Neuron

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.

The substantia nigra pars reticulata (SNr), the principal output nucleus of the basal ganglia, is traditionally viewed as a binary gate that permits actions through disinhibition. However, this framework fails to explain the fluid, high-dimensional control required for skilled behavior. Using high-resolution 3D kinematic tracking and automated behavioral classification, we show that SNr population activity paradoxically increases during skilled forelimb reaching and scales with motif-specific kinematics. Systematic optogenetic and chemogenetic perturbations reveal that the SNr bidirectionally controls movement vigor and online kinematics. Remarkably, a transient 12.5 ms pause is sufficient to abort a motor program, while a 12.5 ms burst enhances velocity and reshapes trajectories without altering sequence identity. Calcium imaging at single-neuron resolution further confirms that endogenous SNr dynamics represent real-time 3D kinematics. These findings demonstrate that SNr output does not merely gate movement initiation but continuously regulates the stability and kinematic evolution of skilled actions, expanding the functional framework of basal ganglia output from binary selection to the real-time dynamical control of motor execution. HighlightsO_LISNr activity paradoxically increases and scales with motor motif kinematics C_LIO_LIBidirectional SNr control of action vigor for skilled forelimb reaching C_LIO_LIBrief SNr pauses or bursts reshape online motor execution C_LIO_LIThe SNr acts as a continuous controller for real-time motor execution C_LI

Internal states such as motivation and task engagement influence cognitive functions. Working memory, which maintains information over time, is an essential component of cognition and is modulated by motivation. Here, we show motivational states modulated attractor dynamics that supported working memory. Combining population recordings from mouse medial prefrontal cortex (mPFC) with data-constrained recurrent neural network (RNN) modeling, we found task engagement selectively modulated attractor dynamics within a memory-maintenance subspace, while stimulus-evoked responses remained intact. Reverse-engineering the RNNs revealed that task engagement reorganized the dynamical landscape by stabilizing memory-specific attractors. Specifically, task engagement modulated interactions between neurons to change the attractor dynamics. Finally, gradual changes in behavioral engagement were predicted by continuous modulation of attractor geometry in RNNs and mPFC. Together, these results suggest that internal state modulate working memory function by controlling the dynamical regime of mPFC circuits, providing a mechanistic link between internal state, neural dynamics, and cognitive function.

Many cortical and subcortical regions contribute to complex social behavior; nevertheless, the network level architecture whereby the brain integrates this information to encode appetitive socioemotional behavior remains unknown. Here we measure electrical activity from eight brain regions as mice engage in a social preference assay. We then use machine learning to discover an explainable brain network that encodes the extent to which mice chose to engage another mouse. This socioemotional network is organized by theta oscillations leading from prelimbic cortex and amygdala that converge on ventral tegmental area, and network activity is synchronized with brain-wide cellular firing. The network generalizes, on a mouse-by-mouse basis, to encode socioemotional behaviors in healthy animals, but fails to encode an appetitive socioemotional state in a high confidence genetic mouse model of autism. Thus, our findings reveal the architecture whereby the brain integrates spatially distributed activity across timescales to encode an appetitive socioemotional brain state in health and disease.

Planning is critical for adaptive behaviour in a changing world, because it lets us anticipate the future and adjust our actions accordingly. While prefrontal cortex is crucial for this process, it remains unknown how planning is implemented in neural circuits. Prefrontal representations were recently discovered in simpler sequence memory tasks, where different populations of neurons represent different future time points. We demonstrate that combining such representations with the ubiquitous principle of neural attractor dynamics allows circuits to solve much richer problems including planning. This is achieved by embedding the environment structure directly in synaptic connections to implement an attractor network that infers desirable futures. The resulting spacetime attractor excels at planning in challenging tasks known to depend on prefrontal cortex. Recurrent neural networks trained by gradient descent on such tasks learn a solution that precisely recapitulates the spacetime attractor - in representation, in dynamics, and in connectivity. Analyses of networks trained across different environment structures reveal a generalisation mechanism that rapidly reconfigures the world model used for planning, without the need for synaptic plasticity. The spacetime attractor is a testable mechanistic theory of planning. If true, it would provide a path towards detailed mechanistic understanding of how prefrontal cortex structures adaptive behaviour.

Exploration allows animals to gather information and adapt to changing conditions. Yet, it also exposes them to potential threats, requiring neural systems that weigh uncertainty and regulate behavioral transitions between cautious and exploratory states. These computations are distributed across cortical and subcortical networks, including the basal ganglia, which integrate sensory, motivational, and contextual information to shape action-selection behaviors. Within this circuitry, the globus pallidus externa (GPe) occupies a central but underappreciated role. Once viewed as a relay between striatum and downstream nuclei, the GPe is gaining recognition as a dynamic regulator that integrates diverse inputs and exerts bidirectional control over motor and cognitive processes. Here, we examine arkypallidal NPAS1-expressing GPe (GPeNPAS1) neurons, which form preferential inhibitory projections to the striatal matrix. Chemogenetic manipulations and in vivo calcium measurement reveal that GPeNPAS1 activity modulates and encodes risk-taking behavior sequences, identifying a circuit mechanism by which the GPe can regulate adaptive decision-making in risky contexts.

Membrane voltage (Vm) regulates spike timing and intracellular signaling. While Vm is extensively modulated by behavior, it is unclear how subthreshold Vm dynamics engage intracellular signaling in the awake mammalian brain. We developed a bicistronic viral vector to express genetically encoded and color compatible voltage and calcium (Ca2+) indicators in the same neuron, and simultaneously recorded cellular Vm and Ca2+ dynamics in awake mice. We report that prolonged subthreshold Vm depolarization is closely accompanied by prominent large amplitude Ca2+ elevation, whereas isolated spikes are coupled with weak Ca2+ rise. Additionally, individual spikes differentially engage intracellular Ca2+ dynamics depending on post-spiking Vm depolarization, consistent with a prominent role of slow Vm depolarization in regulating cellular signaling. While brief intracranial electrical stimulation consistently leads to Vm depolarization and Ca2+ increase, longer stimulation disrupts Vm and Ca2+ coupling, highlighting a tightly regulated cellular mechanism that relays slow Vm depolarization to intracellular signaling. One-Sentence SummaryProlonged membrane depolarization engages cytosolic calcium.

Cues that predict reward can acquire strong power to bias orientation and approach, directing seeking behaviors into close proximity with reward. The parafascicular thalamus (PF), part of the intralaminar complex, is well-placed as a neural hub for integrating subcortical sensory and attentional signals to generate actions that support cue-guided behavior. Neurons in the PF respond to sensory cues and encode features of head position, but little is known about how the PF is engaged in vivo during learning. Here, we recorded calcium activity in PF neurons using fiber photometry throughout a pavlovian conditioning task. We found that PF neurons developed sustained cue-evoked responses that scaled with associative learning and diminished with extinction. PF neurons preferentially signaled cue-directed orientations and body movements made during the cue, and their activity paused during reward consumption. Using optogenetics, we found that stimulation of PF neurons disrupted normal cue-directed orientation and impaired associative learning by promoting exaggerated ipsilateral turning behavior. Broadly, these results suggest an encoding profile by which the PF can support cue and reward approach behavior via dynamic regulation of head and body position. Together, our data demonstrate a thalamic region that is important for steering attention to facilitate cue-reward learning.

The dorsal anterior midline thalamus (aMT) consists of several closely packed nuclei that are important for motivated and emotional behavior. Previous work on aMT has focused on cells and synapses in the paraventricular thalamus (PVT), and little is known about the adjacent paratenial thalamus (PT). Here we examine neural circuits involving PT using a combination of molecular profiling, anatomical tracing, electrophysiology, and optogenetics. We first find that Protein Kinase C-delta (PKCd) selectively labels thalamocortical (TC) cells concentrated in PT but largely absent from neighboring PVT. We show that TC cells in PT project to the infralimbic region (IL) of the medial prefrontal cortex (mPFC), where they contact and drive L2/3 pyramidal cells. In return, we find that IL mPFC primarily projects to PT over nearby PVT, making connections onto reciprocally connected TC cells. However, these cortical inputs are even stronger onto thalamostriatal (TS) and thalamoamygdala (TA) cells, allowing the mPFC to broadcast to the subcortex. Together, our findings help to parcellate aMT, highlight PT as a distinct thalamic nucleus, establish reciprocal connectivity between PT and IL mPFC, and show cortico-thalamic throughput to the subcortex.

The thalamus engages in attention by amplifying relevant signals and filtering distractors. Whether architectural features of thalamic circuitry offer a unique locus for attentional control is unknown. We developed a circuit model of excitatory thalamocortical and inhibitory reticular neurons, capturing key observations from task-engaged animals. We found that top-down inputs onto reticular neurons regulate thalamic gain effectively, compared to direct thalamocortical inputs. This mechanism enhances downstream readout, improving detection, discrimination, and cross-modal performance. The model revealed heterogeneous thalamic responses that enable decoding top-down versus bottom-up signals. Spiking activity from task-performing mice supported model predictions, with a similar coding geometry in auditory thalamus and readout strategy in auditory cortex. Dynamical systems analysis explained why reticular neurons are potent sites for control, and how lack of excitatory connectivity among thalamocortical neurons enables separation of top-down from bottom-up signals. Our work reveals mechanisms for attentional control and connects circuit architectures to computational functions.

Beliefs about states of the world profoundly impact decision-making and learning, but little is known about how neural circuits represent and update beliefs. We performed projection-specific recordings and perturbations from neurons in the orbitofrontal cortex (OFC) projecting to the intermediate or rostral caudate putamen (CPi/CPr) in rats performing a task with hidden reward states. Stimulating OFC[->]CPi neurons biased rats beliefs towards high reward states. Recordings from optogenetically-tagged OFC[->]CPi neurons showed that they encoded evidence for high reward states, and evidence encoding was shaped by local inhibition. Finally, projection-specific perturbations disrupted encoding of hidden states within OFC via long-range cortico-basal ganglia-thalamic loops. These findings reveal the circuit implementation of a core cognitive computation, updating subjective beliefs about abstract latent states of the environment.

Flexible decision-making relies on interactions between frontal cortex and subcortical structures. The claustrum, a subcortical nucleus highly interconnected with frontal cortex, influences cortical activity and has been implicated in cognitive functions. Recording from claustrum neurons as mice performed a reinforcement learning task, we found that the activity of almost half of recorded neurons scaled with reward rate and predicted trial- by-trial adjustments in reaction time and choice switching. Individual neurons sustained this activity over seconds between trials. Our recordings identified two electrophysiologically distinct populations. One was excited during task execution and bidirectionally scaled its activity with reward rate. The other was suppressed during task execution, scaled activity inversely with reward rate and projected to frontal cortex, indicating that claustrocortical outputs produce graded increases in activity with decreasing reward rate. Our results identify the claustrum as a subcortical locus for stable value representations and integrate it into neuronal circuits for value-based decision-making.

Sensory neuron spiking responses vary across repeated presentations of the same stimuli, but whether this trial-to-trial variability represents noise versus unidentified signals remains unresolved. Some of the variability can be attributed to correlations between neural activity and arousal, locomotion, and other overt movements. We hypothesized that correlations with global activity factors, i.e., patterns of neural activity observable in other brain regions, may explain additional variability in spike count responses of visual cortical neurons. To test this, we used Neuropixels 2.0 probes to record neural activity in mouse primary visual cortex (V1) while subjects passively viewed images. We recorded videos of behavior alongside neural activity from other brain regions, either spiking activity of neural populations in anterior cingulate area (ACA) or widefield calcium signals from across the dorsal cortex. We then used a model based on reduced rank regression to partition the explainable variability of visual cortical responses by source. Some of the trial-to-trial variability in V1 spike counts was attributable to locally shared patterns of activity uncorrelated with either behavior or global activity patterns. Locally shared activity patterns explained trial-to-trial variability that was in excess of Poisson spike generation. Of the parts of variability attributable to non-local sources, global cortical activity predicted significantly more V1 spike count variability than behavioral factors. Additionally, behavioral factors explained little variability uniquely and comprised a geometric subspace of the globally predictable V1 activity. Finally, optogenetically perturbing ACA directly impacted V1 activity, and ACA activity patterns predicted V1 spike count variability even on trials without overt behaviors. Our data indicate that globally shared factors from other cortical areas contribute substantially to shared spike count variability in V1, with only a minority of shared variability confined to local V1 circuits.

Humans and animals plan actions to achieve goals in worlds that are complex and continually changing. While planning is critically dependent on the prefrontal cortex in humans, little is known about its cellular underpinnings. Mechanistic understanding has been limited by a scarcity of controlled animal experiments in which subjects must flexibly plan novel behaviours on every trial. Here we characterise the neural representations and dynamics of mouse medial frontal cortex (mFC) during flexible navigation in structured environments. We trained mice to navigate complex mazes, to goals that changed location on every trial. Optogenetic silencing established that mFC was necessary for efficient navigation. mFC activity was dominated by two factorised components: (i) a structured representation of subjects position within the maze that formed an efficient code for behavioural trajectories, and (ii) a flexible representation of the shortest path-distance to the current goal. Both representations oscillated within local field potential (LFP) theta cycles, processing from further to closer to the goal at a systematic offset. These data suggest a computation in which mFC evaluates possible futures by their distance-to-goal to update a structured behavioural policy.

Naturalistic, goal directed behavior often requires continuous actions directed at dynamically changing goals. In this context, the closest analogue to choice is a strategic reweighting of multiple goal-specific control policies in response to shifting environmental pressures. To understand the algorithmic and neural bases of choice in continuous contexts, we examined behavior and brain activity in humans performing a continuous prey-pursuit task. Using a newly developed control-theoretic decomposition of behavior, we find pursuit strategies are well described by a meta-controller dictating a mixture of lower-level controllers, each linked to specific pursuit goals. We find that hippocampal neurons encode the policy blending variable in a value-invariant manner and monitor policy switches after they occur. ACC neurons encode policy switches in a value-dependent manner, with value related modulation detectable several hundred ms before the switch, alongside a ramping increase in mean firing rate toward the switch. Meanwhile, OFC activity is consistent with an encoding of the current value structure of the task, rather than policy switching. Together these results are consistent with a tripartite functional division in which hippocampus serves as a controller over behavior, ACC serves as a meta-controller, and OFC provides a value context signal. Overall, our results shed light onto the complex processes associated with choice during naturalistic continuous interactive behavior.

Escape from threat is one of the most ancient and conserved sensorimotor transformations and must satisfy two competing demands. It must be fast and reliable, because failing to escape genuine threats risks death, yet also selective, because escaping indiscriminately costs energy and missed resources. Speed and reliability favour a system with a low threshold that responds to the slightest indication of danger, whereas selectivity demands a high threshold that filters out innocuous stimuli - yet both must be achieved simultaneously. While previous work has identified mechanisms for implementing selectivity, it is not known how the mammalian brain achieves speed and reliability once genuine threats have been identified. Here we use whole-cell recordings from dorsal periaqueductal gray (dPAG) neurons in behaving mice to show that the escape circuit resolves this tension via high intrinsic excitability at the single cell level. We find that while the membrane potential of dPAG neurons does not reach action potential threshold during exploratory behaviour, they require only a small amount of current to fire. Threat stimuli cause a sparse increase in synaptic input rate that, because of the high excitability, produces a sustained depolarizing voltage step that drives spiking and escape behaviour. Individual dPAG neurons respond similarly on escape and non-escape trials - what determines escape is the fraction of the dPAG population that is recruited, a finding we confirm with single-unit recordings in freely moving mice. The membrane voltage step response is also invariant to threat type, in contrast to superior colliculus neurons where membrane potential dynamics reflect stimulus identity. These findings reveal a single neuron mechanism for fast and reliable escape, in which the high intrinsic excitability of dPAG neurons transforms sparse, threat-evoked synaptic input into a rapid and stereotyped voltage signal, converting diverse threat representations into a uniform escape command.

The mammalian basal ganglia (BG) orchestrate motor, cognitive, and affective functions, yet cell type-specific genetic access remains limited, especially beyond rodents. Key structures implicated in movement and psychiatric disorders, including pallidum, subthalamic nucleus, and dopaminergic midbrain, lack scalable tools for cross-species targeting. Here, we present a comprehensive enhancer-AAV library enabling selective labeling and manipulation of major BG neuronal populations: striatal projection neuron subtypes, pallidal and subthalamic neurons, and midbrain dopaminergic and GABAergic populations. Using an evolutionarily informed discovery pipeline, we identified enhancers targeting canonical, non-canonical, and disease-relevant cell types, with validation demonstrating robust cross-species conservation of specificity between mouse and macaque. Computational modeling revealed sequence features predictive of in vivo performance, including motif grammar, chromatin accessibility, and evolutionary conservation, and identified distinct regulatory architectures across glial, projection, and interneuron lineages. This work establishes a comprehensive cross-species viral toolkit for the BG, unlocking previously inaccessible cell types for circuit dissection.

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.

Humans solve problems adaptively by selecting strategies suited to the situation. For example, when missing the bus to an appointment, we may wait for the next bus, call a taxi, cancel, or reschedule depending on the circumstances. Yet the neural and computational principles that support such flexible problem solving remain poorly understood. To address this question, we designed a moderately complex decision task for monkeys that allows multiple plausible solution strategies. Animals learned the task rapidly, generalized to novel maze geometries, and their choices were inconsistent with any single fixed strategy. We then recorded large-scale neural activity from the frontal cortex and found that population dynamics varied systematically with maze geometry. Neural responses clustered into two distinct dynamical regimes with separable initial states, consistent with hierarchical and sequential strategies. A decoder trained on population activity revealed time-resolved decision dynamics that aligned with these regimes, and an unsupervised latent-space analysis provided convergent evidence that strategy use varied across trials. A behavioral model grounded in neurally inferred strategies accounted for choices better than fixed-strategy alternatives and captured trial-by-trial variability. Together, these results provide a neural and computational account of how the brain selects and implements distinct strategies during adaptive problem solving.

Learning operates across different brain circuits to associate population activity patterns with desired outcomes, and to enable volitional reactivation of those patterns to control behavior. These circuits differ profoundly in their architecture and dynamical regimes, yet which features of learning are shared across them and which arise from circuit-specific implementations remains unknown. Here, we use a brain-computer interface (BCI) to train mice to modulate the activity of selected neuronal ensembles toward configurations that trigger reward delivery. By making reward delivery contingent directly on population activity, we impose an identical associative learning problem on two circuits with distinct dynamical regimes: the primary motor cortex (M1) and the hippocampal area CA3. Mice acquired robust volitional control in both regions, and learning produced a set of shared signatures across circuits, including modulation of reward-controlling neurons, network-level sparsification, and greater exploration of reward-related activity patterns. These signatures were underpinned by distinct population dynamics: M1 activity flowed continuously through reward-associated states, whereas CA3 activity traced approach-and-return dynamics around them. Recurrent network models endowed with distinct minimal connectivity constraints chosen to reflect the dominant dynamical regime associated with each region captured key features of these shared signatures and region-specific dynamics, indicating that local architectural constraints are sufficient to account for the distinct implementations of learning. These findings indicate that equivalent learning outcomes arise from divergent dynamical implementations across architecturally distinct circuits. This principled degeneracy reveals that learning is not a single canonical solution, but is implemented through multiple circuit-specific mechanisms shaped by local network architecture.