Opponent control of behavior by dorsomedial striatal pathways depends on task demands and internal state

A Comparison of Rapid Rule-Learning Strategies in Humans and Monkeys

Interspecies comparisons are key to deriving an understanding of the behavioral and neural correlates of human cognition from animal models. We perform a detailed comparison of the strategies of female macaque monkeys to male and female humans on a variant of the Wisconsin Card Sorting Test (WCST), a widely studied and applied task that provides a multiattribute measure of cognitive function and depends on the frontal lobe. WCST performance requires the inference of a rule change given ambiguous feedback. We found that well-trained monkeys infer new rules three times more slowly than minimally instructed humans. Input-dependent hidden Markov model-generalized linear models were fit to their choices, revealing hidden states akin to feature-based attention in both species. Decision processes resembled a win-stay, lose-shift strategy with interspecies similarities as well as key differences. Monkeys and humans both test multiple rule hypotheses over a series of rule-search trials and perform inference-like computations to exclude candidate choice options. We quantitatively show that perseveration, random exploration, and poor sensitivity to negative feedback account for the slower task-switching performance in monkeys.

DART.2: bidirectional synaptic pharmacology with thousandfold cellular specificity

Precision pharmacology aims to manipulate specific cellular interactions within complex tissues. In this pursuit, we introduce DART.2 (drug acutely restricted by tethering), a second-generation cell-specific pharmacology technology. The core advance is optimized cellular specificity-up to 3,000-fold in 15 min-enabling the targeted delivery of even epileptogenic drugs without off-target effects. Additionally, we introduce brain-wide dosing methods as an alternative to local cannulation and tracer reagents for brain-wide dose quantification. We describe four pharmaceuticals-two that antagonize excitatory and inhibitory postsynaptic receptors, and two that allosterically potentiate these receptors. Their versatility is showcased across multiple mouse-brain regions, including cerebellum, striatum, visual cortex and retina. Finally, in the ventral tegmental area, we find that blocking inhibitory inputs to dopamine neurons accelerates locomotion, contrasting with previous optogenetic and pharmacological findings. Beyond enabling the bidirectional perturbation of chemical synapses, these reagents offer intersectional precision-between genetically defined postsynaptic cells and neurotransmitter-defined presynaptic partners.

Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network

Objective.Distributed hypothalamic-midbrain neural circuits help orchestrate complex behavioral responses during social interactions. Given rapid advances in optical imaging, it is a fundamental question how population-averaged neural activity measured by multi-fiber photometry (MFP) for calcium fluorescence signals correlates with social behaviors is a fundamental question. This paper aims to investigate the correspondence between MFP data and social behaviors.Approach:We propose a state-space analysis framework to characterize mouse MFP data based on dynamic latent variable models, which include a continuous-state linear dynamical system and a discrete-state hidden semi-Markov model. We validate these models on extensive MFP recordings during aggressive and mating behaviors in male-male and male-female interactions, respectively.Main results:Our results show that these models are capable of capturing both temporal behavioral structure and associated neural states, and produce interpretable latent states. Our approach is also validated in computer simulations in the presence of known ground truth.Significance:Overall, these analysis approaches provide a state-space framework to examine neural dynamics underlying social behaviors and reveals mechanistic insights into the relevant networks.

Large-scale foundation models and generative AI for BigData neuroscience

Recent advances in machine learning have led to revolutionary breakthroughs in computer games, image and natural language understanding, and scientific discovery. Foundation models and large-scale language models (LLMs) have recently achieved human-like intelligence thanks to BigData. With the help of self-supervised learning (SSL) and transfer learning, these models may potentially reshape the landscapes of neuroscience research and make a significant impact on the future. Here we present a mini-review on recent advances in foundation models and generative AI models as well as their applications in neuroscience, including natural language and speech, semantic memory, brain-machine interfaces (BMIs), and data augmentation. We argue that this paradigm-shift framework will open new avenues for many neuroscience research directions and discuss the accompanying challenges and opportunities.

Mixed representations of choice and outcome by GABA/glutamate cotransmitting neurons in the entopeduncular nucleus

The basal ganglia (BG) are an evolutionarily conserved and phylogenetically old set of sub-cortical nuclei that guide action selection, evaluation, and reinforcement. The entopeduncular nucleus (EP) is a major BG output nucleus that contains a population of GABA/glutamate cotransmitting neurons (EP Sst+ ) that specifically target the lateral habenula (LHb) and whose function in behavior remains mysterious. Here we use a probabilistic switching task that requires an animal to maintain flexible relationships between action selection and evaluation to examine when and how GABA/glutamate cotransmitting neurons contribute to behavior. We find that EP Sst+ neurons are strongly engaged during this task and show bidirectional changes in activity during the choice and outcome periods of a trial. We then tested the effects of either permanently blocking cotransmission or modifying the GABA/glutamate ratio on behavior in well-trained animals. Neither manipulation produced detectable changes in behavior despite significant changes in synaptic transmission in the LHb, demonstrating that the outputs of these neurons are not required for on-going action-outcome updating in a probabilistic switching task.

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.

Basal ganglia-spinal cord pathway that commands locomotor gait asymmetries in mice

The basal ganglia are essential for executing motor actions. How the basal ganglia engage spinal motor networks has remained elusive. Medullary Chx10 gigantocellular (Gi) neurons are required for turning gait programs, suggesting that turning gaits organized by the basal ganglia are executed via this descending pathway. Performing deep brainstem recordings of Chx10 Gi Ca2+ activity in adult mice, we show that striatal projection neurons initiate turning gaits via a dominant crossed pathway to Chx10 Gi neurons on the contralateral side. Using intersectional viral tracing and cell-type-specific modulation, we uncover the principal basal ganglia-spinal cord pathway for locomotor asymmetries in mice: basal ganglia → pontine reticular nucleus, oral part (PnO) → Chx10 Gi → spinal cord. Modulating the restricted PnO → Chx10 Gi pathway restores turning competence upon striatal damage, suggesting that dysfunction of this pathway may contribute to debilitating turning deficits observed in Parkinson's disease. Our results reveal the stratified circuit architecture underlying a critical motor program.

Chronic Ethanol Exposure Produces Persistent Impairment in Cognitive Flexibility and Decision Signals in the Striatum

Lack of cognitive flexibility is a hallmark of substance use disorders and has been associated with drug-induced synaptic plasticity in the dorsomedial striatum (DMS). Yet the possible impact of altered plasticity on real-time striatal neural dynamics during decision-making is unclear. Here, we identified persistent impairments induced by chronic ethanol (EtOH) exposure on cognitive flexibility and striatal decision signals. After a substantial withdrawal period from prior EtOH vapor exposure, male, but not female, rats exhibited reduced adaptability and exploratory behavior during a dynamic decision-making task. Reinforcement learning models showed that prior EtOH exposure enhanced learning from rewards over omissions. Notably, neural signals in the DMS related to the decision outcome were enhanced, while those related to choice and choice-outcome conjunction were reduced, in EtOH-treated rats compared to the controls. These findings highlight the profound impact of chronic EtOH exposure on adaptive decision-making, pinpointing specific changes in striatal representations of actions and outcomes as underlying mechanisms for cognitive deficits.

Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learning

Different brain systems have been hypothesized to subserve multiple "experts" that compete to generate behavior. In reinforcement learning, two general processes, one model-free (MF) and one model-based (MB), are often modeled as a mixture of agents (MoA) and hypothesized to capture differences between automaticity vs. deliberation. However, shifts in strategy cannot be captured by a static MoA. To investigate such dynamics, we present the mixture-of-agents hidden Markov model (MoA-HMM), which simultaneously learns inferred action values from a set of agents and the temporal dynamics of underlying "hidden" states that capture shifts in agent contributions over time. Applying this model to a multi-step,reward-guided task in rats reveals a progression of within-session strategies: a shift from initial MB exploration to MB exploitation, and finally to reduced engagement. The inferred states predict changes in both response time and OFC neural encoding during the task, suggesting that these states are capturing real shifts in dynamics.

Active Learning for Discrete Latent Variable Models

Active learning seeks to reduce the amount of data required to fit the parameters of a model, thus forming an important class of techniques in modern machine learning. However, past work on active learning has largely overlooked latent variable models, which play a vital role in neuroscience, psychology, and a variety of other engineering and scientific disciplines. Here we address this gap by proposing a novel framework for maximum-mutual-information input selection for discrete latent variable regression models. We first apply our method to a class of models known as mixtures of linear regressions (MLR). While it is well known that active learning confers no advantage for linear-gaussian regression models, we use Fisher information to show analytically that active learning can nevertheless achieve large gains for mixtures of such models, and we validate this improvement using both simulations and real-world data. We then consider a powerful class of temporally structured latent variable models given by a hidden Markov model (HMM) with generalized linear model (GLM) observations, which has recently been used to identify discrete states from animal decision-making data. We show that our method substantially reduces the amount of data needed to fit GLM-HMMs and outperforms a variety of approximate methods based on variational and amortized inference. Infomax learning for latent variable models thus offers a powerful approach for characterizing temporally structured latent states, with a wide variety of applications in neuroscience and beyond.

Trial-history biases in evidence accumulation can give rise to apparent lapses in decision-making

Trial history biases and lapses are two of the most common suboptimalities observed during perceptual decision-making. These suboptimalities are routinely assumed to arise from distinct processes. However, previous work has suggested that they covary in their prevalence and that their proposed neural substrates overlap. Here we demonstrate that during decision-making, history biases and apparent lapses can both arise from a common cognitive process that is optimal under mistaken beliefs that the world is changing i.e. nonstationary. This corresponds to an accumulation-to-bound model with history-dependent updates to the initial state of the accumulator. We test our model's predictions about the relative prevalence of history biases and lapses, and show that they are robustly borne out in two distinct decision-making datasets of male rats, including data from a novel reaction time task. Our model improves the ability to precisely predict decision-making dynamics within and across trials, by positing a process through which agents can generate quasi-stochastic choices.

Neural circuit models for evidence accumulation through choice-selective sequences

Decision making is traditionally thought to be mediated by populations of neurons whose firing rates persistently accumulate evidence across time. However, recent decision-making experiments in rodents have observed neurons across the brain that fire sequentially as a function of spatial position or time, rather than persistently, with the subset of neurons in the sequence depending on the animal's choice. We develop two new candidate circuit models, in which evidence is encoded either in the relative firing rates of two competing chains of neurons or in the network location of a stereotyped pattern ("bump") of neural activity. Encoded evidence is then faithfully transferred between neuronal populations representing different positions or times. Neural recordings from four different brain regions during a decision-making task showed that, during the evidence accumulation period, different brain regions displayed tuning curves consistent with different candidate models for evidence accumulation. This work provides mechanistic models and potential neural substrates for how graded-value information may be precisely accumulated within and transferred between neural populations, a set of computations fundamental to many cognitive operations.

Striatum supports fast learning but not memory recall

Animals learn to carry out motor actions in specific sensory contexts to achieve goals. The striatum has been implicated in producing sensory-motor associations, yet its contribution to memory formation or recall is not clear. To investigate the contribution of striatum to these processes, mice were taught to associate a cue, consisting of optogenetic activation of striatum-projecting neurons in visual cortex, with forelimb reaches to access food pellets. As necessary to direct learning, striatal neural activity encoded both the sensory context and outcome of reaching. With training, the rate of cued reaching increased, but brief optogenetic inhibition of striatal activity arrested learning and prevented trial-to-trial improvements in performance. However, the same manipulation did not affect performance improvements already consolidated into short- (within an hour) or long-term (across days) memories. Hence, striatal activity is necessary for trial-to-trial improvements in task performance, leading to plasticity in other brain areas that mediate memory recall.

Activity in the Dorsomedial Striatum Underlies Serial Reversal Learning Performance Under Probabilistic Uncertainty

Background

Corticostriatal circuits, particularly the dorsomedial striatum (DMS) and lateral orbitofrontal cortex, are critical for navigating reversal learning under probabilistic uncertainty. These same areas are implicated in the reversal learning impairments observed in individuals with psychosis as well as their psychotic symptoms, suggesting that they may share a common neurobiological substrate. To address this question, we used psychostimulant exposure and specific activation of the DMS during reversal learning in mice to assess corticostriatal activity.

Methods

We used amphetamine treatment to induce psychosis-relevant neurobiology in male mice during reversal learning and to examine pathway-specific corticostriatal activation. To determine the causal role of DMS activity, we used chemogenetics to drive midbrain inputs during a range of probabilistic contingencies.

Results

Mice treated with amphetamine showed altered punishment learning, which was associated with decreased shifting after losses and increased perseverative errors after reversals. Reversal learning performance and strategies were dependent on increased activity in lateral orbitofrontal cortex to DMS circuits as well as in the DMS itself. Specific activation of midbrain to DMS circuits also decreased shifting after losses and reversal learning performance. However, these alterations were dependent on the probabilistic contingency.

Conclusions

Our work suggests that the DMS plays a multifaceted role in reversal learning. Increasing DMS activity impairs multiple reversal learning processes dependent on the level of uncertainty, confirming its role in the maintenance and selection of incoming cortical inputs. Together, these outcomes suggest that elevated dopamine levels in the DMS could contribute to decision-making impairments in individuals with psychosis.

Inhibition of the dorsomedial striatal direct pathway is essential for the execution of action sequences

Contrary to the previous notion that the dorsomedial striatum (DMS) is crucial for acquiring new learning, accumulated evidence has suggested that the DMS also plays a role in the execution of already learned action sequences. Here, we examined how the direct and indirect pathways in the DMS regulate action sequences using a task that requires animals to press a lever consecutively. Cell-type-specific bulk Ca2+ recording revealed that the direct pathway was inhibited at the time of sequence execution. The sequence-related response was blunted in trials where the sequential behaviors were disrupted. Optogenetic activation at the sequence start caused distraction of action sequences without affecting motor function or memory of the task structure. By contrast with the direct pathway, the indirect pathway was slightly activated at the start of the sequence, but the optogenetic suppression of such sequence-related signaling did not impact the behaviors. These results suggest that the inhibition of the DMS direct pathway promotes sequence execution potentially by suppressing the formation of a new association.

The respective activation and silencing of striatal direct and indirect pathway neurons support behavior encoding

The basal ganglia are known to control actions and modulate movements. Neuronal activity in the two efferent pathways of the dorsal striatum is critical for appropriate behavioral control. Previous evidence has led to divergent conclusions on the respective engagement of both pathways during actions. Using calcium imaging to evaluate how neurons in the direct and indirect pathways encode behaviors during self-paced spontaneous explorations in an open field, we observed that the two striatal pathways exhibit distinct tuning properties. Supervised learning algorithms revealed that direct pathway neurons encode behaviors through their activation, whereas indirect pathway neurons exhibit behavior-specific silencing. These properties remain stable for weeks. Our findings highlight a complementary encoding of behaviors with congruent activations in the direct pathway encoding multiple accessible behaviors in a given context, and in the indirect pathway encoding the suppression of competing behaviors. This model reconciles previous conflicting conclusions on motor encoding in the striatum.

The indirect pathway of the basal ganglia promotes transient punishment but not motor suppression

Optogenetic stimulation of Adora2a receptor-expressing spiny projection neurons (A2A-SPNs) in the striatum drives locomotor suppression and transient punishment, results attributed to activation of the indirect pathway. The sole long-range projection target of A2A-SPNs is the external globus pallidus (GPe). Unexpectedly, we found that inhibition of the GPe drove transient punishment but not suppression of movement. Within the striatum, A2A-SPNs inhibit other SPNs through a short-range inhibitory collateral network, and we found that optogenetic stimuli that drove motor suppression shared a common mechanism of recruiting this inhibitory collateral network. Our results suggest that the indirect pathway plays a more prominent role in transient punishment than in motor control and challenges the assumption that activity of A2A-SPNs is synonymous with indirect pathway activity.

Compartment-specific dendritic information processing in striatal cholinergic interneurons is reconfigured by peptide neuromodulation

Cholinergic interneurons are central hubs of the striatal neuronal network, controlling information processing in a behavioral-state-dependent manner. It remains unknown, however, how such state transitions influence the integrative properties of these neurons. To address this, we made simultaneous somato-dendritic recordings from identified rodent cholinergic interneurons, revealing that action potentials are initiated at dendritic sites because of a dendritic axonal origin. Functionally, this anatomical arrangement ensured that the action potential initiation threshold was lowest at axon-bearing dendritic sites, a privilege efficacy powerfully accentuated at the hyperpolarized membrane potentials achieved in cholinergic interneurons following salient behavioral stimuli. Experimental analysis revealed the voltage-dependent attenuation of the efficacy of non-axon-bearing dendritic excitatory input was mediated by the recruitment of dendritic potassium channels, a regulatory mechanism that, in turn, was controlled by the pharmacological activation of neurokinin receptors. Together, these results indicate that the neuropeptide microenvironment dynamically controls state- and compartment-dependent dendritic information processing in striatal cholinergic interneurons.

Optogenetic Globus Pallidus Stimulation Improves Motor Deficits in 6-Hydroxydopamine-Lesioned Mouse Model of Parkinson's Disease

Excessive inhibition of the external globus pallidus (GPe) by striatal GABAergic neurons is considered a central mechanism contributing to motor symptoms of Parkinson's disease (PD). While electrophysiological findings support this view, behavioral studies assessing the beneficial effects of global GPe activations are scarce and the reported results are controversial. We used an optogenetic approach and the standard unilateral 6-hydroxydopamine nigrostriatal dopamine (DA) lesion model of PD to explore the effects of GPe photostimulation on motor deficits in mice. Global optogenetic GPe inhibition was used in normal mice to verify whether it reproduced the typical motor impairment induced by DA lesions. GPe activation improved ipsilateral circling, contralateral forelimb akinesia, locomotor hypoactivity, and bradykinesia in 6-OHDA-lesioned mice at ineffective photostimulation parameters (532 nm, 5 Hz, 3 mW) in normal mice. GPe photoinhibition (450 nm, 12 mW) had no effect on locomotor activity and forelimb use in normal mice. Bilateral photoinhibition (450 nm, 6 mW/side) reduced directed exploration and improved working memory performances indicating that recruitment of GPe in physiological conditions may depend on the behavioral task involved. Collectively, these findings shed new light on the functional role of GPe and suggest that it is a promising target for neuromodulatory restoration of motor deficits in PD.

Trial-history biases in evidence accumulation can give rise to apparent lapses

Trial history biases and lapses are two of the most common suboptimalities observed during perceptual decision-making. These suboptimalities are routinely assumed to arise from distinct processes. However, several hints in the literature suggest that they covary in their prevalence and that their proposed neural substrates overlap - what could underlie these links? Here we demonstrate that history biases and apparent lapses can both arise from a common cognitive process that is normative under misbeliefs about non-stationarity in the world. This corresponds to an accumulation-to-bound model with history-dependent updates to the initial state of the accumulator. We test our model's predictions about the relative prevalence of history biases and lapses, and show that they are robustly borne out in two distinct rat decision-making datasets, including data from a novel reaction time task. Our model improves the ability to precisely predict decision-making dynamics within and across trials, by positing a process through which agents can generate quasi-stochastic choices.

Comparing rapid rule-learning strategies in humans and monkeys

Inter-species comparisons are key to deriving an understanding of the behavioral and neural correlates of human cognition from animal models. We perform a detailed comparison of macaque monkey and human strategies on an analogue of the Wisconsin Card Sort Test, a widely studied and applied multi-attribute measure of cognitive function, wherein performance requires the inference of a changing rule given ambiguous feedback. We found that well-trained monkeys rapidly infer rules but are three times slower than humans. Model fits to their choices revealed hidden states akin to feature-based attention in both species, and decision processes that resembled a Win-stay lose-shift strategy with key differences. Monkeys and humans test multiple rule hypotheses over a series of rule-search trials and perform inference-like computations to exclude candidates. An attention-set based learning stage categorization revealed that perseveration, random exploration and poor sensitivity to negative feedback explain the under-performance in monkeys.

Dynamic control of decision and movement speed in the human basal ganglia

To optimally adjust our behavior to changing environments we need to both adjust the speed of our decisions and movements. Yet little is known about the extent to which these processes are controlled by common or separate mechanisms. Furthermore, while previous evidence from computational models and empirical studies suggests that the basal ganglia play an important role during adjustments of decision-making, it remains unclear how this is implemented. Leveraging the opportunity to directly access the subthalamic nucleus of the basal ganglia in humans undergoing deep brain stimulation surgery, we here combine invasive electrophysiological recordings, electrical stimulation and computational modelling of perceptual decision-making. We demonstrate that, while similarities between subthalamic control of decision- and movement speed exist, the causal contribution of the subthalamic nucleus to these processes can be disentangled. Our results show that the basal ganglia independently control the speed of decisions and movement for each hemisphere during adaptive behavior.

Activation, but not inhibition, of the indirect pathway disrupts choice rejection in a freely moving, multiple-choice foraging task

The dorsomedial striatum (DMS) plays a key role in action selection, but less is known about how direct and indirect pathway spiny projection neurons (dSPNs and iSPNs, respectively) contribute to choice rejection in freely moving animals. Here, we use pathway-specific chemogenetic manipulation during a serial choice foraging task to test the role of dSPNs and iSPNs in learned choice rejection. We find that chemogenetic activation, but not inhibition, of iSPNs disrupts rejection of nonrewarded choices, contrary to predictions of a simple "select/suppress" heuristic. Our findings suggest that iSPNs' role in stopping and freezing does not extend in a simple fashion to choice rejection in an ethological, freely moving context. These data may provide insights critical for the successful design of interventions for addiction or other conditions in which it is desirable to strengthen choice rejection.

Multiple timescales of sensory-evidence accumulation across the dorsal cortex

Cortical areas seem to form a hierarchy of intrinsic timescales, but the relevance of this organization for cognitive behavior remains unknown. In particular, decisions requiring the gradual accrual of sensory evidence over time recruit widespread areas across this hierarchy. Here, we tested the hypothesis that this recruitment is related to the intrinsic integration timescales of these widespread areas. We trained mice to accumulate evidence over seconds while navigating in virtual reality and optogenetically silenced the activity of many cortical areas during different brief trial epochs. We found that the inactivation of all tested areas affected the evidence-accumulation computation. Specifically, we observed distinct changes in the weighting of sensory evidence occurring during and before silencing, such that frontal inactivations led to stronger deficits on long timescales than posterior cortical ones. Inactivation of a subset of frontal areas also led to moderate effects on behavioral processes beyond evidence accumulation. Moreover, large-scale cortical Ca2+ activity during task performance displayed different temporal integration windows. Our findings suggest that the intrinsic timescale hierarchy of distributed cortical areas is an important component of evidence-accumulation mechanisms.

A blueprint for examining striatal control of cognition

In a recent study, Bolkan, Stone, and colleagues demonstrated that direct and indirect striatal pathways in mice exert opponent control over choice behavior in a task- and state-dependent manner. This work highlights the need for rigorously controlled behavioral experiments and novel behavioral modeling in investigations of the neural mechanisms of decision making.