Neuron

Humans are remarkable at adapting to changing norms, yet we know little about how the human brain encodes these social signals at the single neuron level. Here, we recorded the activity of single neurons in the substantia nigra and globus pallidus in neurosurgical participants as they played an iterative ultimatum game versus human and computer avatars. Using computational modeling of behavior, we found that putative dopaminergic units in the substantia nigra tracked the valence of norm prediction errors, with greater encoding during gameplay with human versus computer avatars. Analogous analyses in the globus pallidus, a nucleus adjacent to the substantia nigra, found no significant effect. Our results identify a novel role for the substantia nigra in encoding social learning signals.

Decisions often involve trade-offs between benefits and costs, such as a small payment now vs. a larger payment after a delay. Although decision variables are distributed across frontal cortex, how these regions transform competing attributes into a choice remains unclear. Combining large-scale electrophysiology with causal perturbations as mice chose between different reward amounts and delays, we found that population geometry of decision variables differed across regions, reflecting distinct computations. Dorsal frontal areas, including motor cortex, collapsed competing attributes onto a decision axis shared across reward-delay contexts, with projection amplitude predicting choice probability; silencing these areas impaired action selection regardless of delay. In contrast, ventral prefrontal cortex (vPFC) represented choice in higher-dimensional subspaces organized by reward delay, whose geometry predicted how strongly delay influenced choice. Notably, inhibiting vPFC preserved the dorsal decision representation but selectively removed its modulation by reward delay, causing animals to behave as if delay no longer influenced choice. vPFC inhibition had no effect when mice chose between reward amounts. Frontal cortex therefore solves multi-attribute decisions through a generator-modulator architecture: dorsal frontal circuits maintain a shared choice axis guiding decisions across contexts, whereas vPFC maintains competing attributes in distinct subspaces that modulate decisions when options conflict.

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.

Circulating RNA can capture molecular variation associated with amyotrophic lateral sclerosis (ALS) progression, but biofluid heterogeneity makes biologically organized signals difficult to recover. Here, we paired microRNA (miRNA) and messenger RNA (mRNA) profiles from glutamate-aspartate transporter (GLAST)-positive small extracellular-vesicles (sEVs) to estimate survival time and identify survival-associated programs. In 45 participants with ALS and 15 controls, biologically constrained multi-omic factor analysis identified a program in which reciprocal miRNA-mRNA states among target-supported pairs tracked survival. The miRNA arm was then evaluated in an independent total-plasma cohort of 248 participants with ALS, refining a five-miRNA panel that added prognostic information beyond functional decline and neurofilament light chain, particularly over longer survival horizons. The mRNA arm was evaluated across 586 cortical profiles from 308 donors and 527,261 nuclei from 69 donors, identifying a five-gene core associated with glial reactivity, vascular programs and reduced myelinating identity. Together, these findings establish a framework for integrating regulatory RNA layers in circulation to identify clinically relevant programs and relate them to disease-relevant cellular states.

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.

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.

Primates are deeply social, but the underlying neural basis has been elusive because brain activity is often recorded in artificially constrained conditions. Here, we recorded wireless neural activity simultaneously from two macaque monkeys interacting socially in a natural setting. Neural activity in each monkey encoded key social variables such as allogrooming state, partner identity and joint movements. Surprisingly, neurons in the high-level visual cortex of each monkey continuously tracked a social favor signal, i.e. net duration of allogrooming given versus received, providing a neural basis for grooming reciprocity. In both monkeys, while grooming their partner, neural activity was predicted better by the partners joints and his brain activity. Thus, the receiver of grooming drove the social interaction, not the groomer. Taken together, our findings elucidate the rich and dynamic neural basis of primate social interactions in an ecologically valid setting.

Phasic ensemble bursting in subcortical neuromodulatory systems such as the basal forebrain (BF) provides a powerful format for broadcasting reward-prediction signals. These bursts encode reward prediction by varying population amplitude, yet their internal structure, reflected in cell-to-cell amplitude relationships, remains conserved. How neural circuits reconcile these competing constraints remains unknown. Here we show that BF bursting is generated by a dynamical attractor: burst structure is imposed not by feedforward inputs but by recurrent circuit dynamics, while burst amplitude is determined by approximately linear integration across concurrent inputs. Optogenetic activation of BF neurons expressing natriuretic peptide receptor 3 (Npr3) outside the behavioral task reproduced the low-dimensional dynamics of natural cue-evoked bursting, preserving the temporal envelope and cell-to-cell amplitude relationships across stimulation patterns. Critically, unilateral activation of non-overlapping Npr3 populations in either hemisphere recruited the full bilateral ensemble into the same native trajectory, showing that restricted inputs can nevertheless recruit the full ensemble. Matched ipsilateral and contralateral onset latencies exceeding 25 ms implicated recurrent interactions beyond the BF. Despite the conserved structure, population burst amplitude varied with stimulation strength and summed approximately linearly across concurrent unilateral inputs. This linear summation extended to natural sensory input during behavior: adding Npr3 stimulation to auditory cues increased cue-evoked burst amplitude, improved detection, and reduced subsequent reward responses, consistent with stronger reward prediction producing smaller reward prediction errors. Thus, a low-dimensional dynamical attractor preserves a shared BF burst structure across diverse inputs while integrating concurrent inputs into a scalar burst amplitude that encodes reward prediction.

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.

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.

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.

Phasic reward prediction signals in ventral tegmental area (VTA) dopamine (DA) neurons and basal forebrain (BF) noncholinergic bursting neurons are remarkably similar despite the distinct circuit organizations and functions of the two systems. Whether this similarity reflects independent computation within each system, inheritance from common upstream circuitry, or causal propagation from one system to the other remains unknown. Here we show that reward prediction signals are tightly coordinated across these two systems, with VTA DA activity causally driving BF bursting neurons on a millisecond timescale. Simultaneous recordings from freely moving rats revealed strong coupling between putative DA neurons and BF bursting neurons on individual trials, both in response amplitude and spike timing. DA activity consistently preceded BF bursting neuron activity by approximately 10 ms across well-trained behavior, learning, and even outside overt task events. In DAT-Cre mice, optogenetic activation of VTA DA neurons rapidly recruited BF bursting neurons at latencies matching those observed during natural DA-BF coupling, while activation of their BF terminals produced responses at still shorter latencies. Conversely, inhibition of DA neurons or their BF terminals suppressed both cue-evoked and, notably, ongoing activity in a substantial subset of BF bursting neurons. Fast DA-evoked BF recruitment persisted after D1/D2 receptor blockade, and anatomical tracing identified glutamatergic DA-to-BF synapses, supporting glutamate co-release as a candidate substrate. Together, these findings reveal a previously unrecognized causal link that enables rapid propagation of reward prediction signals between functionally distinct subcortical systems.

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.

Neuromodulatory systems project broadly, yet computations engage specific neuronal populations. Whether neuromodulation can selectively influence task-engaged neurons remains unclear. During goal-directed navigation, neurons in the locus coeruleus (LC), the brain's principal norepinephrine source, responded at navigation onset. Concurrently, dopamine transients arose in micrometer-scale domains around LC axons in CA1. These transients preferentially enhanced nearby CA1 neurons with ramping dynamics, whose higher activity predicted later reward-anticipatory action. Brief LC activation evoked local dopamine, enhanced ramping dynamics, and delayed action initiation seconds later. Blocking D1-like, but not adrenergic, receptors weakened ramping dynamics and impaired reward anticipation. Modeling showed that selectivity can emerge when localized dopamine coincides with strong ongoing neuronal activity, without dedicated wiring. Thus, a widely projecting system can selectively modulate task-engaged neurons and shape behavior.

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.

Arousal is essential for survival, and maladaptive arousal processing leads to an inability to focus, anxiety-like behavior, and dysregulated affective states. Norepinephrine (NE) is known to regulate anxiety, arousal, and learning through locus coeruleus (LC) projections throughout the brain. Evidence for co-release of the NE precursor and neurotransmitter dopamine (DA) from LC neurons has been accumulating for years, yet definitive measures of DA release across regions, stimulus paradigms, and behaviors associated with the LC-NE system remain controversial. Here, we identified the physiological and behavioral properties that evoke DA release from LC axon terminals. Using concomitant approaches, we inhibited the LC and ventral tegmental area (VTA) to selectively isolate the contributions of LC-derived DA release. Together these findings establish the constraints by which LC neurons release DA in a modality-dependent manner.

Animals learn to select actions that maximize reward in complex environments. A central challenge in this process is credit routing: behavioral outcomes must be routed back to the neural populations that contributed to generating the relevant actions. One possible route is through thalamocortical pathways conveying learning signals from the basal ganglia and cerebellum, where plasticity is shaped by reward-prediction errors. However, how these pathways route such signals back to specific cortical circuits remains unclear. To address this question, we combined anatomical circuit mapping in mice with closed-loop circuit modeling. Anterograde transsynaptic tracing revealed that thalamic neurons receiving input from the substantia nigra pars reticulata or the lateral cerebellar nucleus were largely segregated, with partial overlap in the ventromedial thalamus. Nevertheless, their cortical axons shared a pronounced enrichment in layer 1a, most prominently in secondary motor cortex. In a brain- wide synergistic loop-circuit model, learning was enhanced when both pathways delivered additive and gain-modulating feedback aligned with the cortical action readout. Local activity- based calibration established this alignment, whereas dendritic-spike- and reward-prediction- error-dependent thalamocortical plasticity further refined behavioral output. Module-specific feedback was essential for a brain-machine interface task requiring the simultaneous conditioning of multiple target neurons. Together, these findings define a credit-routing architecture in which laminar convergence, plastic alignment, and modular feedback link parallel subcortical learning systems to cortical action representations.

Successful navigation requires the integration of self-motion signals with spatial representations, yet the circuits that provide velocity information necessary for stable grid cell firing remain incompletely understood. Here, we identify medial septal (MS) glutamatergic neurons as a critical component of this pathway. Using cell-type-specific optogenetic silencing combined with in vivo electrophysiology in freely moving mice, we show that MS glutamatergic neurons support the fidelity of firing rate-based speed coding in the medial entorhinal cortex (MEC). Silencing this population reduced grid cell spatial periodicity and stability and produced distortions of grid firing fields. Silencing also reduced theta phase-locking strength in strongly theta-modulated MEC neurons. Model simulations of a hybrid oscillatory interference-continuous attractor network with reductions in speed-signal fidelity disrupted grid periodicity and reproduced features of the spatial distortions observed in vivo. Together, these findings identify a septo-entorhinal circuit that coordinates self-motion information with the spatial and temporal organization of MEC activity to maintain a stable neural representation of space.