBB) in the hippocampus.

Forebrain Glucocorticoid Receptor Overexpression Alters Behavioral Encoding of Hippocampal CA1 Pyramidal Cells in Mice

Glucocorticoid signaling influences hippocampal-dependent behavior and vulnerability to stress-related neuropsychiatric disorders. In mice, lifelong overexpression of glucocorticoid receptor (GR) in forebrain excitatory neurons altered exploratory behavior, cognition, and dorsal hippocampal gene expression in adulthood, but whether GR overexpression alters the information encoded by hippocampal neurons is not known. We performed in vivo microendoscopic calcium imaging of 1359 dorsal CA1 pyramidal cells in freely behaving male and female wild-type (WT) and GR-overexpressing (GRov) mice during exploration of a novel open field, where most CA1 neurons are expected to respond to center location and mobility. Most neurons showed sensitivity to center location and/or mobility based on single-neuron calcium amplitude and event rate, but these sensitivity patterns differed between genotypes. GRov neurons were more likely than WT neurons to display center sensitivity and less likely to display mobility sensitivity. More than one-third of these responsive GRov neurons were sensitive only to center location and not mobility, while uniquely center-sensitive neurons were rare in WT. Most center-sensitive neurons exhibited anticipatory activity, suggesting they could drive behavior. We conclude that, compared with wild-type, dorsal CA1 pyramidal cells in GRov mice preferentially respond to center location rather than mobility in a novel open field. Such changes in the information encoded by individual hippocampal neurons in an aversive environment could underlie changes in stress vulnerability because of genetic or epigenetic variations in glucocorticoid receptor signaling.

The implications of categorical and category-free mixed selectivity on representational geometries

The firing rates of individual neurons displaying mixed selectivity are modulated by multiple task variables. When mixed selectivity is nonlinear, it confers an advantage by generating a high-dimensional neural representation that can be flexibly decoded by linear classifiers. Although the advantages of this coding scheme are well accepted, the means of designing an experiment and analyzing the data to test for and characterize mixed selectivity remain unclear. With the growing number of large datasets collected during complex tasks, the mixed selectivity is increasingly observed and is challenging to interpret correctly. We review recent approaches for analyzing and interpreting neural datasets and clarify the theoretical implications of mixed selectivity in the variety of forms that have been reported in the literature. We also aim to provide a practical guide for determining whether a neural population has linear or nonlinear mixed selectivity and whether this mixing leads to a categorical or category-free representation.

Dose-dependent suppression of hippocampal contextual memory formation, place cells, and spatial engrams by the NMDAR antagonist (R)-CPP

We recently reported that the competitive NMDAR antagonist (R,S)-3-(2-carboxypiperazin-4-yl)-propyl-1-phosphonic acid (CPP) does not suppress NMDAR-mediated field EPSPs (fEPSPNMDA) or long-term potentiation (LTP) in vitro at concentrations that block contextual conditioning in vivo. Here we tested one possible explanation for the mismatch - that the hippocampus is relatively resistant to CPP compared to other brain structures engaged in contextual fear conditioning. Using the context pre-exposure facilitation effect (CPFE) paradigm to separate the hippocampal and extra-hippocampal components of contextual learning, we found that the active enantiomer (R)-CPP suppressed the hippocampal component with an IC50 of 3.1 mg/kg, a dose that produces brain concentrations below those required to block fEPSPNMDA or LTP. Moreover, using in-vivo calcium imaging of place cells and spatial engrams to directly assess hippocampal spatial coding, we found that (R)-CPP dose-dependently reduced the development of place cells and interfered with the formation of stable spatial engrams when it was administered prior to exposing mice to a novel context. Both effects occurred at doses that interfered with freezing to context in CPFE experiments. We conclude that (R)-CPP blocks memory formation by interfering with hippocampal function, but that it does so by modulating NMDARs at sites that are not engaged in vitro in the same manner that they are in vivo - perhaps through interneuron circuits that do not contribute to fEPSPs and are not required to elicit LTP using standard induction protocols in vitro, but are essential for successful mnemonic function in vivo.

From synapses to circuits and back: Bridging levels of understanding in animal models of Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by behavioural changes that include memory loss and cognitive decline and is associated with the appearance of amyloid-β plaques and neurofibrillary tangles throughout the brain. Although aspects of the disease percolate across multiple levels of neuronal organization, from the cellular to the behavioural, it is increasingly clear that circuits are a critical junction between the cellular pathology and the behavioural phenotypes that bookend these levels of analyses. In this review, we discuss critical aspects of neural circuit research, beginning with synapses and progressing to network activity and how they influence our understanding of disease processed in AD.

A neuronal prospect theory model in the brain reward circuitry

Prospect theory, arguably the most prominent theory of choice, is an obvious candidate for neural valuation models. How the activity of individual neurons, a possible computational unit, obeys prospect theory remains unknown. Here, we show, with theoretical accuracy equivalent to that of human neuroimaging studies, that single-neuron activity in four core reward-related cortical and subcortical regions represents the subjective valuation of risky gambles in monkeys. The activity of individual neurons in monkeys passively viewing a lottery reflects the desirability of probabilistic rewards parameterized as a multiplicative combination of utility and probability weighting functions, as in the prospect theory framework. The diverse patterns of valuation signals were not localized but distributed throughout most parts of the reward circuitry. A network model aggregating these signals reconstructed the risk preferences and subjective probability weighting revealed by the animals’ choices. Thus, distributed neural coding explains the computation of subjective valuations under risk.

It is unclear how the activity of individual neurons conform to prospect theory. Here, the authors demonstrate that the activity of single neurons in various reward-related regions in the monkey brain can be described as encoding a multiplicative combination of utility and probability weighting, and that this subjective valuation process is achieved via a distributed coding scheme.

Multiple types of navigational information are independently encoded in the population activities of the dentate gyrus neurons

In this study, we found that multiple types of information (position, speed, and motion direction in an open field and current and future location in a T-maze) are independently encoded in the overlapping, but different, populations of dentate gyrus (DG) neurons. This computational nature of the independent distribution of information in neural circuits is newly found not only in the DG, but also in other hippocampal regions.

The dentate gyrus (DG) plays critical roles in cognitive functions, such as learning, memory, and spatial coding, and its dysfunction is implicated in various neuropsychiatric disorders. However, it remains largely unknown how information is represented in this region. Here, we recorded neuronal activity in the DG using Ca²⁺ imaging in freely moving mice and analyzed this activity using machine learning. The activity patterns of populations of DG neurons enabled us to successfully decode position, speed, and motion direction in an open field, as well as current and future location in a T-maze, and each individual neuron was diversely and independently tuned to these multiple information types. Our data also showed that each type of information is unevenly distributed in groups of DG neurons, and different types of information are independently encoded in overlapping, but different, populations of neurons. In alpha-calcium/calmodulin-dependent kinase II (αCaMKII) heterozygous knockout mice, which present deficits in spatial remote and working memory, the decoding accuracy of position in the open field and future location in the T-maze were selectively reduced. These results suggest that multiple types of information are independently distributed in DG neurons.

Noise correlations in neural ensemble activity limit the accuracy of hippocampal spatial representations

Neurons in the CA1 area of the mouse hippocampus encode the position of the animal in an environment. However, given the variability in individual neurons responses, the accuracy of this code is still poorly understood. It was proposed that downstream areas could achieve high spatial accuracy by integrating the activity of thousands of neurons, but theoretical studies point to shared fluctuations in the firing rate as a potential limitation. Using high-throughput calcium imaging in freely moving mice, we demonstrated the limiting factors in the accuracy of the CA1 spatial code. We found that noise correlations in the hippocampus bound the estimation error of spatial coding to ~10 cm (the size of a mouse). Maximal accuracy was obtained using approximately [300-1400] neurons, depending on the animal. These findings reveal intrinsic limits in the brain's representations of space and suggest that single neurons downstream of the hippocampus can extract maximal spatial information from several hundred inputs.

Long-term transverse imaging of the hippocampus with glass microperiscopes

The hippocampus consists of a stereotyped neuronal circuit repeated along the septal-temporal axis. This transverse circuit contains distinct subfields with stereotyped connectivity that support crucial cognitive processes, including episodic and spatial memory. However, comprehensive measurements across the transverse hippocampal circuit in vivo are intractable with existing techniques. Here, we developed an approach for two-photon imaging of the transverse hippocampal plane in awake mice via implanted glass microperiscopes, allowing optical access to the major hippocampal subfields and to the dendritic arbor of pyramidal neurons. Using this approach, we tracked dendritic morphological dynamics on CA1 apical dendrites and characterized spine turnover. We then used calcium imaging to quantify the prevalence of place and speed cells across subfields. Finally, we measured the anatomical distribution of spatial information, finding a non-uniform distribution of spatial selectivity along the DG-to-CA1 axis. This approach extends the existing toolbox for structural and functional measurements of hippocampal circuitry.

Adolescent thalamic inhibition leads to long-lasting impairments in prefrontal cortex function

Impaired cortical maturation is a postulated mechanism in the etiology of neurodevelopmental disorders, including schizophrenia. In the sensory cortex, activity relayed by the thalamus during a postnatal sensitive period is essential for proper cortical maturation. Whether thalamic activity also shapes prefrontal cortical maturation is unknown. We show that inhibiting the mediodorsal and midline thalamus in mice during adolescence leads to a long-lasting decrease in thalamo-prefrontal projection density and reduced excitatory drive to prefrontal neurons. It also caused prefrontal-dependent cognitive deficits during adulthood associated with disrupted prefrontal cross-correlations and task outcome encoding. Thalamic inhibition during adulthood had no long-lasting consequences. Exciting the thalamus in adulthood during a cognitive task rescued prefrontal cross-correlations, task outcome encoding and cognitive deficits. These data point to adolescence as a sensitive window of thalamocortical circuit maturation. Furthermore, by supporting prefrontal network activity, boosting thalamic activity provides a potential therapeutic strategy for rescuing cognitive deficits in neurodevelopmental disorders.

In Vivo Multi-Day Calcium Imaging of CA1 Hippocampus in Freely Moving Rats Reveals a High Preponderance of Place Cells with Consistent Place Fields

Calcium imaging using GCaMP indicators and miniature microscopes has been used to image cellular populations during long timescales and in different task phases, as well as to determine neuronal circuit topology and organization. Because the hippocampus (HPC) is essential for tasks of memory, spatial navigation, and learning, calcium imaging of large populations of HPC neurons can provide new insight on cell changes over time during these tasks. All reported HPC in vivo calcium imaging experiments have been done in mouse. However, rats have many behavioral and physiological experimental advantages over mice. In this paper, we present the first (to our knowledge) in vivo calcium imaging from CA1 HPC in freely moving male rats. Using the UCLA Miniscope, we demonstrate that, in rat, hundreds of cells can be visualized and held across weeks. We show that calcium events in these cells are highly correlated with periods of movement, with few calcium events occurring during periods without movement. We additionally show that an extremely large percent of cells recorded during a navigational task are place cells (77.3 ± 5.0%, surpassing the percent seen during mouse calcium imaging), and that these cells enable accurate decoding of animal position and can be held over days with consistent place fields in a consistent spatial map. A detailed protocol is included, and implications of these advancements on in vivo imaging and place field literature are discussed.SIGNIFICANCE STATEMENT In vivo calcium imaging in freely moving animals allows the visualization of cellular activity across days. In this paper, we present the first in vivo Ca2+ recording from CA1 hippocampus (HPC) in freely moving rats. We demonstrate that hundreds of cells can be visualized and held across weeks, and that calcium activity corresponds to periods of movement. We show that a high percentage (77.3 ± 5.0%) of imaged cells are place cells, and that these place cells enable accurate decoding and can be held stably over days with little change in field location. Because the HPC is essential for many tasks involving memory, navigation, and learning, imaging of large populations of HPC neurons can shed new insight on cellular activity changes and organization.

Spatial maps and oscillations in the healthy hippocampus of Octodon degus, a natural model of sporadic Alzheimer's disease

The Octodon degus is a South American rodent that is receiving increased attention as a potential model of aging and sporadic late-onset Alzheimer’s disease (AD). Impairments in spatial memory tasks in Octodon degus have been reported in relation to either advanced AD-like disease or hippocampal lesion, opening the way to investigate how the function of hippocampal networks affects behavior across AD stages. However, no characterization of hippocampal electrophysiology exists in this species. Here we describe in young, healthy specimens the activity of neurons and local field potential rhythms during spatial navigation tasks with and without objects. Our findings show similarities between the Octodon degus and laboratory rodents. First, place cells with characteristics similar to those found in rats and mice exist in the CA1 subfield of the Octodon degus. Second, the introduction of objects elicits novelty-related exploration and an increase in activity of CA1 cells, with location specific and unspecific components. Third, oscillations of the local field potential are organized according to their spectral content into bands similar to those found in laboratory rodents. These results suggest a common framework of underlying mechanisms, opening the way to future studies of hippocampal dysfunction in this species associated to aging and disease.

Top-down control of hippocampal signal-to-noise by prefrontal long-range inhibition

Prefrontal cortex (PFC) is postulated to exert "top-down control" on information processing throughout the brain to promote specific behaviors. However, pathways mediating top-down control remain poorly understood. In particular, knowledge about direct prefrontal connections that might facilitate top-down control of hippocampal information processing remains sparse. Here we describe monosynaptic long-range GABAergic projections from PFC to hippocampus. These preferentially inhibit vasoactive intestinal polypeptide-expressing interneurons, which are known to disinhibit hippocampal microcircuits. Indeed, stimulating prefrontal-hippocampal GABAergic projections increases hippocampal feedforward inhibition and reduces hippocampal activity in vivo. The net effect of these actions is to specifically enhance the signal-to-noise ratio for hippocampal encoding of object locations and augment object-induced increases in spatial information. Correspondingly, activating or inhibiting these projections promotes or suppresses object exploration, respectively. Together, these results elucidate a top-down prefrontal pathway in which long-range GABAergic projections target disinhibitory microcircuits, thereby enhancing signals and network dynamics underlying exploratory behavior.

Higher-dimensional neural representations predict better episodic memory

Episodic memory enables humans to encode and later vividly retrieve information about our rich experiences, yet the neural representations that support this mental capacity are poorly understood. Using a large fMRI dataset (n = 468) of face-name associative memory tasks and principal component analysis to examine neural representational dimensionality (RD), we found that the human brain maintained a high-dimensional representation of faces through hierarchical representation within and beyond the face-selective regions. Critically, greater RD was associated with better subsequent memory performance both within and across participants, and this association was specific to episodic memory but not general cognitive abilities. Furthermore, the frontoparietal activities could suppress the shared low-dimensional fluctuations and reduce the correlations of local neural responses, resulting in greater RD. RD was not associated with the degree of item-specific pattern similarity, and it made complementary contributions to episodic memory. These results provide a mechanistic understanding of the role of RD in supporting accurate episodic memory.

Social reactivation of fear engrams enhances memory recall

Social interactions can bolster and protect memory performance. However, the relationship between social stimuli and individually learned memories remains enigmatic. Our work reveals that exposure to a stressed, naïve nonfamiliar conspecific or to the ambient olfactory–auditory cues of a recently stressed familiar conspecific induces reactivation of the cellular ensembles associated with a fear memory in the hippocampus. Artificially stimulating the hippocampal ensemble active during the social experience induces fearful behaviors in animals that have previously acquired a negative memory, revealing the interaction between individual history and social experience. The neural resurgence of fear-driving ensembles during social experiences leads to a context-specific enhancement of fear recall. Our findings provide evidence that unlike direct stressors, social stimuli reactivate and amplify an individual’s memories.

For group-living animals, the social environment provides salient experience that can weaken or strengthen aspects of cognition such as memory recall. Although the cellular substrates of individually acquired fear memories in the dentate gyrus (DG) and basolateral amygdala (BLA) have been well-studied and recent work has revealed circuit mechanisms underlying the encoding of social experience, the processes by which social experience interacts with an individual’s memories to alter recall remain unknown. Here we show that stressful social experiences enhance the recall of previously acquired fear memories in male but not female mice, and that social buffering of conspecifics’ distress blocks this enhancement. Activity-dependent tagging of cells in the DG during fear learning revealed that these ensembles were endogenously reactivated during the social experiences in males, even after extinction. These reactivated cells were shown to be functional components of engrams, as optogenetic stimulation of the cells active during the social experience in previously fear-conditioned and not naïve animals was sufficient to drive fear-related behaviors. Taken together, our findings suggest that social experiences can reactivate preexisting engrams to thereby strengthen discrete memories.