Neuron

The decision criterion is foundational to theories of decision-making, yet little is known about its neural underpinnings. In memory-based decision making, the criterion sets the minimal memory strength for something to be familiar, but whether or how it is distinctly represented from memory strength is unknown. We recorded single neurons in the medial frontal cortex (MFC) and medial temporal lobe (MTL), both implicated in memory-based decisions, while participants made decisions under different decision criteria. We identified criterion-selective (CS) neurons in the MFC that tracked the criterion regardless of memory strength, and memory-selective (MS) neurons in both regions that tracked memory strength regardless of the criterion. CS neurons signaled the criterion before MS neurons signaled memory strength, and a race model incorporating both neuron types outperformed one using MS neurons alone. These findings reveal two independent cellular substrates, one for the decision criterion and one for memory strength, whose joint activity underlies memory-based decisions.

Natural vision is continuously shaped by an animal's own movements, which determine what enters the visual system and when visual input changes. Gaze shifts are known to initiate a temporally structured sequence of activity in primary visual cortex (V1), but how the visual content sampled by each movement contributes to this sequence has remained unclear. We recorded visual input, eye and head movements, and V1 activity in freely moving mice, and asked how the visual content sampled on each gaze shift shapes the response. The magnitude of visual change induced by each gaze shift scaled the amplitude of responses according to each neuron's characteristic response profile, while movement amplitude alone did not reproduce this modulation in darkness, supporting a role of visual input in driving the sequence. Activity following gaze shifts reflected each neuron's spatial receptive field structure, and visual filters estimated under head-fixed conditions predicted the relative timing of spike responses during gaze shifts of freely moving animals, demonstrating that gaze shift responses encode visual information. At the population level, decoded V1 activity shifted toward the scene sampled after each gaze shift. Thus, across single-neuron, spatial, temporal, and population measures, V1 activity tracked the content of each sample beyond movement timing alone, indicating that gaze shifts act as sampling events that result in V1 encoding visual information in temporally ordered responses.

A prevailing view in affective neuroscience holds that innate and learned behaviors are processed through distinct neuroanatomical pathways, one pre-wired, the other running on synaptic plasticity. However, here we show that processing innate and learned threats in the lateral amygdala deviates fundamentally from this view. We tracked the three core elements of circuit function (excitatory neurons, inhibitory neurons, and neuromodulators) in mice, as they were exposed to an innately aversive looming stimulus and as they learned a cued threat. Tracking the same neurons across sessions, revealed a subpopulation of excitatory neurons recruited by the innate threat that was preferentially potentiated following auditory threat learning. Furthermore, both forms of threat converged on the same modulatory mechanisms: the disinhibitory VIP/SST motif and norepinephrine release, but with a critical difference. While the innately aversive stimulus possessed privileged access to these pathways, the learned cue acquired access through synaptic plasticity. In this instance, learning about a new threat apparently recruits a circuit that protects animals from natural threats.

Decision-making involves dynamic evaluation of competing options. Recording 16,495 neurons across nine frontal and subcortical areas in macaques revealed brain-wide fluctuations between encoding the attributes of available options. These dynamic representations track deliberation and scale with decision difficulty. Instead of binding attribute information of different options together, our analyses show that these representations are associated with dynamic area- and attribute-specific changes in neural geometry.

Flexible behavior requires generalizable memory and learning. For example, we rapidly learn to commute in new cities by reusing our knowledge of Euclidean two-dimensional space and structures like roundabouts and subway systems without forgetting how to get to a favorite restaurant back home. Yet we lack a detailed understanding of how the brain uses existing knowledge to generalize while retaining the memory of specific past experiences. To address this gap, we combine behavioral measurements, neural recordings, and computational modeling in an abstract sequential image navigation task to study three forms of generalization: mnemonic generalization, from visual to mental navigation; transitive generalization, from trained to novel routes; and structural generalization, from familiar to new environments. In contrast to monkeys and humans, recurrent neural networks failed at all generalizations. We found that a structured entorhinal-hippocampal memory model, which provides a content-independent metric scaffold based on grid cells for storing experience, coupled to a policy recurrent network, succeeds at all three. The content-independent scaffold enables mnemonic and transitive generalization through path integration and facilitates structural generalization by allowing reuse of a previously learned action policy network. Moreover, the scaffold's high combinatorial capacity permits continual learning without catastrophic forgetting. We recorded neural activity from the entorhinal cortex and posterior parietal cortex of two monkeys performing the task and found two distinct computations across the neural population. Modularizing an entorhinal and parietal action policy network to separately track distance and initiate actions captured the distinct population dynamics and improved model performance. Finally, we added a reinforcement learning module to the network that enabled it to learn an appropriate scale factor to align the grid periodicity with the environmental temporal structure. Our findings reveal that an architecture which factorizes invariant metric representations from rapid sensory associations and a transferable policy learns, generalizes, and remembers like the brain.

Flexible behavior requires using past experiences to configure cortical computations to suit current task demands. A central question in neuroscience is how memory representations control such reconfigurations. Although hippocampal (HPC) engrams can drive learned behaviors, prior studies have been largely limited to a fixed stimulus-response computation. Thus, whether engrams can drive retrieval of a set of stimulus-response mappings, rather than a specific response itself, remains unresolved. Moreover, how engrams affect cortical task representations and dynamics so as to produce engram-consistent behavior remains unstudied. Here, we address both questions by combining tagging and reactivation of HPC engrams with simultaneous large-scale recordings in medial prefrontal cortex (mPFC) during a context-dependent task-switching paradigm in mice. We report that HPC engram reactivation caused mice to apply engram-consistent decision-rules rather than a specific motor output. Simultaneous mPFC recordings revealed that reactivating the HPC engram for a given context reinstated the representation of that context in mPFC within hundreds of milliseconds, indicating that it was mediated by rapid network effects, leading to choice behavior consistent with the reactivated HPC engram. Other aspects of endogenous dynamics in mPFC were left remarkably intact. Together, our findings provide direct causal evidence that HPC engrams can configure task-relevant population states of downstream cortical circuits in real time, establishing a neural mechanism by which memory traces control flexible behavior.

Substantia nigra pars compacta (SNc) dopaminergic neurons integrate synaptic inputs through dendrites that project laterally within the SNc and ventrally into the substantia nigra pars reticulata (SNr), which then determines dopaminergic output. We use spatially-specific optogenetics to map excitatory subthalamic nucleus (STN) and pedunculopontine nucleus (PPN) as well as inhibitory SNr inputs that provide feedforward inhibition onto SNc neurons. Using multiple GABAergic mouse lines, we find that SNr inputs selectively inhibit SNc somas and proximal dendrites without preference for SNr versus SNc dendrites. Using glutamatergic mouse lines, we find that PPN inputs selectively excite somas and proximal dendrites, while STN inputs selectively excite SNr dendrites, mirroring dendrite-selective inhibition from striosomes. These findings reveal a high level of spatial organization in SNc synaptic inputs and point to separate functional roles for STN versus PPN control of dopaminergic activity. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/742307v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@ac8c5borg.highwire.dtl.DTLVardef@32cb89org.highwire.dtl.DTLVardef@d08f79org.highwire.dtl.DTLVardef@7b546d_HPS_FORMAT_FIGEXP M_FIG C_FIG

Psilocybin profoundly alters visual perception, yet the neuronal mechanisms underlying these effects remain unclear. Here we combined large-scale Neuropixels recordings with cell-type specific optogenetics in head-fixed mice performing a visual change-detection task. Psilocybin severely impaired task performance without overt motor deficits. In cortex, the drug modestly suppressed activity of layer 5 neurons while preserving representations of image identity. By contrast, psilocybin imposed a 4-Hz oscillation on visually evoked activity that preferentially affected neurons encoding image change rather than image identity. Under psilocybin, expected image repetitions aberrantly recruited change-encoding ensembles and shifted cortical population dynamics towards trajectories normally evoked by genuine stimulus changes. These effects were strongest in somatostatin-expressing (SST) interneurons in visual cortex. The strength of this modulation depended on image structure and was greatest for images with clear, continuous contours, which preferentially recruited change-encoding ensembles. These findings demonstrate that psilocybin drives internally generated cortical surprise signals, providing a circuit mechanism for altered perception in the acute psychedelic state.

Neurovascular coupling (NVC) links neuronal activity to local hemodynamics, underlies functional imaging signals such as fMRI, and is often disrupted in neurological disorders. Although inhibitory interneurons can directly signal to blood vessels, their relative contribution to NVC during sensory processing remains unclear. Here, we combined mesoscale cell-type-specific calcium imaging, hemodynamic imaging, and chemogenetic silencing to determine how parvalbumin-expressing (PV) and somatostatin-expressing (SOM) interneurons shape functional hyperemia in the mouse barrel cortex. During single-whisker stimulations, PV and SOM activity exhibited strong spatial co-variation with local hemodynamic responses across the barrel field. Silencing PV interneurons produced variable changes in local hemodynamic responses that closely tracked excitatory activity while disproportionately broadening the spatial spread of the hemodynamic response, whereas SOM silencing exerted comparatively modest effects. Together, these findings suggest that NVC predominantly reflects overall circuit activity, even when inhibitory signaling is broadly impaired.

UFMylation, a ubiquitin-like protein modification, drives tau spread through the brain, but what keeps this process in check has remained unclear. We show that TRAPPC8 acts as a natural brake on UFMylation, binding directly to the E1 enzyme UBA5 to dampen pathway activity. In Alzheimer's disease brain tissue, TRAPPC8 is reduced while UFMylation is elevated, suggesting this brake fails as disease progresses. Sustaining UFMylation in human iPSC-derived neurons increased tau aggregation and spread while disrupting lysosomal function and lipid balance. Restoring TRAPPC8-UBA5 binding reversed these defects through the lysosomal protein CLN8, which restored lysosomal function and reduced tau pathology. Notably, expressing just the UBA5-binding region of TRAPPC8 was enough to suppress tau pathology in vivo, marking it as a promising therapeutic target.

Adaptive behaviour usually requires exploiting regularities in the environment, but in competitive settings the opposite can be true: predictable choice patterns can be exploited by others, making unpredictability itself advantageous. How neural circuits generate such strategic variability remains poorly understood. Here, we trained mice to play a zero-sum game against an opponent that exploited statistical regularities in their choices and rewards, and tracked their behaviour and dorsal cortical dynamics across learning. Using a hidden Markov model, we found that mice transitioned from structured, predictable strategies towards a near-optimal stochastic strategy as they learned. Applying the same framework to monkeys playing the same game identified a shared stochastic strategy across species, despite differences in how it was deployed. Cortex-wide imaging revealed that stochastic choices were associated with reduced representation of reward history, while immediate reward signals remained robust. Critically, while the strength of cortical reward signals predicted subsequent choice during reward-guided behaviour, this relationship was abolished during stochastic behaviour. Thus, adaptive stochasticity does not simply arise from a loss of reward information, but from selectively decoupling reward from future choice. These results reveal a neural mechanism through which animals suppress otherwise useful reward-guided structure to generate adaptive unpredictability in competitive environments.

Adaptive choice requires transforming the evaluation of available options into commitment to a specific action but understanding how this transformation is implemented across neural circuits remains a central challenge. Here we recorded well-isolated neurons in the substantia nigra pars reticulata (SNr) and frontal eye field (FEF), which send parallel projections to the superior colliculus for driving the eye movement choice, while monkeys performed a sequential-offer choice task designed to partially dissociate scene-defined ordinal rank from the categorical commitment. Before target onset, SNr displayed stronger scene-related modulation than FEF. During target evaluation, SNr activity showed ordered modulation across behavioral outcomes dominated by ordinal rank, whereas FEF activity categorically separated acceptance from rejection and strongly encoded target direction. Behavioral model decomposition revealed that ordinal rank alone best explained SNr activity, outperforming both reward magnitude and even a composite acceptability measure that incorporated rank together with reward, scene context, and waiting cost, whereas FEF activity was best explained by categorical commitment. This dissociation was consistent across multivariable modeling, single-neuron response patterns, and all three monkeys. Together, these findings support a division of labor in which context-dependent evaluation and categorical commitment are distributed across parallel basal ganglia and frontal cortical output pathways to efficiently guide voluntary choices.

Local field potentials (LFPs) contain signals generated by individual neurons and by coordinated population activity, but distinguishing these contributions remains a challenge. We examined how hippocampal LFPs at different frequencies predict single-neuron spiking during spatial navigation in male rats using two datasets. At each frequency, we assessed the spatial distribution of LFP-based prediction across the electrode array and its generalization across behavioral contexts in which a neuron remained active, but its co-active peers changed. For pyramidal cells, spatially distributed LFP features, consistent with population-level activity, contributed primarily to spike prediction at theta ([~]10 Hz) and its harmonics. In contrast, spatially localized signals, consistent with the recorded neurons activity, contributed predominantly at higher frequencies. Notably, gamma-band LFPs (30-80 Hz) provided comparatively little information about pyramidal-cell spiking, while distributed LFP features predicted interneuron spiking across a broader frequency range. Together, this predictive approach separates local and distributed correlates of spiking within the LFP. In hippocampal CA1, these correlates fell into two spatiotemporal regimes: a distributed regime expressed primarily at theta frequencies and a localized regime reflecting single-neuron activity at higher frequencies. Significance StatementBrain waves reflect the activity of neuronal populations across spatiotemporal scales. We asked which features of brain waves recorded in the hippocampus predict an individual neurons spiking activity as a rat navigated a maze. Brain waves predicted spiking activity most accurately at two different regimes: a low-frequency, spatially distributed regime and a high-frequency, local regime. The distributed regime was concentrated in the [~]10 Hz theta band, while the local regime appeared to reflect the recorded neurons activity at high frequencies (>100 Hz). Surprisingly, the gamma band, often linked to neuronal communication and cell assemblies, was the weakest predictor of place cell activity. Our work provides a way to separate individual-neuron and population-level information in hippocampal LFPs.

Inflammatory activation and lipid remodeling are linked features of microglial states, but how inflammatory transcription factors shape microglial lipid handling is unclear. Here we show that STAT1 sets neutral-lipid content in microglia through a route not predicted by lipid-handling transcription. Acute STAT1 depletion in primary microglia lowered neutral-lipid content while lipid-uptake and lipid-storage programs were induced, and interferon-{gamma} activation moved inflammatory transcription in the opposite direction yet lowered lipid content alike. Single-cell transcriptomic and chromatin profiling of Stat1- and Irf1-deficient mice showed that STAT1 and IRF1 organize overlapping inflammatory and lipid-handling programs, with genome-wide accessibility changes that did not predict transcriptional output at individual lipid-handling loci. Microglia co-expressing STAT1 and APOE recurred across Alzheimer's disease and multiple sclerosis datasets. Transcriptional program engagement is therefore separable from cellular lipid state, and lipid-handling gene expression cannot be read as a proxy for microglial lipid content.

All physical interactions between an organism and its environment occur within the space immediately adjacent to and surrounding its body, its peripersonal space (PPS). This space has been extensively studied behaviorally in humans, and through sparse single-neuron recordings in primates. However, how PPS is represented and organized at cellular and circuit scales remains poorly understood. Here, using dense extracellular recordings in the mouse rostro-lateral visual cortex (VISrl; >19,000 single units), we reveal the cellular and circuit organization of PPS in mice. Visuo-tactile neurons prioritize near-body space while also representing farther space in a direction-selective manner, tracking approaching but not receding objects across the environment. VISrl PPS neurons integrate vision and touch nonlinearly, and their tactile responses are progressively facilitated as visual objects near the body. PPS neurons are embedded in structured networks characterized by "like-to-like" functional connectivity and remap according to recent visuo-tactile statistics. Together, these findings establish VISrl as a circuit-accessible substrate for PPS, and reveal how near-body space is represented by a dynamic, plastic, multisensory cortical network.

The corticothalamic neurons from layer 6 (L6CT) of primary sensory cortices provide extensive input to the thalamus in addition to projecting within the cortex, positioning them to play a key hypothesized role in shaping thalamocortical signaling. With the expansion of tools for precise functional identification of L6CT neurons for in-vivo electrophysiology, increasing evidence highlights L6CT neurons as dynamic gain modulators of thalamocortical sensory responses. However much of the work to date has been conducted under anesthesia and not in the context of awake and/or behaving animals. In this study, we show that L6CT neurons in the awake mouse convey information about ascending sensory inputs, the timing of which is fast enough to contribute to the sensory response of neurons throughout the thalamocortical circuit. Overall, L6CT neurons robustly encode the presence vs absence of a sensory stimulus but are relatively weak encoders of the fine details. Benchmarked against the activity of other excitatory cortical neuron, we also provide evidence for L6CT neurons as predictors of the behavioral outcome during a trained detection task. Taken together, the results in this study tie L6CT neurons to behavior in tactile detection, one of the most fundamental functional roles of the pathway.

Astrocytes influence synaptic plasticity and neuronal function through astrocytic calcium dynamics (ACD). However, astrocyte contribution to cognitive operations like reinforcement learning (RL) remains unclear. To examine this, we trained mice on a RL dependent probabilistic decision-making task. We attenuated ACD across distinct striatal regions, and found ACD attenuation specifically in ventral striatum (VS) increased decision noisiness and impaired reward-guided choice performance. This effect was largely due to a reduction in win-stay behavior. Using in-vivo calcium imaging, we found that VS ACD correlated with reward prediction errors (RPEs). Furthermore, these trial-by-trial ACD fluctuations predicted trial-by-trial choice variability. In-silico lesions of a biologically constrained circuit model suggest that astrocytes could regulate behavioral variability by sharing RPE signals across populations of striatal neurons. Together, these results suggest that VS astrocytes contribute to cortico-striatal functions to mediate decision noisiness.

We utilize internal representations of meaning for two purposes: to understand the words we hear and to generate our own speech. This dual requirement necessitates abstract, modality-agnostic representations. Building on work identifying it as a hub for relational mapping, we hypothesized that the hippocampus supports abstract, cross-person representations, and uses shared semantic geometries to do so. We tested this hypothesis by examining hippocampal activity in a remarkable single-neuron dataset derived from conversational speech. Neurons robustly encoded meanings of both spoken and heard words, and used common geometric embeddings for both, leading to abstract meaning performance. Speaker identity was aligned with meaning via partial subspace alignment, which affords speaker-meaning binding by partitioning meaning by speaker while maintaining cross-speaker generalization. Degrees of subspace rotation varied on a single word level and depended systematically on semantic category. Together, these findings indicate how geometric principles allow for abstract cross-personal meanings while preserving binding to speaker identity.

Elucidating the neuronal circuitry that underpins memory formation is critical to understanding how organisms use past experience to guide adaptive behaviour. While memory formation has long been framed as the reactivation of a static ensemble of neurons established during initial learning, growing evidence suggests that memory traces are highly dynamic and undergo substantial reorganisation during consolidation. During the formation of auditory fear memory, initial acquisition is primarily mediated by the basolateral amygdala (BLA), whereas long-term expression relies on the medial prefrontal cortex (mPFC). However, the circuit motifs that coordinate this systemic redistribution remain poorly understood. Here, using targeted anatomical tracing and electrophysiology, we show that the reciprocal connectivity between the mPFC and BLA is organised as a parallel topography along the rostro-caudal axis. Leveraging this novel anatomical understanding of reciprocal communication between the amygdala and prefrontal cortex, we reveal an underlying circuitry mechanism by which fear memory traces are redistributed into subcortical-cortical networks after learning. Using activity-dependent engram capture and optogenetic manipulation, we demonstrate that post-learning engagement of a distinct sub-circuit linking the rostral BLA and rostral mPFC is a hallmark of the consolidated fear memory. These insights reveal that the consolidated engram requires the targeted engagement of a post-learning engram circuit, rather than a simple reactivation of neurons engaged during initial learning.

Frontal cortex neurons are sensory responsive and send long-range feedback to multiple different sensory cortices, but whether these functions are carried out by the same neurons or by distinct modality-specific circuits remains unknown. Using large-scale electrophysiology, two-photon calcium imaging, and viral circuit tracing in awake mice, we identify rich, modality-specific sensory coding in the frontal cortex (secondary motor/anterior cingulate cortex) that is largely dissociated from the neurons providing feedback to sensory cortex. Frontal cortex neurons exhibited robust sensory-evoked activity, with response magnitudes, latencies, and feature selectivity comparable to those observed in primary sensory cortex. Individual neurons displayed tuning for distinct sensory modalities, while population-level activity reliably decoded both sensory modality and stimulus identity. Anatomically, primary auditory (A1) and visual (V1) cortex axons were largely intermingled in anterior frontal cortex but more segregated in posterior regions, revealing spatial variation in the integration of sensory inputs. Frontal cortex neurons responsive to auditory stimuli were biased more anterior compared to visually-responsive neurons. Dual retrograde tracing identified distinct frontal cortex populations projecting back to A1 and V1 that were biased to the posterior and medial extent of the frontal cortex. A1- and V1-projecting frontal neurons were minimally sensory responsive, and no more likely to be responsive than other frontal neurons. Together, these findings reveal a division of labor within the frontal cortex, in which detailed sensory representations and corticocortical feedback arise from partially distinct neuronal populations. This circuit architecture provides a means through which specific sensory information can be transformed within the frontal cortex before being communicated back to the sensory cortex according to behavioral demands.