Shared and specialized coding across posterior cortical areas for dynamic navigation decisions

Local Differences in Network Organization in the Auditory and Parietal Cortex, Revealed with Single Neuron Activation

The structure of local circuits is highly conserved across the cortex, yet the spatial and temporal properties of population activity differ fundamentally in sensory-level and association-level areas. In the sensory cortex, population activity has a shorter timescale and decays sharply over distance, supporting a population code for the fine-scale features of sensory stimuli. In the association cortex, population activity has a longer timescale and spreads over wider distances, a code that is suited to holding information in memory and driving behavior. We tested whether these differences in activity dynamics could be explained by differences in network structure. We targeted photostimulations to single excitatory neurons of layer 2/3, while monitoring surrounding population activity using two-photon calcium imaging. Experiments were performed in the auditory (AC) and posterior parietal cortex (PPC) within the same mice of both sexes, which also expressed a red fluorophore in somatostatin-expressing interneurons (SOM). In both cortical regions, photostimulations resulted in a spatially restricted zone of positive influence on neurons closely neighboring the targeted neuron and a more spatially diffuse zone of negative influence affecting more distant neurons (akin to a network-level "suppressive surround"). However, the relative spatial extents of positive and negative influence were different in AC and PPC. In PPC, the central zone of positive influence was wider, but the negative suppressive surround was more narrow than in AC, which could account for the larger-scale network dynamics in PPC. The more narrow central positive influence zone and wider suppressive surround in AC could serve to sharpen sensory representations.

Locomotion-dependent auditory gating to the parietal cortex guides multisensory decisions

Decision-making in mammals fundamentally relies on integrating multiple sensory inputs, with conflicting information resolved flexibly based on a dominant sensory modality. However, the neural mechanisms underlying state-dependent changes in sensory dominance remain poorly understood. Our study demonstrates that locomotion in mice shifts auditory-dominant decisions toward visual dominance during audiovisual conflicts. Using circuit-specific calcium imaging and optogenetic manipulations, we found that weakened visual representation in the posterior parietal cortex (PPC) leads to auditory-dominant decisions in stationary mice. Prolonged locomotion, however, promotes visual dominance by inhibiting auditory cortical neurons projecting to the PPC (ACPPC). This shift is mediated by secondary motor cortical neurons projecting to the auditory cortex (M2AC), which specifically inhibit ACPPC neurons without affecting auditory cortical projections to the striatum (ACSTR). Our findings reveal the neural circuit mechanisms underlying auditory gating to the association cortex depending on locomotion states, providing insights into the state-dependent changes in sensory dominance during multisensory decision-making.

An opponent striatal circuit for distributional reinforcement learning

Machine learning research has achieved large performance gains on a wide range of tasks by expanding the learning target from mean rewards to entire probability distributions of rewards-an approach known as distributional reinforcement learning (RL)1. The mesolimbic dopamine system is thought to underlie RL in the mammalian brain by updating a representation of mean value in the striatum2, but little is known about whether, where and how neurons in this circuit encode information about higher-order moments of reward distributions3. Here, to fill this gap, we used high-density probes (Neuropixels) to record striatal activity from mice performing a classical conditioning task in which reward mean, reward variance and stimulus identity were independently manipulated. In contrast to traditional RL accounts, we found robust evidence for abstract encoding of variance in the striatum. Chronic ablation of dopamine inputs disorganized these distributional representations in the striatum without interfering with mean value coding. Two-photon calcium imaging and optogenetics revealed that the two major classes of striatal medium spiny neurons-D1 and D2-contributed to this code by preferentially encoding the right and left tails of the reward distribution, respectively. We synthesize these findings into a new model of the striatum and mesolimbic dopamine that harnesses the opponency between D1 and D2 medium spiny neurons4-9 to reap the computational benefits of distributional RL.

Ramping dynamics in the frontal cortex unfold over multiple timescales during motor planning

Plans are formulated and refined throughout the period leading up to their execution, ensuring that the appropriate behaviors are enacted at the appropriate times. Although existing evidence suggests that memory circuits convey the passage of time through diverse neuronal responses, it remains unclear whether the neural circuits involved in planning exhibit analogous temporal dynamics. Using publicly available data, we analyzed how activity in the mouse frontal motor cortex evolves during motor planning. Individual neurons exhibited diverse ramping activity throughout a delay interval that preceded a planned movement. The collective activity of these neurons was useful for making temporal predictions that became increasingly precise as the movement time approached. This temporal diversity gave rise to a spectrum of encoding patterns, ranging from stable to dynamic representations of the upcoming movement. Our results indicate that ramping activity unfolds over multiple timescales during motor planning, suggesting a shared mechanism in the brain for processing temporal information related to both memories from the past and plans for the future. NEW & NOTEWORTHY Neuronal responses in the cortex are diverse, but the nature and functional consequences of this diversity remain ambiguous. We identified a specific pattern of temporal heterogeneity in the mouse frontal motor cortex, whereby the firing of different neurons ramps up at varying speeds before the execution of a movement. Our decoding analyses reveal that this heterogeneity in ramping dynamics enables precise and reliable encoding of movement plans and time across various timescales.

Sleep microstructure organizes memory replay

Recently acquired memories are reactivated in the hippocampus during sleep, an initial step for their consolidation1-3. This process is concomitant with the hippocampal reactivation of previous memories4-6, posing the problem of how to prevent interference between older and recent, initially labile, memory traces. Theoretical work has suggested that consolidating multiple memories while minimizing interference can be achieved by randomly interleaving their reactivation7-10. An alternative is that a temporal microstructure of sleep can promote the reactivation of different types of memories during specific substates. Here, to test these two hypotheses, we developed a method to simultaneously record large hippocampal ensembles and monitor sleep dynamics through pupillometry in naturally sleeping mice. Oscillatory pupil fluctuations revealed a previously unknown microstructure of non-REM sleep-associated memory processes. We found that memory replay of recent experiences dominated in sharp-wave ripples during contracted pupil substates of non-REM sleep, whereas replay of previous memories preferentially occurred during dilated pupil substates. Selective closed-loop disruption of sharp-wave ripples during contracted pupil non-REM sleep impaired the recall of recent memories, whereas the same manipulation during dilated pupil substates had no behavioural effect. Stronger extrinsic excitatory inputs characterized the contracted pupil substate, whereas higher recruitment of local inhibition was prominent during dilated pupil substates. Thus, the microstructure of non-REM sleep organizes memory replay, with previous versus new memories being temporally segregated in different substates and supported by local and input-driven mechanisms, respectively. Our results suggest that the brain can multiplex distinct cognitive processes during sleep to facilitate continuous learning without interference.

Mesoscale functional architecture in medial posterior parietal cortex

The posterior parietal cortex (PPC) in mice has various functions including multisensory integration1-3, vision-guided behaviors4-6, working memory7-13, and posture control14,15. However, an integrated understanding of these functions and their cortical localizations in and around the PPC and higher visual areas (HVAs), has not been completely elucidated. Here we simultaneously imaged the activity of thousands of neurons within a 3 × 3 mm2 field-of-view, including eight cortical areas around the PPC, during behavior with a two-photon mesoscope16. Mice performed both a vision-guided task and a choice history-dependent task, and the imaging results revealed distinct, localized, behavior-related functions of two medial PPC areas. Neurons in the anteromedial (AM) HVA responded to both vision and choice information, and thus AM is a locus of association between these channels. By contrast, the anterior (A) HVA stores choice history with sequential dynamics and represents posture. Mesoscale correlation analysis on the intertrial variability of neuronal activity demonstrated that neurons in area A shared fluctuations with the primary somatosensory area, while neurons in AM exhibited diverse, area-dependent interactions. Pairwise interarea interactions among neurons were precisely predicted by the anatomical input correlations, with the exception of some global interactions. Thus, the medial PPC has two distinct modules, areas A and AM, which each have distinctive modes of cortical communication. These medial PPC modules can serve separate higher-order functions: area A for transmission of information including posture, movement, and working memory; and area AM for multisensory and cognitive integration with locally processed signals.

An optical brain-machine interface reveals a causal role of posterior parietal cortex in goal-directed navigation

Cortical circuits contain diverse sensory, motor, and cognitive signals, and form densely recurrent networks. This creates challenges for identifying causal relationships between neural populations and behavior. We developed a calcium imaging-based brain-machine interface (BMI) to study the role of posterior parietal cortex (PPC) in controlling navigation in virtual reality. By training a decoder to estimate navigational heading and velocity from PPC activity during virtual navigation, we discovered that mice could immediately navigate toward goal locations when control was switched to BMI. No learning or adaptation was observed during BMI, indicating that naturally occurring PPC activity patterns are sufficient to drive navigational trajectories in real time. During successful BMI trials, decoded trajectories decoupled from the mouse's physical movements, suggesting that PPC activity relates to intended trajectories. Our work demonstrates a role for PPC in navigation and offers a BMI approach for investigating causal links between neural activity and behavior.

An adaptable, reusable, and light implant for chronic Neuropixels probes

Electrophysiology has proven invaluable to record neural activity, and the development of Neuropixels probes dramatically increased the number of recorded neurons. These probes are often implanted acutely, but acute recordings cannot be performed in freely moving animals and the recorded neurons cannot be tracked across days. To study key behaviors such as navigation, learning, and memory formation, the probes must be implanted chronically. An ideal chronic implant should (1) allow stable recordings of neurons for weeks; (2) allow reuse of the probes after explantation; (3) be light enough for use in mice. Here, we present the "Apollo Implant", an open-source and editable device that meets these criteria and accommodates up to two Neuropixels 1.0 or 2.0 probes. The implant comprises a "payload" module which is attached to the probe and is recoverable, and a "docking" module which is cemented to the skull. The design is adjustable, making it easy to change the distance between probes, the angle of insertion, and the depth of insertion. We tested the implant across eight labs in head-fixed mice, freely moving mice, and freely moving rats. The number of neurons recorded across days was stable, even after repeated implantations of the same probe. The Apollo implant provides an inexpensive, lightweight, and flexible solution for reusable chronic Neuropixels recordings.

Differential stability of task variable representations in retrosplenial cortex

Cortical neurons store information across different timescales, from seconds to years. Although information stability is variable across regions, it can vary within a region as well. Association areas are known to multiplex behaviorally relevant variables, but the stability of their representations is not well understood. Here, we longitudinally recorded the activity of neuronal populations in the mouse retrosplenial cortex (RSC) during the performance of a context-choice association task. We found that the activity of neurons exhibits different levels of stability across days. Using linear classifiers, we quantified the stability of three task-relevant variables. We find that RSC representations of context and trial outcome display higher stability than motor choice, both at the single cell and population levels. Together, our findings show an important characteristic of association areas, where diverse streams of information are stored with varying levels of stability, which may balance representational reliability and flexibility according to behavioral demands.

Flexible gating between subspaces in a neural network model of internally guided task switching

Behavioral flexibility relies on the brain's ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism that is consistent across networks trained with different hyperparameters. The networks' dynamical trajectories for different rules resided in separate subspaces of population activity; the subspaces collapsed and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, illustrating how a phenomenological description of representational subspaces is explained by a specific circuit mechanism.

Stimulus information guides the emergence of behavior-related signals in primary somatosensory cortex during learning

Neurons in the primary cortex carry sensory- and behavior-related information, but it remains an open question how this information emerges and intersects together during learning. Current evidence points to two possible learning-related changes: sensory information increases in the primary cortex or sensory information remains stable, but its readout efficiency in association cortices increases. We investigated this question by imaging neuronal activity in mouse primary somatosensory cortex before, during, and after learning of an object localization task. We quantified sensory- and behavior-related information and estimated how much sensory information was used to instruct perceptual choices as learning progressed. We find that sensory information increases from the start of training, while choice information is mostly present in the later stages of learning. Additionally, the readout of sensory information becomes more efficient with learning as early as in the primary sensory cortex. Together, our results highlight the importance of primary cortical neurons in perceptual learning.

Behavioral strategy shapes activation of the Vip-Sst disinhibitory circuit in visual cortex

In complex environments, animals can adopt diverse strategies to find rewards. How distinct strategies differentially engage brain circuits is not well understood. Here, we investigate this question, focusing on the cortical Vip-Sst disinhibitory circuit between vasoactive intestinal peptide-postive (Vip) interneurons and somatostatin-positive (Sst) interneurons. We characterize the behavioral strategies used by mice during a visual change detection task. Using a dynamic logistic regression model, we find that individual mice use mixtures of a visual comparison strategy and a statistical timing strategy. Separately, mice also have periods of task engagement and disengagement. Two-photon calcium imaging shows large strategy-dependent differences in neural activity in excitatory, Sst inhibitory, and Vip inhibitory cells in response to both image changes and image omissions. In contrast, task engagement has limited effects on neural population activity. We find that the diversity of neural correlates of strategy can be understood parsimoniously as the increased activation of the Vip-Sst disinhibitory circuit during the visual comparison strategy, which facilitates task-appropriate responses.

Different state-dependence of population codes across cortex

During perceptual decision-making, behavioral performance varies with changes in internal states such as arousal, motivation, and strategy. Yet it is unknown how these internal states affect information coding across cortical regions involved in differing aspects of sensory perception and decision-making. We recorded neural activity from the primary auditory cortex (AC) and posterior parietal cortex (PPC) in mice performing a navigation-based sound localization task. We then modeled transitions in the behavioral strategies mice used during task performance. Mice transitioned between three latent performance states with differing decision-making strategies: an 'optimal' state and two 'sub-optimal' states characterized by choice bias and frequent errors. Performance states strongly influenced population activity patterns in association but not sensory cortex. Surprisingly, activity of individual PPC neurons was better explained by external inputs and behavioral variables during suboptimal behavioral performance than in the optimal performance state. Furthermore, shared variability across neurons (coupling) in PPC was strongest in the optimal state. In AC, shared variability was similarly weak across all performance states. Together, these findings indicate that neural activity in association cortex is more strongly linked to internal state than in sensory cortex.

Synaptic wiring motifs in posterior parietal cortex support decision-making

The posterior parietal cortex exhibits choice-selective activity during perceptual decision-making tasks1-10. However, it is not known how this selective activity arises from the underlying synaptic connectivity. Here we combined virtual-reality behaviour, two-photon calcium imaging, high-throughput electron microscopy and circuit modelling to analyse how synaptic connectivity between neurons in the posterior parietal cortex relates to their selective activity. We found that excitatory pyramidal neurons preferentially target inhibitory interneurons with the same selectivity. In turn, inhibitory interneurons preferentially target pyramidal neurons with opposite selectivity, forming an opponent inhibition motif. This motif was present even between neurons with activity peaks in different task epochs. We developed neural-circuit models of the computations performed by these motifs, and found that opponent inhibition between neural populations with opposite selectivity amplifies selective inputs, thereby improving the encoding of trial-type information. The models also predict that opponent inhibition between neurons with activity peaks in different task epochs contributes to creating choice-specific sequential activity. These results provide evidence for how synaptic connectivity in cortical circuits supports a learned decision-making task.

Reward-based option competition in human dorsal stream and transition from stochastic exploration to exploitation in continuous space

Primates exploring and exploiting a continuous sensorimotor space rely on dynamic maps in the dorsal stream. Two complementary perspectives exist on how these maps encode rewards. Reinforcement learning models integrate rewards incrementally over time, efficiently resolving the exploration/exploitation dilemma. Working memory buffer models explain rapid plasticity of parietal maps but lack a plausible exploration/exploitation policy. The reinforcement learning model presented here unifies both accounts, enabling rapid, information-compressing map updates and efficient transition from exploration to exploitation. As predicted by our model, activity in human frontoparietal dorsal stream regions, but not in MT+, tracks the number of competing options, as preferred options are selectively maintained on the map, while spatiotemporally distant alternatives are compressed out. When valuable new options are uncovered, posterior β1/α oscillations desynchronize within 0.4 to 0.7 s, consistent with option encoding by competing β1-stabilized subpopulations. Together, outcomes matching locally cached reward representations rapidly update parietal maps, biasing choices toward often-sampled, rewarded options.

Circuit mechanisms of navigation strategy learning in mice

Navigation tasks involve the gradual selection and deployment of increasingly effective searching procedures to reach targets. The brain mechanisms underlying such complex behavior are poorly understood, but their elucidation might provide insights into the systems linking exploration and decision making in complex learning. Here, we developed a trial-by-trial goal-related search strategy analysis as mice learned to navigate identical water mazes encompassing distinct goal-related rules and monitored the strategy deployment process throughout learning. We found that navigation learning involved the following three distinct phases: an early phase during which maze-specific search strategies are deployed in a minority of trials, a second phase of preferential increasing deployment of one search strategy, and a final phase of increasing commitment to this strategy only. The three maze learning phases were affected differently by inhibition of retrosplenial cortex (RSC), dorsomedial striatum (DMS), or dorsolateral striatum (DLS). Through brain region-specific inactivation experiments and gain-of-function experiments involving activation of learning-related cFos+ ensembles, we unraveled how goal-related strategy selection relates to deployment throughout these sequential processes. We found that RSC is critically important for search strategy selection, DMS mediates strategy deployment, and DLS ensures searching consistency throughout maze learning. Notably, activation of specific learning-related ensembles was sufficient to direct strategy selection (RSC) or strategy deployment (DMS) in a different maze. Our results establish a goal-related search strategy deployment approach to dissect unsupervised navigation learning processes and suggest that effective searching in navigation involves evidence-based goal-related strategy direction by RSC, reinforcement-modulated strategy deployment through DMS, and online guidance through DLS.

New information triggers prospective codes to adapt for flexible navigation

Navigating a dynamic world requires rapidly updating choices by integrating past experiences with new information. In hippocampus and prefrontal cortex, neural activity representing future goals is theorized to support planning. However, it remains unknown how prospective goal representations incorporate new, pivotal information. Accordingly, we designed a novel task that precisely introduces new information using virtual reality, and we recorded neural activity as mice flexibly adapted their planned destinations. We found that new information triggered increased hippocampal prospective representations of both possible goals; while in prefrontal cortex, new information caused prospective representations of choices to rapidly shift to the new choice. When mice did not flexibly adapt, prefrontal choice codes failed to switch, despite relatively intact hippocampal goal representations. Prospective code updating depended on the commitment to the initial choice and degree of adaptation needed. Thus, we show how prospective codes update with new information to flexibly adapt ongoing navigational plans.

Mixtures of strategies underlie rodent behavior during reversal learning

In reversal learning tasks, the behavior of humans and animals is often assumed to be uniform within single experimental sessions to facilitate data analysis and model fitting. However, behavior of agents can display substantial variability in single experimental sessions, as they execute different blocks of trials with different transition dynamics. Here, we observed that in a deterministic reversal learning task, mice display noisy and sub-optimal choice transitions even at the expert stages of learning. We investigated two sources of the sub-optimality in the behavior. First, we found that mice exhibit a high lapse rate during task execution, as they reverted to unrewarded directions after choice transitions. Second, we unexpectedly found that a majority of mice did not execute a uniform strategy, but rather mixed between several behavioral modes with different transition dynamics. We quantified the use of such mixtures with a state-space model, block Hidden Markov Model (block HMM), to dissociate the mixtures of dynamic choice transitions in individual blocks of trials. Additionally, we found that blockHMM transition modes in rodent behavior can be accounted for by two different types of behavioral algorithms, model-free or inference-based learning, that might be used to solve the task. Combining these approaches, we found that mice used a mixture of both exploratory, model-free strategies and deterministic, inference-based behavior in the task, explaining their overall noisy choice sequences. Together, our combined computational approach highlights intrinsic sources of noise in rodent reversal learning behavior and provides a richer description of behavior than conventional techniques, while uncovering the hidden states that underlie the block-by-block transitions.

Specialized structure of neural population codes in parietal cortex outputs

Do cortical neurons that send axonal projections to the same target area form specialized population codes for transmitting information? We used calcium imaging in mouse posterior parietal cortex (PPC), retrograde labeling, and statistical multivariate models to address this question during a delayed match-to-sample task. We found that PPC broadcasts sensory, choice, and locomotion signals widely, but sensory information is enriched in the output to anterior cingulate cortex. Neurons projecting to the same area have elevated pairwise activity correlations. These correlations are structured as information-limiting and information-enhancing interaction networks that collectively enhance information levels. This network structure is unique to sub-populations projecting to the same target and strikingly absent in surrounding neural populations with unidentified projections. Furthermore, this structure is only present when mice make correct, but not incorrect, behavioral choices. Therefore, cortical neurons comprising an output pathway form uniquely structured population codes that enhance information transmission to guide accurate behavior.

A cell-type-specific error-correction signal in the posterior parietal cortex

Neurons in the posterior parietal cortex contribute to the execution of goal-directed navigation1 and other decision-making tasks2-4. Although molecular studies have catalogued more than 50 cortical cell types5, it remains unclear what distinct functions they have in this area. Here we identified a molecularly defined subset of somatostatin (Sst) inhibitory neurons that, in the mouse posterior parietal cortex, carry a cell-type-specific error-correction signal for navigation. We obtained repeatable experimental access to these cells using an adeno-associated virus in which gene expression is driven by an enhancer that functions specifically in a subset of Sst cells6. We found that during goal-directed navigation in a virtual environment, this subset of Sst neurons activates in a synchronous pattern that is distinct from the activity of surrounding neurons, including other Sst neurons. Using in vivo two-photon photostimulation and ex vivo paired patch-clamp recordings, we show that nearby cells of this Sst subtype excite each other through gap junctions, revealing a self-excitation circuit motif that contributes to the synchronous activity of this cell type. These cells selectively activate as mice execute course corrections for deviations in their virtual heading during navigation towards a reward location, for both self-induced and experimentally induced deviations. We propose that this subtype of Sst neurons provides a self-reinforcing and cell-type-specific error-correction signal in the posterior parietal cortex that may help with the execution and learning of accurate goal-directed navigation trajectories.

An entorhinal-like region in food-caching birds

The mammalian entorhinal cortex routes inputs from diverse sources into the hippocampus. This information is mixed and expressed in the activity of many specialized entorhinal cell types, which are considered indispensable for hippocampal function. However, functionally similar hippocampi exist even in non-mammals that lack an obvious entorhinal cortex or, generally, any layered cortex. To address this dilemma, we mapped extrinsic hippocampal connections in chickadees, whose hippocampi are used for remembering numerous food caches. We found a well-delineated structure in these birds that is topologically similar to the entorhinal cortex and interfaces between the hippocampus and other pallial regions. Recordings of this structure revealed entorhinal-like activity, including border and multi-field grid-like cells. These cells were localized to the subregion predicted by anatomical mapping to match the dorsomedial entorhinal cortex. Our findings uncover an anatomical and physiological equivalence of vastly different brains, suggesting a fundamental nature of entorhinal-like computations for hippocampal function.

Arousal and Locomotion Differently Modulate Activity of Somatostatin Neurons across Cortex

Arousal powerfully influences cortical activity, in part by modulating local inhibitory circuits. Somatostatin (SOM)-expressing inhibitory interneurons are particularly well situated to shape local population activity in response to shifts in arousal, yet the relationship between arousal state and SOM activity has not been characterized outside of sensory cortex. To determine whether SOM activity is similarly modulated by behavioral state across different levels of the cortical processing hierarchy, we compared the behavioral modulation of SOM-expressing neurons in auditory cortex (AC), a primary sensory region, and posterior parietal cortex (PPC), an association-level region of cortex, in mice. Behavioral state modulated activity differently in AC and PPC. In PPC, transitions to high arousal were accompanied by large increases in activity across the full PPC neural population, especially in SOM neurons. In AC, arousal transitions led to more subtle changes in overall activity, as individual SOM and Non-SOM neurons could be either positively or negatively modulated during transitions to high arousal states. The coding of sensory information in population activity was enhanced during periods of high arousal in AC, but not in PPC. Our findings suggest unique relationships between activity in local circuits and arousal across cortex, which may be tailored to the roles of specific cortical regions in sensory processing or the control of behavior.

A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions

Decision-making requires flexibility to rapidly switch one's actions in response to sensory stimuli depending on information stored in memory. We identified cortical areas and neural activity patterns underlying this flexibility during virtual navigation, where mice switched navigation toward or away from a visual cue depending on its match to a remembered cue. Optogenetics screening identified V1, posterior parietal cortex (PPC), and retrosplenial cortex (RSC) as necessary for accurate decisions. Calcium imaging revealed neurons that can mediate rapid navigation switches by encoding a mixture of a current and remembered visual cue. These mixed selectivity neurons emerged through task learning and predicted the mouse's choices by forming efficient population codes before correct, but not incorrect, choices. They were distributed across posterior cortex, even V1, and were densest in RSC and sparsest in PPC. We propose flexibility in navigation decisions arises from neurons that mix visual and memory information within a visual-parietal-retrosplenial network.

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.

An entorhinal-like region in food-caching birds

The mammalian entorhinal cortex routes inputs from diverse sources into the hippocampus. This information is mixed and expressed in the activity of many specialized entorhinal cell types, which are considered indispensable for hippocampal function. However, functionally similar hippocampi exist even in non-mammals that lack an obvious entorhinal cortex, or generally any layered cortex. To address this dilemma, we mapped extrinsic hippocampal connections in chickadees, whose hippocampi are used for remembering numerous food caches. We found a well-delineated structure in these birds that is topologically similar to the entorhinal cortex and interfaces between the hippocampus and other pallial regions. Recordings of this structure revealed entorhinal-like activity, including border and multi-field grid-like cells. These cells were localized to the subregion predicted by anatomical mapping to match the dorsomedial entorhinal cortex. Our findings uncover an anatomical and physiological equivalence of vastly different brains, suggesting a fundamental nature of entorhinal-like computations for hippocampal function.

Everything, everywhere, all at once: Functional specialization and distributed coding in the cerebral cortex

In this issue of Neuron, Tseng and colleagues reveal functional gradients in the mouse posterior cortex that reconcile specialized and distributed processing during flexible, goal-directed navigation.