Geometry of abstract learned knowledge in the hippocampus

Inferring neural population codes for Drosophila acoustic communication

Social communication between animals is often mediated by sequences of acoustic signals, sometimes spanning long timescales. How auditory neural circuits respond to extended input sequences to guide behavior is not understood. We address this problem using Drosophila acoustic communication, a behavior involving the male's production of and female's response to long, highly variable courtship songs. Here we ask whether female neural and behavioral responses to song are better described by a linear-nonlinear feature detection model vs. a nonlinear accumulation model. Comparing both models against head-fixed neural recordings and pure-behavioral recordings of unrestrained courtship, we found that while both models could explain the neural data, the accumulation model better predicted female locomotion during courtship, outperforming several alternative predictors. To understand how the accumulation model encoded song to predict locomotion, we analyzed the relationship between neural activity simulated by the model and female locomotion during courtship-this revealed the model's reliance on heterogeneous nonlinear adaptation and slow integration. Finally, we asked how adaptation and integration processes could cooperate across the model neural population to encode temporal patterns in song. Simulations revealed how adaptation can transform song inputs prior to integration, allowing fine-scale song information to be retained in the population code for long periods. Thus, modeling fly auditory responses as a nonlinearly adaptive, accumulating population code accounts for female locomotor responses to song during courtship and suggests a biologically plausible mechanism for the online encoding of extended communication sequences.

The estrous cycle modulates hippocampal spine dynamics, dendritic processing, and spatial coding

Histological evidence suggests that the estrous cycle exerts a powerful influence on CA1 neurons in the mammalian hippocampus. Decades have passed since this landmark observation, yet how the estrous cycle shapes dendritic spine dynamics and hippocampal spatial coding in vivo remains a mystery. Here, we used a custom hippocampal microperiscope and two-photon calcium imaging to track CA1 pyramidal neurons in female mice across multiple cycles. Estrous cycle stage had a potent effect on spine dynamics, with spine density peaking during proestrus when estradiol levels are highest. These morphological changes coincided with greater somatodendritic coupling and increased infiltration of back-propagating action potentials into the apical dendrite. Finally, tracking CA1 response properties during navigation revealed greater place field stability during proestrus, evident at both the single-cell and population levels. These findings demonstrate that the estrous cycle drives large-scale structural and functional plasticity in hippocampal neurons essential for learning and memory.

Thinking as Analogy-Making: Toward a Neural Process Account of General Intelligence

What is the secret of human intelligence? A key discovery in psychology is that performance correlations across diverse cognitive tasks are explained by a few broad abilities and one overarching general factor, which is also predictive of real-life achievements. Whether these factors correspond to biological processes is a century-old debate. While previous research focused on localizing their correlates in brain structure, connectivity, and activation levels, the mechanisms of neural information processing related to intelligence are still unexplored. I outline a new approach integrating psychometrics with neuroscientific advances in identifying the computations underlying single tasks from their representational geometry to provide a novel perspective on this topic. In particular, I propose a neural process account of the general factor that builds on the central role of structure mapping-the process of abstracting and reasoning based on relational knowledge-in human cognition. Neural coding properties in the hippocampal and prefrontal-parietal systems that enable inferential leaps through structural abstraction might contribute to the general factor. In general, integrating neuro-representational and psychometric research has the potential to uncover core principles of natural intelligence.

Predictive modeling of hemodynamics during viscerosensory neurostimulation via neural computation mechanism in the brainstem

Neurostimulation for cardiovascular control faces challenges due to the lack of predictive modeling for stimulus-driven dynamic responses, which is crucial for precise neuromodulation via quality feedback. We address this by employing a digital twin approach that leverages computational mechanisms underlying neuro-hemodynamic responses during neurostimulation. Our results emphasize the computational role of the nucleus tractus solitarius (NTS) in the brainstem in determining these responses. The intrinsic neural circuit within the NTS harbors collective dynamics residing in a low-dimensional latent space, which effectively captures stimulus-driven hemodynamic perturbations. Building on this, we developed a digital twin framework for individually optimized predictive modeling of neuromodulatory outcomes. This framework potentially enables the design of closed-loop neurostimulation systems for precise hemodynamic control. Consequently, our digital twin based on neural computation mechanisms marks an advancement in the artificial regulation of internal organs, paving the way for precise translational medicine to treat chronic diseases.

Cell-type-specific manifold analysis discloses independent geometric transformations in the hippocampal spatial code

Integrating analyses of genetically defined cell types with population-level approaches remains poorly explored. We investigated this question by focusing on hippocampal spatial maps and the contribution of two genetically defined pyramidal cell types in the deep and superficial CA1 sublayers. Using single- and dual-color miniscope imaging in mice running along a linear track, we found that population activity from these cells exhibited three-dimensional ring manifolds that encoded the animal position and running direction. Despite shared topology, sublayer-specific manifolds displayed distinct geometric features. Manipulating track orientation revealed rotational and translational changes in manifolds from deep cells, contrasting with more stable representations by superficial cells. These transformations were not observed in manifolds derived from the entire CA1 population. Instead, cell-type-specific chemogenetic silencing of either sublayer revealed independent geometric codes. Our results show how genetically specified subpopulations may underpin parallel spatial maps that can be manipulated independently.

Learning produces an orthogonalized state machine in the hippocampus

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal and abstract relationships that can be used to shape thought, planning and behaviour. Cognitive maps have been observed in the hippocampus1, but their algorithmic form and learning mechanisms remain obscure. Here we used large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different linear tracks in virtual reality. Throughout learning, both animal behaviour and hippocampal neural activity progressed through multiple stages, gradually revealing improved task representation that mirrored improved behavioural efficiency. The learning process involved progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. This decorrelation process was driven by individual neurons acquiring task-state-specific responses (that is, 'state cells'). Although various standard artificial neural networks did not naturally capture these dynamics, the clone-structured causal graph, a hidden Markov model variant, uniquely reproduced both the final orthogonalized states and the learning trajectory seen in animals. The observed cellular and population dynamics constrain the mechanisms underlying cognitive map formation in the hippocampus, pointing to hidden state inference as a fundamental computational principle, with implications for both biological and artificial intelligence.

Brain-Inspired Learning, Perception, and Cognition: A Comprehensive Review

The progress of brain cognition and learning mechanisms has provided new inspiration for the next generation of artificial intelligence (AI) and provided the biological basis for the establishment of new models and methods. Brain science can effectively improve the intelligence of existing models and systems. Compared with other reviews, this article provides a comprehensive review of brain-inspired deep learning algorithms for learning, perception, and cognition from microscopic, mesoscopic, macroscopic, and super-macroscopic perspectives. First, this article introduces the brain cognition mechanism. Then, it summarizes the existing studies on brain-inspired learning and modeling from the perspectives of neural structure, cognitive module, learning mechanism, and behavioral characteristics. Next, this article introduces the potential learning directions of brain-inspired learning from four aspects: perception, cognition, understanding, and decision-making. Finally, the top-ten open problems that brain-inspired learning, perception, and cognition currently face are summarized, and the next generation of AI technology has been prospected. This work intends to provide a quick overview of the research on brain-inspired AI algorithms and to motivate future research by illuminating the latest developments in brain science.

Representational learning by optimization of neural manifolds in an olfactory memory network

Cognitive brain functions rely on experience-dependent internal representations of relevant information. Such representations are organized by attractor dynamics or other mechanisms that constrain population activity onto "neural manifolds". Quantitative analyses of representational manifolds are complicated by their potentially complex geometry, particularly in the absence of attractor states. Here we trained juvenile and adult zebrafish in an odor discrimination task and measured neuronal population activity to analyze representations of behaviorally relevant odors in telencephalic area pDp, the homolog of piriform cortex. No obvious signatures of attractor dynamics were detected. However, olfactory discrimination training selectively enhanced the separation of neural manifolds representing task-relevant odors from other representations, consistent with predictions of autoassociative network models endowed with precise synaptic balance. Analytical approaches using the framework of manifold capacity revealed multiple geometrical modifications of representational manifolds that supported the classification of task-relevant sensory information. Manifold capacity predicted odor discrimination across individuals better than other descriptors of population activity, indicating a close link between manifold geometry and behavior. Hence, pDp and possibly related recurrent networks store information in the geometry of representational manifolds, resulting in joint sensory and semantic maps that may support distributed learning processes.

Flexible hippocampal representation of abstract boundaries supports memory-guided choice

Hippocampal cognitive maps encode the relative locations of spatial cues in an environment and adapt their representation when boundaries geometrically change. Hippocampal cognitive maps can represent abstract knowledge, yet it's unclear whether the hippocampus is sensitive to changes to the extreme coordinates, boundaries, of abstract spaces. We create a memory-guided choice task to test whether the human hippocampus and medial prefrontal cortex (mPFC) flexibly learn abstract boundary representations in distinct two-dimensional (2D) knowledge spaces. Participants build up a 2D map-like representation of abstract boundaries, where the hippocampus and mPFC represent a decision cue's Euclidean distance to the closest boundary. Notably, mPFC distance representations selectively reflect individual performance improvements during the task. Testing for neural sensitivity to boundary-defined contextual changes, only the hippocampus flexibly represents abstract boundaries, which relates to choice behavior. These findings suggest that abstract knowledge retrieval within dynamically changing contexts is facilitated by generalized mPFC and flexible hippocampal boundary representations.

Complexity in speech and music listening via neural manifold flows

Understanding the complex neural mechanisms underlying speech and music perception remains a multifaceted challenge. In this study, we investigated neural dynamics using human intracranial recordings. Employing a novel approach based on low-dimensional reduction techniques, the Manifold Density Flow (MDF), we quantified the complexity of brain dynamics during naturalistic speech and music listening and during resting state. Our results reveal higher complexity in patterns of interdependence between different brain regions during speech and music listening compared with rest, suggesting that the cognitive demands of speech and music listening drive the brain dynamics toward states not observed during rest. Moreover, speech listening has more complexity than music, highlighting the nuanced differences in cognitive demands between these two auditory domains. Additionally, we validated the efficacy of the MDF method through experimentation on a toy model and compared its effectiveness in capturing the complexity of brain dynamics induced by cognitive tasks with another established technique in the literature. Overall, our findings provide a new method to quantify the complexity of brain activity by studying its temporal evolution on a low-dimensional manifold, suggesting insights that are invisible to traditional methodologies in the contexts of speech and music perception.

Self-supervised learning of scale-invariant neural representations of space and time

Hippocampal representations of space and time seem to share a common coding scheme characterized by neurons with bell-shaped tuning curves called place and time cells. The properties of the tuning curves are consistent with Weber's law, such that, in the absence of visual inputs, width scales with the peak time for time cells and with distance for place cells. Building on earlier computational work, we examined how neurons with such properties can emerge through self-supervised learning. We found that a network based on autoencoders can, given a particular inputs and connectivity constraints, produce scale-invariant time cells. When the animal's velocity modulates the decay rate of the leaky integrators, the same network gives rise to scale-invariant place cells. Importantly, this is not the case when velocity is fed as a direct input to the leaky integrators, implying that weight modulation by velocity might be critical for developing scale-invariant spatial receptive fields. Finally, we demonstrated that after training, scale-invariant place cells emerge in environments larger than those used during training. Taken together, these findings bring us closer to understanding the emergence of neurons with bell-shaped tuning curves in the hippocampus and highlight the critical role of velocity modulation in the formation of scale-invariant place cells.

Hippocampal neuronal activity is aligned with action plans

Neurons in the hippocampus are correlated with different variables, including space, time, sensory cues, rewards and actions, in which the extent of tuning depends on ongoing task demands1-8. However, it remains uncertain whether such diverse tuning corresponds to distinct functions within the hippocampal network or whether a more generic computation can account for these observations9. Here, to disentangle the contribution of externally driven cues versus internal computation, we developed a task in mice in which space, auditory tones, rewards and context were juxtaposed with changing relevance. High-density electrophysiological recordings revealed that neurons were tuned to each of these modalities. By comparing movement paths and action sequences, we observed that external variables had limited direct influence on hippocampal firing. Instead, spiking was influenced by online action plans and modulated by goal uncertainty. Our results suggest that internally generated cell assembly sequences are selected and updated by action plans towards deliberate goals. The apparent tuning of hippocampal neuronal spiking to different sensory modalities might emerge due to alignment to the afforded action progression within a task rather than representation of external cues.

Perceptual and semantic maps in individual humans share structural features that predict creative abilities

Building perceptual and associative links between internal representations is a fundamental neural process, allowing individuals to structure their knowledge about the world and combine it to enable efficient and creative behavior. In this context, the representational similarity between pairs of represented entities is thought to reflect their associative linkage at different levels of sensory processing, ranging from lower-order perceptual levels up to higher-order semantic levels. While recently specific structural features of semantic representational maps were linked with creative abilities of individual humans, it remains unclear if these features are also shared on lower level, perceptual maps. Here, we address this question by presenting 148 human participants with psychophysical scaling tasks, using two sets of independent and qualitatively distinct stimuli, to probe representational map structures in the lower-order auditory and the higher-order semantic domain. We quantify individual representational features with graph-theoretical measures and demonstrate a robust correlation of representational structures in the perceptual auditory and semantic modality. We delineate these shared representational features to predict multiple verbal standard measures of creativity, observing that both, semantic and auditory features, reflect creative abilities. Our findings indicate that the general, modality-overarching representational geometry of an individual is a relevant underpinning of creative thought.

A non-Hebbian code for episodic memory

Hebbian plasticity has long dominated neurobiological models of memory formation. Yet, plasticity rules operating on one-shot episodic memory timescales rarely depend on both pre- and postsynaptic spiking, challenging Hebbian theory in this crucial regime. Here, we present an episodic memory model governed by a simpler rule depending only on presynaptic activity. We show that this rule, capitalizing on high-dimensional neural activity with restricted transitions, naturally stores episodes as paths through complex state spaces like those underlying a world model. The resulting memory traces, which we term path vectors, are highly expressive and decodable with an odor-tracking algorithm. We show that path vectors are robust alternatives to Hebbian traces, support one-shot sequential and associative recall, along with policy learning, and shed light on specific hippocampal plasticity rules. Thus, non-Hebbian plasticity is sufficient for flexible memory and learning and well-suited to encode episodes and policies as paths through a world model.

Exploring replay

Animals face uncertainty about their environments due to initial ignorance or subsequent changes. They therefore need to explore. However, the algorithmic structure of exploratory choices in the brain still remains largely elusive. Artificial agents face the same problem, and a venerable idea in reinforcement learning is that they can plan appropriate exploratory choices offline, during the equivalent of quiet wakefulness or sleep. Although offline processing in humans and other animals, in the form of hippocampal replay and preplay, has recently been the subject of highly informative modelling, existing methods only apply to known environments. Thus, they cannot predict exploratory replay choices during learning and/or behaviour in the face of uncertainty. Here, we extend an influential theory of hippocampal replay and examine its potential role in approximately optimal exploration, deriving testable predictions for the patterns of exploratory replay choices in a paradigmatic spatial navigation task. Our modelling provides a normative interpretation of the available experimental data suggestive of exploratory replay. Furthermore, we highlight the importance of sequence replay, and license a range of new experimental paradigms that should further our understanding of offline processing.

TWINKLE: An open-source two-photon microscope for teaching and research

Many laboratories use two-photon microscopy through commercial suppliers, or homemade designs of considerable complexity. The integrated nature of these systems complicates customization, troubleshooting, and training on the principles of two-photon microscopy. Here, we present "Twinkle": a microscope for Two-photon Imaging in Neuroscience, and Kit for Learning and Education. It is a fully open, high performing and easy-to-set-up microscope that can effectively be used for both education and research. The instrument features a >1 mm field of view, using a modern objective with 3 mm working distance and 2 inch diameter optics combined with GaAsP photomultiplier tubes to maximize the fluorescence signal. We document our experiences using this system as a teaching tool in several two week long workshops, exemplify scientific use cases, and conclude with a broader note on the place of our work in the growing space of open scientific instrumentation.

Exploring the role of dimensionality transformation in episodic memory

Episodic memory must accomplish two adversarial goals: encoding and storing a multitude of experiences without exceeding the finite neuronal structure of the brain, and recalling memories in vivid detail. Dimensionality reduction and expansion ('dimensionality transformation') enable the brain to meet these demands. Reduction compresses sensory input into simplified, storable codes, while expansion reconstructs vivid details. Although these processes are essential to memory, their neural mechanisms for episodic memory remain unclear. Drawing on recent insights from cognitive psychology, systems neuroscience, and neuroanatomy, we propose two accounts of how dimensionality transformation occurs in the brain: structurally (via corticohippocampal pathways) and functionally (through neural oscillations). By examining cross-species evidence, we highlight neural mechanisms that may support episodic memory and identify crucial questions for future research.

Atypical hippocampal excitatory neurons express and govern object memory

Classically, pyramidal cells of the hippocampus are viewed as flexibly representing spatial and non-spatial information. Recent work has illustrated distinct types of hippocampal excitatory neurons, suggesting that hippocampal representations and functions may be constrained and interpreted by these underlying cell-type identities. In mice, here we reveal a non-pyramidal excitatory neuron type - the "ovoid" neuron - that is spatially adjacent to subiculum pyramidal cells but differs in gene expression, electrophysiology, morphology, and connectivity. Functionally, novel object encounters drive sustained ovoid neuron activity, whereas familiar objects fail to drive activity even months after single-trial learning. Silencing ovoid neurons prevents non-spatial object learning but leaves spatial learning intact, and activating ovoid neurons toggles novel-object seeking to familiar-object seeking. Such function is doubly dissociable from pyramidal neurons, wherein manipulation of pyramidal cells affects spatial assays but not non-spatial learning. Ovoid neurons of the subiculum thus illustrate selective cell-type-specific control of non-spatial memory and behavioral preference.

Diabetes exerts a causal impact on the nervous system within the right hippocampus: substantiated by genetic data

INTRODUCTION

Diabetes and neuronal loss in the hippocampus have been observed to be correlated in several studies; however, the exact causality of this association remains uncertain. This study aims to explore the potential causal relationship between diabetes and the hippocampal nervous system.

METHODS

We utilized the two-sample Mendelian randomization (MR) analysis to investigate the potential causal connection between diabetes and the hippocampal nervous system. The summary statistics of Genome-wide association study (GWAS) for diabetes and hippocampus neuroimaging measurement were acquired from published GWASs, all of which were based on European ancestry. Several two-sample MR analyses were conducted in this study, utilizing inverse-variance weighted (IVW), MR Egger, and Weight-median methods. To ensure the reliability of the results and identify any horizontal pleiotropy, sensitivity analyses were undertaken using Cochran's Q test and the MR-PRESSO global test.

RESULTS

Causal associations were found between diabetes and the nervous system in the hippocampus. Type 1 and type 2 diabetes were both identified as having adverse causal connections with the right hippocampal nervous system. This was supported by specific ranges of IVW-OR values (P < 0.05). The consistency of the sensitivity analyses further reinforced the main findings, revealing no significant heterogeneity or presence of horizontal pleiotropy.

CONCLUSIONS

This study delved into the causal associations between diabetes and the hippocampal nervous system, revealing that both type 1 and type 2 diabetes have detrimental effects on the right hippocampal nervous system. Our findings have significant clinical implications as they indicate that diabetes may play a role in the decline of neurons in the right hippocampus among European populations, often resulting in cognitive decline.

Hippocampal coding of identity, sex, hierarchy, and affiliation in a social group of wild fruit bats

Social animals live in groups and interact volitionally in complex ways. However, little is known about neural responses under such natural conditions. Here, we investigated hippocampal CA1 neurons in a mixed-sex group of five to 10 freely behaving wild Egyptian fruit bats that lived continuously in a laboratory-based cave and formed a stable social network. In-flight, most hippocampal place cells were socially modulated and represented the identity and sex of conspecifics. Upon social interactions, neurons represented specific interaction types. During active observation, neurons encoded the bat's own position and head direction, together with the position, direction, and identity of multiple conspecifics. Identity-coding neurons encoded the same bat across contexts. The strength of identity coding was modulated by sex, hierarchy, and social affiliation. Thus, hippocampal neurons form a multidimensional sociospatial representation of the natural world.

Identifying representational structure in CA1 to benchmark theoretical models of cognitive mapping

Decades of theoretical and empirical work have suggested the hippocampus instantiates some form of a cognitive map. Yet, tests of competing theories have been limited in scope and largely qualitative in nature. Here, we develop a novel framework to benchmark model predictions against observed neuronal population dynamics as animals navigate a series of geometrically distinct environments. In this task space, we show a representational structure in the dynamics of hippocampal remapping that generalizes across brains, discriminates between competing theoretical models, and effectively constrains biologically viable model parameters. With this approach, we find that accurate models capture the correspondence in spatial coding of a changing environment. The present dataset and framework thus serve to empirically evaluate and advance theories of cognitive mapping in the brain.

TWINKLE: An open-source two-photon microscope for teaching and research

Many laboratories use two-photon microscopy through commercial suppliers, or homemade designs of considerable complexity. The integrated nature of these systems complicates customization, troubleshooting, and training on the principles of two-photon microscopy. Here, we present "Twinkle": a microscope for Two-photon Imaging in Neuroscience, and Kit for Learning and Education. It is a fully open, high performing and easy-to-set-up microscope that can effectively be used for both education and research. The instrument features a > 1 mm field of view, using a modern objective with 3 mm working distance and 2 inch diameter optics combined with GaAsP photomultiplier tubes to maximize the fluorescence signal. We document our experiences using this system as a teaching tool in several two week long workshops, exemplify scientific use cases, and conclude with a broader note on the place of our work in the growing space of open scientific instrumentation.

A language model of problem solving in humans and macaque monkeys

Human intelligence is characterized by the remarkable ability to solve complex problems by planning a sequence of actions that takes us from an initial state to a desired goal state. Quantifying and comparing problem-solving capabilities across species and finding their evolutionary roots are critical for understanding how the brain carries out this intricate process. We introduce the Language of Problem Solving (LoPS) model as a novel quantitative framework that investigates the structure of problem-solving behavior through a language model. We applied the model to an adapted classic Pac-Man game as a cross-species behavioral paradigm to test both humans and macaque monkeys. The LoPS model extracted the latent structure, or grammar, embedded in the agents' gameplay, revealing the non-Markovian temporal dependency structure of their problem-solving behavior and the hierarchical structures of problem solving in both species. The complexity of LoPS grammar correlated with individuals' game performance and reflected the difference in problem-solving capacity between humans and monkeys. Both species evolved their LoPS grammars during learning, progressing from simpler to more complex ones, suggesting that the structure of problem solving is not fixed but evolves to support more sophisticated and efficient problem solving. Our study provides insights into how humans and monkeys break down problem solving into compositional units and navigate complex tasks, deepening our understanding of human intelligence and its evolution and establishing a foundation for future investigations of the neural mechanisms of problem solving.

Latent learning drives sleep-dependent plasticity in distinct CA1 subpopulations

Latent learning is a process that enables the brain to transform experiences into "cognitive maps," a form of implicit memory, without requiring reinforced training. To investigate its neural mechanisms, we record from hippocampal neurons in mice during latent learning of spatial maps and observe that the high-dimensional neural state space gradually transforms into a low-dimensional manifold that closely resembles the physical environment. This transformation process is associated with the neural reactivation of navigational experiences during sleep. Additionally, we identify a subset of hippocampal neurons that, rather than forming place fields in a novel environment, maintain weak spatial tuning but gradually develop correlated activity with other neurons. The elevated correlation introduces redundancy into the ensemble code, transforming the neural state space into a low-dimensional manifold that effectively links discrete place fields of place cells into a map-like structure. These results suggest a potential mechanism for latent learning of spatial maps in the hippocampus.

Idiothetic representations are modulated by availability of sensory inputs and task demands in the hippocampal-septal circuit

The hippocampus is a higher-order brain structure responsible for encoding new episodic memories and predicting future outcomes. In the absence of external stimuli, neurons in the hippocampus track elapsed time, distance traveled, and other idiothetic variables. To this day, the exact determinants of idiothetic representations during free navigation remain unclear. Here, we developed unsupervised approaches to extract population and single-cell properties of more than 30,000 CA1 pyramidal neurons in freely moving mice. We find that spatiotemporal representations are composed of a mixture of idiothetic and allocentric information, the balance of which is dictated by task demand and environmental conditions. Additionally, a subset of CA1 pyramidal neurons encodes the spatiotemporal distance to rewards. Finally, distance and time information is integrated postsynaptically in the lateral septum, indicating that these high-level representations are effectively integrated in downstream neurons.

A universal hippocampal memory code across animals and environments

How learning is affected by context is a fundamental question of neuroscience, as the ability to generalize learning to different contexts is necessary for navigating the world. An example of swift contextual generalization is observed in conditioning tasks, where performance is quickly generalized from one context to another. A key question in identifying the neural substrate underlying this ability is how the hippocampus (HPC) represents task-related stimuli across different environments, given that HPC cells exhibit place-specific activity that changes across contexts (remapping). In this study, we used calcium imaging to monitor hippocampal neuron activity as rats performed a conditioning task across multiple spatial contexts. We investigated whether hippocampal cells, which encode both spatial locations (place cells) and task-related information, could maintain their task representation even when their spatial encoding remapped in a new spatial context. To assess the consistency of task representations, we used advanced dimensionality reduction techniques combined with machine learning to develop manifold representations of population level HPC activity. The results showed that task-related neural representations remained stable even as place cell representations of spatial context changed, thus demonstrating similar embedding geometries of neural representations of the task across different spatial contexts. Notably, these patterns were not only consistent within the same animal across different contexts but also significantly similar across different animals, suggesting a standardized neural encoding or 'neural syntax' in the hippocampus. These findings bridge a critical gap between memory and navigation research, revealing how the hippocampus maintains cognitive consistency across different spatial environments. These findings also suggest that hippocampal function is governed by a neural framework shared between animals, an observation that may have broad implications for understanding memory, learning, and related cognitive processes. Looking ahead, this work opens new avenues for exploring the fundamental principles underlying hippocampal encoding strategies.

Sensory History Drives Adaptive Neural Geometry in LP/Pulvinar-Prefrontal Cortex Circuits

Prior expectations guide attention and support perceptual filtering for efficient processing during decision-making. Here we show that during a visual discrimination task, mice adaptively use prior stimulus history to guide ongoing choices by estimating differences in evidence between consecutive trials (| Δ Dir |). The thalamic lateral posterior (LP)/pulvinar nucleus provides robust inputs to the Anterior Cingulate Cortex (ACC), which has been implicated in selective attention and predictive processing, but the function of the LP-ACC projection is unknown. We found that optogenetic manipulations of LP-ACC axons disrupted animals' ability to effectively estimate and use information across stimulus history, leading to | Δ Dir |-dependent ipsilateral biases. Two-photon calcium imaging of LP-ACC axons revealed an engagement-dependent low-dimensional organization of stimuli along a curved manifold. This representation was scaled by | Δ Dir | in a manner that emphasized greater deviations from prior evidence. Thus, our work identifies the LP-ACC pathway as essential for selecting and evaluating stimuli relative to prior evidence to guide decisions.

Analysis methods for large-scale neuronal recordings

Simultaneous recordings from hundreds or thousands of neurons are becoming routine because of innovations in instrumentation, molecular tools, and data processing software. Such recordings can be analyzed with data science methods, but it is not immediately clear what methods to use or how to adapt them for neuroscience applications. We review, categorize, and illustrate diverse analysis methods for neural population recordings and describe how these methods have been used to make progress on longstanding questions in neuroscience. We review a variety of approaches, ranging from the mathematically simple to the complex, from exploratory to hypothesis-driven, and from recently developed to more established methods. We also illustrate some of the common statistical pitfalls in analyzing large-scale neural data.

Temporal dynamics of nucleus accumbens neurons in male mice during reward seeking

The nucleus accumbens (NAc) regulates reward-motivated behavior, but the temporal dynamics of NAc neurons that enable "free-willed" animals to obtain rewards remain elusive. Here, we recorded Ca2+ activity from individual NAc neurons when mice performed self-paced lever-presses for sucrose. NAc neurons exhibited three temporally-sequenced clusters, defined by times at which they exhibited increased Ca2+ activity: approximately 0, -2.5 or -5 sec relative to the lever-pressing. Dopamine D1 receptor (D1)-expressing neurons and D2-neurons formed the majority of the -5-sec versus -2.5-sec clusters, respectively, while both neuronal subtypes were represented in the 0-sec cluster. We found that pre-press activity patterns of D1- or D2-neurons could predict subsequent lever-presses. Inhibiting D1-neurons at -5 sec or D2-neurons at -2.5 sec, but not at other timepoints, reduced sucrose-motivated lever-pressing. We propose that the time-specific activity of D1- and D2-neurons mediate key temporal features of the NAc through which reward motivation initiates reward-seeking behavior.

Latent dynamics of primary sensory cortical population activity structured by fluctuations in the local field potential

Introduction

As emerging technologies enable measurement of precise details of the activity within microcircuits at ever-increasing scales, there is a growing need to identify the salient features and patterns within the neural populations that represent physiologically and behaviorally relevant aspects of the network. Accumulating evidence from recordings of large neural populations suggests that neural population activity frequently exhibits relatively low-dimensional structure, with a small number of variables explaining a substantial fraction of the structure of the activity. While such structure has been observed across the brain, it is not known how reduced-dimension representations of neural population activity relate to classical metrics of "brain state," typically described in terms of fluctuations in the local field potential (LFP), single-cell activity, and behavioral metrics.

Methods

Hidden state models were fit to spontaneous spiking activity of populations of neurons, recorded in the whisker area of primary somatosensory cortex of awake mice. Classic measures of cortical state in S1, including the LFP and whisking activity, were compared to the dynamics of states inferred from spiking activity.

Results

A hidden Markov model fit the population spiking data well with a relatively small number of states, and putative inhibitory neurons played an outsize role in determining the latent state dynamics. Spiking states inferred from the model were more informative of the cortical state than a direct readout of the spiking activity of single neurons or of the population. Further, the spiking states predicted both the trial-by-trial variability in sensory responses and one aspect of behavior, whisking activity.

Discussion

Our results show how classical measurements of brain state relate to neural population spiking dynamics at the scale of the microcircuit and provide an approach for quantitative mapping of brain state dynamics across brain areas.

The representational geometry for naturalistic textures in macaque V1 and V2

Our understanding of visual cortical processing has relied primarily on studying the selectivity of individual neurons in different areas. A complementary approach is to study how the representational geometry of neuronal populations differs across areas. Though the geometry is derived from individual neuronal selectivity, it can reveal encoding strategies difficult to infer from single neuron responses. In addition, recent theoretical work has begun to relate distinct functional objectives to different representational geometries. To understand how the representational geometry changes across stages of processing, we measured neuronal population responses in primary visual cortex (V1) and area V2 of macaque monkeys to an ensemble of synthetic, naturalistic textures. Responses were lower dimensional in V2 than V1, and there was a better alignment of V2 population responses to different textures. The representational geometry in V2 afforded better discriminability between out-of-sample textures. We performed complementary analyses of standard convolutional network models, which did not replicate the representational geometry of cortex. We conclude that there is a shift in the representational geometry between V1 and V2, with the V2 representation exhibiting features of a low-dimensional, systematic encoding of different textures and of different instantiations of each texture. Our results suggest that comparisons of representational geometry can reveal important transformations that occur across successive stages of visual processing.

The topology and geometry of neural representations

A central question for neuroscience is how to characterize brain representations of perceptual and cognitive content. An ideal characterization should distinguish different functional regions with robustness to noise and idiosyncrasies of individual brains that do not correspond to computational differences. Previous studies have characterized brain representations by their representational geometry, which is defined by the representational dissimilarity matrix (RDM), a summary statistic that abstracts from the roles of individual neurons (or responses channels) and characterizes the discriminability of stimuli. Here, we explore a further step of abstraction: from the geometry to the topology of brain representations. We propose topological representational similarity analysis, an extension of representational similarity analysis that uses a family of geotopological summary statistics that generalizes the RDM to characterize the topology while de-emphasizing the geometry. We evaluate this family of statistics in terms of the sensitivity and specificity for model selection using both simulations and functional MRI (fMRI) data. In the simulations, the ground truth is a data-generating layer representation in a neural network model and the models are the same and other layers in different model instances (trained from different random seeds). In fMRI, the ground truth is a visual area and the models are the same and other areas measured in different subjects. Results show that topology-sensitive characterizations of population codes are robust to noise and interindividual variability and maintain excellent sensitivity to the unique representational signatures of different neural network layers and brain regions.

Blind Video Quality Prediction by Uncovering Human Video Perceptual Representation

Blind video quality assessment (VQA) has become an increasingly demanding problem in automatically assessing the quality of ever-growing in-the-wild videos. Although efforts have been made to measure temporal distortions, the core to distinguish between VQA and image quality assessment (IQA), the lack of modeling of how the human visual system (HVS) relates to the temporal quality of videos hinders the precise mapping of predicted temporal scores to the human perception. Inspired by the recent discovery of the temporal straightness law of natural videos in the HVS, this paper intends to model the complex temporal distortions of in-the-wild videos in a simple and uniform representation by describing the geometric properties of videos in the visual perceptual domain. A novel videolet, with perceptual representation embedding of a few consecutive frames, is designed as the basic quality measurement unit to quantify temporal distortions by measuring the angular and linear displacements from the straightness law. By combining the predicted score on each videolet, a perceptually temporal quality evaluator (PTQE) is formed to measure the temporal quality of the entire video. Experimental results demonstrate that the perceptual representation in the HVS is an efficient way of predicting subjective temporal quality. Moreover, when combined with spatial quality metrics, PTQE achieves top performance over popular in-the-wild video datasets. More importantly, PTQE requires no additional information beyond the video being assessed, making it applicable to any dataset without parameter tuning. Additionally, the generalizability of PTQE is evaluated on video frame interpolation tasks, demonstrating its potential to benefit temporal-related enhancement tasks.

Hippocampal neuronal activity is aligned with action plans

Neurons in the hippocampus are correlated with different variables, including space, time, sensory cues, rewards, and actions, where the extent of tuning depends on ongoing task demands. However, it remains uncertain whether such diverse tuning corresponds to distinct functions within the hippocampal network or if a more generic computation can account for these observations. To disentangle the contribution of externally driven cues versus internal computation, we developed a task in mice where space, auditory tones, rewards, and context were juxtaposed with changing relevance. High-density electrophysiological recordings revealed that neurons were tuned to each of these modalities. By comparing movement paths and action sequences, we observed that external variables had limited direct influence on hippocampal firing. Instead, spiking was influenced by online action plans modulated by goal uncertainty. Our results suggest that internally generated cell assembly sequences are selected and updated by action plans toward deliberate goals. The apparent tuning of hippocampal neuronal spiking to different sensory modalities might emerge due to alignment to the afforded action progression within a task rather than representation of external cues.

Transitions in dynamical regime and neural mode underlie perceptual decision-making

Perceptual decision-making is the process by which an animal uses sensory stimuli to choose an action or mental proposition. This process is thought to be mediated by neurons organized as attractor networks 1,2 . However, whether attractor dynamics underlie decision behavior and the complex neuronal responses remains unclear. Here we use an unsupervised, deep learning-based method to discover decision-related dynamics from the simultaneous activity of neurons in frontal cortex and striatum of rats while they accumulate pulsatile auditory evidence. We found that trajectories evolved along two sequential regimes, the first dominated by sensory inputs, and the second dominated by the autonomous dynamics, with flow in a direction (i.e., "neural mode") largely orthogonal to that in the first regime. We propose that the second regime corresponds to decision commitment. We developed a simplified model that approximates the coupled transition in dynamics and neural mode and allows precise inference, from each trial's neural activity, of a putative internal decision commitment time in that trial. The simplified model captures diverse and complex single-neuron temporal profiles, such as ramping and stepping 3-5 . It also captures trial-averaged curved trajectories 6-8 , and reveals distinctions between brain regions. The putative neurally-inferred commitment times ("nTc") occurred at times broadly distributed across trials, and not time-locked to stimulus onset, offset, or response onset. Nevertheless, when trials were aligned to nTc, behavioral analysis showed that, as predicted by a decision commitment time, sensory evidence before nTc affected the subjects' decision, but evidence after nTc did not. Our results show that the formation of a perceptual choice involves a rapid, coordinated transition in both the dynamical regime and the neural mode of the decision process, and suggest the moment of commitment to be a useful entry point for dissecting mechanisms underlying rapid changes in internal state.

Neural Sequences Underlying Directed Turning in C. elegans

Complex behaviors like navigation rely on sequenced motor outputs that combine to generate effective movement. The brain-wide organization of the circuits that integrate sensory signals to select and execute appropriate motor sequences is not well understood. Here, we characterize the architecture of neural circuits that control C. elegans olfactory navigation. We identify error-correcting turns during navigation and use whole-brain calcium imaging and cell-specific perturbations to determine their neural underpinnings. These turns occur as motor sequences accompanied by neural sequences, in which defined neurons activate in a stereotyped order during each turn. Distinct neurons in this sequence respond to sensory cues, anticipate upcoming turn directions, and drive movement, linking key features of this sensorimotor behavior across time. The neuromodulator tyramine coordinates these sequential brain dynamics. Our results illustrate how neuromodulation can act on a defined neural architecture to generate sequential patterns of activity that link sensory cues to motor actions.

Predictive sequence learning in the hippocampal formation

The hippocampus receives sequences of sensory inputs from the cortex during exploration and encodes the sequences with millisecond precision. We developed a predictive autoencoder model of the hippocampus including the trisynaptic and monosynaptic circuits from the entorhinal cortex (EC). CA3 was trained as a self-supervised recurrent neural network to predict its next input. We confirmed that CA3 is predicting ahead by analyzing the spike coupling between simultaneously recorded neurons in the dentate gyrus, CA3, and CA1 of the mouse hippocampus. In the model, CA1 neurons signal prediction errors by comparing CA3 predictions to the next direct EC input. The model exhibits the rapid appearance and slow fading of CA1 place cells and displays replay and phase precession from CA3. The model could be learned in a biologically plausible way with error-encoding neurons. Similarities between the hippocampal and thalamocortical circuits suggest that such computation motif could also underlie self-supervised sequence learning in the cortex.

The estrous cycle modulates hippocampal spine dynamics, dendritic processing, and spatial coding

Histological evidence suggests that the estrous cycle exerts a powerful effect on CA1 neurons in mammalian hippocampus. Decades have passed since this landmark observation, yet how the estrous cycle shapes dendritic spine dynamics and hippocampal spatial coding in vivo remains a mystery. Here, we used a custom hippocampal microperiscope and two-photon calcium imaging to track CA1 pyramidal neurons in female mice over multiple cycles. Estrous cycle stage had a potent effect on spine dynamics, with heightened density during periods of greater estradiol (proestrus). These morphological changes were accompanied by greater somatodendritic coupling and increased infiltration of back-propagating action potentials into the apical dendrite. Finally, tracking CA1 response properties during navigation revealed enhanced place field stability during proestrus, evident at the single-cell and population level. These results establish the estrous cycle as a driver of large-scale structural and functional plasticity in hippocampal circuits essential for learning and memory.

Dentate gyrus ensembles gate context-dependent neural states and memory retrieval

Memories of events are linked to the contexts in which they were encoded. This contextual linking ensures enhanced access to those memories that are most relevant to the context at hand, including specific associations that were previously learned in that context. This principle, referred to as encoding specificity, predicts that context-specific neural states should bias retrieval of particular associations over others, potentially allowing for the disambiguation of retrieval cues that may have multiple associations or meanings. Using a context-odor paired associate learning paradigm in mice, here, we show that chemogenetic manipulation of dentate gyrus ensembles corresponding to specific contexts reinstates context-specific neural states in downstream CA1 and biases retrieval toward context-specific associations.

Learning, Fast and Slow: Single- and Many-Shot Learning in the Hippocampus

The hippocampus is critical for memory and spatial navigation. The ability to map novel environments, as well as more abstract conceptual relationships, is fundamental to the cognitive flexibility that humans and other animals require to survive in a dynamic world. In this review, we survey recent advances in our understanding of how this flexibility is implemented anatomically and functionally by hippocampal circuitry, during both active exploration (online) and rest (offline). We discuss the advantages and limitations of spike timing-dependent plasticity and the more recently discovered behavioral timescale synaptic plasticity in supporting distinct learning modes in the hippocampus. Finally, we suggest complementary roles for these plasticity types in explaining many-shot and single-shot learning in the hippocampus and discuss how these rules could work together to support the learning of cognitive maps.

Abstract representations emerge in human hippocampal neurons during inference

Humans have the remarkable cognitive capacity to rapidly adapt to changing environments. Central to this capacity is the ability to form high-level, abstract representations that take advantage of regularities in the world to support generalization1. However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning and how they relate to behaviour2,3. Here we characterized the representational geometry of populations of neurons (single units) recorded in the hippocampus, amygdala, medial frontal cortex and ventral temporal cortex of neurosurgical patients performing an inferential reasoning task. We found that only the neural representations formed in the hippocampus simultaneously encode several task variables in an abstract, or disentangled, format. This representational geometry is uniquely observed after patients learn to perform inference, and consists of disentangled directly observable and discovered latent task variables. Learning to perform inference by trial and error or through verbal instructions led to the formation of hippocampal representations with similar geometric properties. The observed relation between representational format and inference behaviour suggests that abstract and disentangled representational geometries are important for complex cognition.

Neural representational geometries reflect behavioral differences in monkeys and recurrent neural networks

Animals likely use a variety of strategies to solve laboratory tasks. Traditionally, combined analysis of behavioral and neural recording data across subjects employing different strategies may obscure important signals and give confusing results. Hence, it is essential to develop techniques that can infer strategy at the single-subject level. We analyzed an experiment in which two male monkeys performed a visually cued rule-based task. The analysis of their performance shows no indication that they used a different strategy. However, when we examined the geometry of stimulus representations in the state space of the neural activities recorded in dorsolateral prefrontal cortex, we found striking differences between the two monkeys. Our purely neural results induced us to reanalyze the behavior. The new analysis showed that the differences in representational geometry are associated with differences in the reaction times, revealing behavioral differences we were unaware of. All these analyses suggest that the monkeys are using different strategies. Finally, using recurrent neural network models trained to perform the same task, we show that these strategies correlate with the amount of training, suggesting a possible explanation for the observed neural and behavioral differences.

Shared structure facilitates working memory of multiple sequences

Daily experiences often involve the processing of multiple sequences, yet storing them challenges the limited capacity of working memory (WM). To achieve efficient memory storage, relational structures shared by sequences would be leveraged to reorganize and compress information. Here, participants memorized a sequence of items with different colors and spatial locations and later reproduced the full color and location sequences one after another. Crucially, we manipulated the consistency between location and color sequence trajectories. First, sequences with consistent trajectories demonstrate improved memory performance and a trajectory correlation between reproduced color and location sequences. Second, sequences with consistent trajectories show neural reactivation of common trajectories, and display spontaneous replay of color sequences when recalling locations. Finally, neural reactivation correlates with WM behavior. Our findings suggest that a shared common structure is leveraged for the storage of multiple sequences through compressed encoding and neural replay, together facilitating efficient information organization in WM.

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.

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

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

Neural dynamics underlying minute-timescale persistent behavior in the human brain

The ability to pursue long-term goals relies on a representations of task context that can both be maintained over long periods of time and switched flexibly when goals change. Little is known about the neural substrate for such minute-scale maintenance of task sets. Utilizing recordings in neurosurgical patients, we examined how groups of neurons in the human medial frontal cortex and hippocampus represent task contexts. When cued explicitly, task context was encoded in both brain areas and changed rapidly at task boundaries. Hippocampus exhibited a temporally dynamic code with fast decorrelation over time, preventing cross-temporal generalization. Medial frontal cortex exhibited a static code that decorrelated slowly, allowing generalization across minutes of time. When task context needed to be inferred as a latent variable, hippocampus encoded task context with a static code. These findings reveal two possible regimes for encoding minute-scale task-context representations that were engaged differently based on task demands.

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.

Beta: bursts of cognition

Beta oscillations are linked to the control of goal-directed processing of sensory information and the timing of motor output. Recent evidence demonstrates they are not sustained but organized into intermittent high-power bursts mediating timely functional inhibition. This implies there is a considerable moment-to-moment variation in the neural dynamics supporting cognition. Beta bursts thus offer new opportunities for studying how sensory inputs are selectively processed, reshaped by inhibitory cognitive operations and ultimately result in motor actions. Recent method advances reveal diversity in beta bursts that provide deeper insights into their function and the underlying neural circuit activity motifs. We propose that brain-wide, spatiotemporal patterns of beta bursting reflect various cognitive operations and that their dynamics reveal nonlinear aspects of cortical processing.

Multiphoton imaging of hippocampal neural circuits: techniques and biological insights into region-, cell-type-, and pathway-specific functions

Significance

The function of the hippocampus in behavior and cognition has long been studied primarily through electrophysiological recordings from freely moving rodents. However, the application of optical recording methods, particularly multiphoton fluorescence microscopy, in the last decade or two has dramatically advanced our understanding of hippocampal function. This article provides a comprehensive overview of techniques and biological findings obtained from multiphoton imaging of hippocampal neural circuits.

Aim

This review aims to summarize and discuss the recent technical advances in multiphoton imaging of hippocampal neural circuits and the accumulated biological knowledge gained through this technology.

Approach

First, we provide a brief overview of various techniques of multiphoton imaging of the hippocampus and discuss its advantages, drawbacks, and associated key innovations and practices. Then, we review a large body of findings obtained through multiphoton imaging by region (CA1 and dentate gyrus), cell type (pyramidal neurons, inhibitory interneurons, and glial cells), and cellular compartment (dendrite and axon).

Results

Multiphoton imaging of the hippocampus is primarily performed under head-fixed conditions and can reveal detailed mechanisms of circuit operation owing to its high spatial resolution and specificity. As the hippocampus lies deep below the cortex, its imaging requires elaborate methods. These include imaging cannula implantation, microendoscopy, and the use of long-wavelength light sources. Although many studies have focused on the dorsal CA1 pyramidal cells, studies of other local and inter-areal circuitry elements have also helped provide a more comprehensive picture of the information processing performed by the hippocampal circuits. Imaging of circuit function in mouse models of Alzheimer's disease and other brain disorders such as autism spectrum disorder has also contributed greatly to our understanding of their pathophysiology.

Conclusions

Multiphoton imaging has revealed much regarding region-, cell-type-, and pathway-specific mechanisms in hippocampal function and dysfunction in health and disease. Future technological advances will allow further illustration of the operating principle of the hippocampal circuits via the large-scale, high-resolution, multimodal, and minimally invasive imaging.

Hippocampal and orbitofrontal neurons contribute to complementary aspects of associative structure

The ability to establish associations between environmental stimuli is fundamental for higher-order brain functions like state inference and generalization. Both the hippocampus and orbitofrontal cortex (OFC) play pivotal roles in this, demonstrating complex neural activity changes after associative learning. However, how precisely they contribute to representing learned associations remains unclear. Here, we train head-restrained mice to learn four 'odor-outcome' sequence pairs composed of several task variables-the past and current odor cues, sequence structure of 'cue-outcome' arrangement, and the expected outcome; and perform calcium imaging from these mice throughout learning. Sequence-splitting signals that distinguish between paired sequences are detected in both brain regions, reflecting associative memory formation. Critically, we uncover differential contents in represented associations by examining, in each area, how these task variables affect splitting signal generalization between sequence pairs. Specifically, the hippocampal splitting signals are influenced by the combination of past and current cues that define a particular sensory experience. In contrast, the OFC splitting signals are similar between sequence pairs that share the same sequence structure and expected outcome. These findings suggest that the hippocampus and OFC uniquely and complementarily organize the acquired associative structure.