Network Resets in Medial Prefrontal Cortex Mark the Onset of Behavioral Uncertainty

Broadscale dampening of uncertainty adjustment in the aging brain

The ability to prioritize among input features according to relevance enables adaptive behaviors across the human lifespan. However, relevance often remains ambiguous, and such uncertainty increases demands for dynamic control. While both cognitive stability and flexibility decline during healthy ageing, it is unknown whether aging alters how uncertainty impacts perception and decision-making, and if so, via which neural mechanisms. Here, we assess uncertainty adjustment across the adult lifespan (N = 100; cross-sectional) via behavioral modeling and a theoretically informed set of EEG-, fMRI-, and pupil-based signatures. On the group level, older adults show a broad dampening of uncertainty adjustment relative to younger adults. At the individual level, older individuals whose modulation more closely resembled that of younger adults also exhibit better maintenance of cognitive control. Our results highlight neural mechanisms whose maintenance plausibly enables flexible task-set, perception, and decision computations across the adult lifespan.

Sensory History Drives Adaptive Neural Geometry in LP/Pulvinar-Prefrontal Cortex Circuits

Prior expectations guide attention and support perceptual filtering for efficient processing during decision-making. Here we show that during a visual discrimination task, mice adaptively use prior stimulus history to guide ongoing choices by estimating differences in evidence between consecutive trials (| Δ Dir |). The thalamic lateral posterior (LP)/pulvinar nucleus provides robust inputs to the Anterior Cingulate Cortex (ACC), which has been implicated in selective attention and predictive processing, but the function of the LP-ACC projection is unknown. We found that optogenetic manipulations of LP-ACC axons disrupted animals' ability to effectively estimate and use information across stimulus history, leading to | Δ Dir |-dependent ipsilateral biases. Two-photon calcium imaging of LP-ACC axons revealed an engagement-dependent low-dimensional organization of stimuli along a curved manifold. This representation was scaled by | Δ Dir | in a manner that emphasized greater deviations from prior evidence. Thus, our work identifies the LP-ACC pathway as essential for selecting and evaluating stimuli relative to prior evidence to guide decisions.

Abrupt and spontaneous strategy switches emerge in simple regularised neural networks

Humans sometimes have an insight that leads to a sudden and drastic performance improvement on the task they are working on. Sudden strategy adaptations are often linked to insights, considered to be a unique aspect of human cognition tied to complex processes such as creativity or meta-cognitive reasoning. Here, we take a learning perspective and ask whether insight-like behaviour can occur in simple artificial neural networks, even when the models only learn to form input-output associations through gradual gradient descent. We compared learning dynamics in humans and regularised neural networks in a perceptual decision task that included a hidden regularity to solve the task more efficiently. Our results show that only some humans discover this regularity, and that behaviour is marked by a sudden and abrupt strategy switch that reflects an aha-moment. Notably, we find that simple neural networks with a gradual learning rule and a constant learning rate closely mimicked behavioural characteristics of human insight-like switches, exhibiting delay of insight, suddenness and selective occurrence in only some networks. Analyses of network architectures and learning dynamics revealed that insight-like behaviour crucially depended on a regularised gating mechanism and noise added to gradient updates, which allowed the networks to accumulate "silent knowledge" that is initially suppressed by regularised gating. This suggests that insight-like behaviour can arise from gradual learning in simple neural networks, where it reflects the combined influences of noise, gating and regularisation. These results have potential implications for more complex systems, such as the brain, and guide the way for future insight research.

Hippocampal representations of alternative possibilities are flexibly generated to meet cognitive demands

The cognitive ability to go beyond the present to consider alternative possibilities, including potential futures and counterfactual pasts, can support adaptive decision making. Complex and changing real-world environments, however, have many possible alternatives. Whether and how the brain can select among them to represent alternatives that meet current cognitive needs remains unknown. We therefore examined neural representations of alternative spatial locations in the rat hippocampus during navigation in a complex patch foraging environment with changing reward probabilities. We found representations of multiple alternatives along paths ahead and behind the animal, including in distant alternative patches. Critically, these representations were modulated in distinct patterns across successive trials: alternative paths were represented proportionate to their evolving relative value and predicted subsequent decisions, whereas distant alternatives were prevalent during value updating. These results demonstrate that the brain modulates the generation of alternative possibilities in patterns that meet changing cognitive needs for adaptive behavior.

Ventral tegmental area dopamine neural activity switches simultaneously with rule representations in the prefrontal cortex and hippocampus

Multiple brain regions need to coordinate activity to support cognitive flexibility and behavioral adaptation. Neural activity in both the hippocampus (HPC) and prefrontal cortex (PFC) is known to represent spatial context and is sensitive to reward and rule alterations. Midbrain dopamine (DA) activity is key in reward seeking behavior and learning. There is abundant evidence that midbrain DA modulates HPC and PFC activity. However, it remains underexplored how these networks engage dynamically and coordinate temporally when animals must adjust their behavior according to changing reward contingencies. In particular, is there any relationship between DA reward prediction change during rule switching, and rule representation changes in PFC and CA1? We addressed these questions using simultaneous recording of neuronal population activity from the hippocampal area CA1, PFC and ventral tegmental area (VTA) in male TH-Cre rats performing two spatial working memory tasks with frequent rule switches in blocks of trials. CA1 and PFC ensembles showed rule-specific activity both during maze running and at reward locations, with PFC rule coding more consistent across animals compared to CA1. Optogenetically tagged VTA DA neuron firing activity responded to and predicted reward outcome. We found that the correct prediction in DA emerged gradually over trials after rule-switching in coordination with transitions in PFC and CA1 ensemble representations of the current rule after a rule switch, followed by behavioral adaptation to the correct rule sequence. Therefore, our study demonstrates a crucial temporal coordination between the rule representation in PFC/CA1, the dopamine reward signal and behavioral strategy.

Persistent Impact of Prior Experience on Spatial Learning

Learning to solve a new problem involves identifying the operating rules, which can be accelerated if known rules generalize in the new context. We ask how prior experience affects learning a new rule that is distinct from known rules. We examined how rats learned a new spatial navigation task after having previously learned tasks with different navigation rules. The new task differed from the previous tasks in spatial layout and navigation rule. We found that experience history did not impact overall performance. However, by examining navigation choice sequences in the new task, we found experience-dependent differences in exploration patterns during early stages of learning, as well as differences in the types of errors made during stable performance. The differences were consistent with the animals adopting experience-dependent memory strategies to discover and implement the new rule. Our results indicate prior experience shapes the strategies for solving novel problems, and the impact of prior experience remains persistent.

Rat anterior cingulate neurons responsive to rule or strategy changes are modulated by the hippocampal theta rhythm and sharp-wave ripples

To better understand neural processing during adaptive learning of stimulus-response-reward contingencies, we recorded synchrony of neuronal activity in anterior cingulate cortex (ACC) and hippocampal rhythms in male rats acquiring and switching between spatial and visual discrimination tasks in a Y-maze. ACC population activity as well as single unit activity shifted shortly after task rule changes or just before the rats adopted different task strategies. Hippocampal theta oscillations (associated with memory encoding) modulated an elevated proportion of rule-change responsive neurons (70%), but other neurons that were correlated with strategy-change, strategy value and reward-rate were not. However, hippocampal sharp wave-ripples modulated significantly higher proportions of rule-change, strategy-change and reward-rate responsive cells during post-session sleep but not pre-session sleep. This suggests an underestimated mechanism for hippocampal mismatch and contextual signals to facilitate ACC to detect contingency changes for cognitive flexibility, a function that is attenuated after it is damaged.

Prediction-error signals in anterior cingulate cortex drive task-switching

Task-switching is a fundamental cognitive ability that allows animals to update their knowledge of current rules or contexts. Detecting discrepancies between predicted and observed events is essential for this process. However, little is known about how the brain computes cognitive prediction-errors and whether neural prediction-error signals are causally related to task-switching behaviours. Here we trained mice to use a prediction-error to switch, in a single trial, between responding to the same stimuli using two distinct rules. Optogenetic silencing and un-silencing, together with widefield and two-photon calcium imaging revealed that the anterior cingulate cortex (ACC) was specifically required for this rapid task-switching, but only when it exhibited neural prediction-error signals. These prediction-error signals were projection-target dependent and were larger preceding successful behavioural transitions. An all-optical approach revealed a disinhibitory interneuron circuit required for successful prediction-error computation. These results reveal a circuit mechanism for computing prediction-errors and transitioning between distinct cognitive states.

Anterior cingulate cortex provides the neural substrates for feedback-driven iteration of decision and value representation

Adjusting decision-making under uncertain and dynamic situations is the hallmark of intelligence. It requires a system capable of converting feedback information to renew the internal value. The anterior cingulate cortex (ACC) involves in error and reward events that prompt switching or maintenance of current decision strategies. However, it is unclear whether and how the changes of stimulus-action mapping during behavioral adaptation are encoded, nor how such computation drives decision adaptation. Here, we tracked ACC activity in male mice performing go/no-go auditory discrimination tasks with manipulated stimulus-reward contingencies. Individual ACC neurons integrate the outcome information to the value representation in the next-run trials. Dynamic recruitment of them determines the learning rate of error-guided value iteration and decision adaptation, forming a non-linear feedback-driven updating system to secure the appropriate decision switch. Optogenetically suppressing ACC significantly slowed down feedback-driven decision switching without interfering with the execution of the established strategy.

Temporal information in the anterior cingulate cortex relates to accumulated experiences

Anterior cingulate cortex (ACC) activity is important for operations that require the ability to integrate multiple experiences over time, such as rule learning, cognitive flexibility, working memory, and long-term memory recall. To shed light on this, we analyzed neuronal activity while rats repeated the same behaviors during hour-long sessions to investigate how activity changed over time. We recorded neuronal ensembles as rats performed a decision-free operant task with varying reward likelihoods at three different response ports (n = 5). Neuronal state space analysis revealed that each repetition of a behavior was distinct, with more recent behaviors more similar than those further apart in time. ACC activity was dominated by a slow, gradual change in low-dimensional representations of neural state space aligning with the pace of behavior. Temporal progression, or drift, was apparent on the top principal component for every session and was driven by the accumulation of experiences and not an internal clock. Notably, these signals were consistent across subjects, allowing us to accurately predict trial numbers based on a model trained on data from a different animal. We observed that non-continuous ramping firing rates over extended durations (tens of minutes) drove the low-dimensional ensemble representations. 40% of ACC neurons' firing ramped over a range of trial lengths and combinations of shorter duration ramping neurons created ensembles that tracked longer durations. These findings provide valuable insights into how the ACC, at an ensemble level, conveys temporal information by reflecting the accumulation of experiences over extended periods.

Humans forage for reward in reinforcement learning tasks

How do we make good decisions in uncertain environments? In psychology and neuroscience, the classic answer is that we calculate the value of each option and then compare the values to choose the most rewarding, modulo some exploratory noise. An ethologist, conversely, would argue that we commit to one option until its value drops below a threshold, at which point we start exploring other options. In order to determine which view better describes human decision-making, we developed a novel, foraging-inspired sequential decision-making model and used it to ask whether humans compare to threshold ("Forage") or compare alternatives ("Reinforcement-Learn" [RL]). We found that the foraging model was a better fit for participant behavior, better predicted the participants' tendency to repeat choices, and predicted the existence of held-out participants with a pattern of choice that was almost impossible under RL. Together, these results suggest that humans use foraging computations, rather than RL, even in classic reinforcement learning tasks.

A vast space of compact strategies for effective decisions

Inference-based decision-making, which underlies a broad range of behavioral tasks, is typically studied using a small number of handcrafted models. We instead enumerate a complete ensemble of strategies that could be used to effectively, but not necessarily optimally, solve a dynamic foraging task. Each strategy is expressed as a behavioral "program" that uses a limited number of internal states to specify actions conditioned on past observations. We show that the ensemble of strategies is enormous-comprising a quarter million programs with up to five internal states-but can nevertheless be understood in terms of algorithmic "mutations" that alter the structure of individual programs. We devise embedding algorithms that reveal how mutations away from a Bayesian-like strategy can diversify behavior while preserving performance, and we construct a compositional description to link low-dimensional changes in algorithmic structure with high-dimensional changes in behavior. Together, this work provides an alternative approach for understanding individual variability in behavior across animals and tasks.

Tracking subjects' strategies in behavioural choice experiments at trial resolution

Investigating how, when, and what subjects learn during decision-making tasks requires tracking their choice strategies on a trial-by-trial basis. Here, we present a simple but effective probabilistic approach to tracking choice strategies at trial resolution using Bayesian evidence accumulation. We show this approach identifies both successful learning and the exploratory strategies used in decision tasks performed by humans, non-human primates, rats, and synthetic agents. Both when subjects learn and when rules change the exploratory strategies of win-stay and lose-shift, often considered complementary, are consistently used independently. Indeed, we find the use of lose-shift is strong evidence that subjects have latently learnt the salient features of a new rewarded rule. Our approach can be extended to any discrete choice strategy, and its low computational cost is ideally suited for real-time analysis and closed-loop control.

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.

ACC neural ensemble dynamics are structured by strategy prevalence

Medial frontal cortical areas are thought to play a critical role in the brain's ability to flexibly deploy strategies that are effective in complex settings, yet the underlying circuit computations remain unclear. Here, by examining neural ensemble activity in male rats that sample different strategies in a self-guided search for latent task structure, we observe robust tracking during strategy execution of a summary statistic for that strategy in recent behavioral history by the anterior cingulate cortex (ACC), especially by an area homologous to primate area 32D. Using the simplest summary statistic - strategy prevalence in the last 20 choices - we find that its encoding in the ACC during strategy execution is wide-scale, independent of reward delivery, and persists through a substantial ensemble reorganization that accompanies changes in global context. We further demonstrate that the tracking of reward by the ACC ensemble is also strategy-specific, but that reward prevalence is insufficient to explain the observed activity modulation during strategy execution. Our findings argue that ACC ensemble dynamics is structured by a summary statistic of recent behavioral choices, raising the possibility that ACC plays a role in estimating - through statistical learning - which actions promote the occurrence of events in the environment.

Reconstructing computational system dynamics from neural data with recurrent neural networks

Computational models in neuroscience usually take the form of systems of differential equations. The behaviour of such systems is the subject of dynamical systems theory. Dynamical systems theory provides a powerful mathematical toolbox for analysing neurobiological processes and has been a mainstay of computational neuroscience for decades. Recently, recurrent neural networks (RNNs) have become a popular machine learning tool for studying the non-linear dynamics of neural and behavioural processes by emulating an underlying system of differential equations. RNNs have been routinely trained on similar behavioural tasks to those used for animal subjects to generate hypotheses about the underlying computational mechanisms. By contrast, RNNs can also be trained on the measured physiological and behavioural data, thereby directly inheriting their temporal and geometrical properties. In this way they become a formal surrogate for the experimentally probed system that can be further analysed, perturbed and simulated. This powerful approach is called dynamical system reconstruction. In this Perspective, we focus on recent trends in artificial intelligence and machine learning in this exciting and rapidly expanding field, which may be less well known in neuroscience. We discuss formal prerequisites, different model architectures and training approaches for RNN-based dynamical system reconstructions, ways to evaluate and validate model performance, how to interpret trained models in a neuroscience context, and current challenges.

Fast updating feedback from piriform cortex to the olfactory bulb relays multimodal reward contingency signals during rule-reversal

While animals readily adjust their behavior to adapt to relevant changes in the environment, the neural pathways enabling these changes remain largely unknown. Here, using multiphoton imaging, we investigated whether feedback from the piriform cortex to the olfactory bulb supports such behavioral flexibility. To this end, we engaged head-fixed mice in a multimodal rule-reversal task guided by olfactory and auditory cues. Both odor and, surprisingly, the sound cues triggered cortical bulbar feedback responses which preceded the behavioral report. Responses to the same sensory cue were strongly modulated upon changes in stimulus-reward contingency (rule reversals). The re-shaping of individual bouton responses occurred within seconds of the rule-reversal events and was correlated with changes in the behavior. Optogenetic perturbation of cortical feedback within the bulb disrupted the behavioral performance. Our results indicate that the piriform-to-olfactory bulb feedback carries reward contingency signals and is rapidly re-formatted according to changes in the behavioral context.

Long-range inhibition synchronizes and updates prefrontal task activity

Changes in patterns of activity within the medial prefrontal cortex enable rodents, non-human primates and humans to update their behaviour to adapt to changes in the environment-for example, during cognitive tasks1-5. Parvalbumin-expressing inhibitory neurons in the medial prefrontal cortex are important for learning new strategies during a rule-shift task6-8, but the circuit interactions that switch prefrontal network dynamics from maintaining to updating task-related patterns of activity remain unknown. Here we describe a mechanism that links parvalbumin-expressing neurons, a new callosal inhibitory connection, and changes in task representations. Whereas nonspecifically inhibiting all callosal projections does not prevent mice from learning rule shifts or disrupt the evolution of activity patterns, selectively inhibiting only callosal projections of parvalbumin-expressing neurons impairs rule-shift learning, desynchronizes the gamma-frequency activity that is necessary for learning8 and suppresses the reorganization of prefrontal activity patterns that normally accompanies rule-shift learning. This dissociation reveals how callosal parvalbumin-expressing projections switch the operating mode of prefrontal circuits from maintenance to updating by transmitting gamma synchrony and gating the ability of other callosal inputs to maintain previously established neural representations. Thus, callosal projections originating from parvalbumin-expressing neurons represent a key circuit locus for understanding and correcting the deficits in behavioural flexibility and gamma synchrony that have been implicated in schizophrenia and related conditions9,10.

Tactile cognition in rodents

Since the discovery 50 years ago of the precisely ordered representation of the whiskers in somatosensory cortex, the rodent tactile sensory system has been a fertile ground for the study of sensory processing. With the growing sophistication of touch-based behavioral paradigms, together with advances in neurophysiological methodology, a new approach is emerging. By posing increasingly complex perceptual and memory problems, in many cases analogous to human psychophysical tasks, investigators now explore the operations underlying rodent problem solving. We define the neural basis of tactile cognition as the transformation from a stage in which neuronal activity encodes elemental features, local in space and in time, to a stage in which neuronal activity is an explicit representation of the behavioral operations underlying the current task. Selecting a set of whisker-based behavioral tasks, we show that rodents achieve high level performance through the workings of neuronal circuits that are accessible, decodable, and manipulatable. As a means towards exploring tactile cognition, this review presents leading psychophysical paradigms and, where known, their neural correlates.

Geometric transformation of cognitive maps for generalization across hippocampal-prefrontal circuits

The ability to abstract information to guide decisions during navigation across changing environments is essential for adaptation and requires the integrity of the hippocampal-prefrontal circuitry. The hippocampus encodes navigational information in a cognitive map, but it remains unclear how cognitive maps are transformed across hippocampal-prefrontal circuits to support abstraction and generalization. Here, we simultaneously record hippocampal-prefrontal ensembles as rats generalize navigational rules across distinct environments. We find that, whereas hippocampal representational maps maintain specificity of separate environments, prefrontal maps generalize across environments. Furthermore, while both maps are structured within a neural manifold of population activity, they have distinct representational geometries. Prefrontal geometry enables abstraction of rule-informative variables, a representational format that generalizes to novel conditions of existing variable classes. Hippocampal geometry lacks such abstraction. Together, these findings elucidate how cognitive maps are structured into distinct geometric representations to support abstraction and generalization while maintaining memory specificity.

Perception of color in primates: A conceptual color neurons hypothesis

Perception of color by humans and other primates is a complex problem, studied by neurophysiology, psychophysiology, psycholinguistics, and even philosophy. Being mostly trichromats, simian primates have three types of opsin proteins, expressed in cone neurons in the eye, which allow for the sensing of color as the physical wavelength of light. Further, in neural networks of the retina, the coding principle changes from three types of sensor proteins to two opponent channels: activity of one type of neuron encode the evolutionarily ancient blue-yellow axis of color stimuli, and another more recent evolutionary channel, encoding the axis of red-green color stimuli. Both color channels are distinctive in neural organization at all levels from the eye to the neocortex, where it is thought that the perception of color (as philosophical qualia) emerges from the activity of some neuron ensembles. Here, using data from neurophysiology as a starting point, we propose a hypothesis on how the perception of color can be encoded in the activity of certain neurons in the neocortex. These conceptual neurons, herein referred to as 'color neurons', code only the hue of the color of visual stimulus, similar to place cells and number neurons, already described in primate brains. A case study with preliminary, but direct, evidence for existing conceptual color neurons in the human brain was published in 2008. We predict that the upcoming studies in non-human primates will be more extensive and provide a more detailed description of conceptual color neurons.

Recurrent networks endowed with structural priors explain suboptimal animal behavior

The strategies found by animals facing a new task are determined both by individual experience and by structural priors evolved to leverage the statistics of natural environments. Rats quickly learn to capitalize on the trial sequence correlations of two-alternative forced choice (2AFC) tasks after correct trials but consistently deviate from optimal behavior after error trials. To understand this outcome-dependent gating, we first show that recurrent neural networks (RNNs) trained in the same 2AFC task outperform rats as they can readily learn to use across-trial information both after correct and error trials. We hypothesize that, although RNNs can optimize their behavior in the 2AFC task without any a priori restrictions, rats' strategy is constrained by a structural prior adapted to a natural environment in which rewarded and non-rewarded actions provide largely asymmetric information. When pre-training RNNs in a more ecological task with more than two possible choices, networks develop a strategy by which they gate off the across-trial evidence after errors, mimicking rats' behavior. Population analyses show that the pre-trained networks form an accurate representation of the sequence statistics independently of the outcome in the previous trial. After error trials, gating is implemented by a change in the network dynamics that temporarily decouple the categorization of the stimulus from the across-trial accumulated evidence. Our results suggest that the rats' suboptimal behavior reflects the influence of a structural prior that reacts to errors by isolating the network decision dynamics from the context, ultimately constraining the performance in a 2AFC laboratory task.

Differential processing of decision information in subregions of rodent medial prefrontal cortex

Decision-making involves multiple cognitive processes requiring different aspects of information about the situation at hand. The rodent medial prefrontal cortex (mPFC) has been hypothesized to be central to these abilities. Functional studies have sought to link specific processes to specific anatomical subregions, but past studies of mPFC have yielded controversial results, leaving the precise nature of mPFC function unclear. To settle this debate, we recorded from the full dorso-ventral extent of mPFC in each of 8 rats, as they performed a complex economic decision task. These data revealed four distinct functional domains within mPFC that closely mirrored anatomically identified subregions, including novel evidence to divide prelimbic cortex into dorsal and ventral components. We found that dorsal aspects of mPFC (ACC, dPL) were more involved in processing information about active decisions, while ventral aspects (vPL, IL) were more engaged in motivational factors.

Temporal context and latent state inference in the hippocampal splitter signal

The hippocampus is thought to enable the encoding and retrieval of ongoing experience, the organization of that experience into structured representations like contexts, maps, and schemas, and the use of these structures to plan for the future. A central goal is to understand what the core computations supporting these functions are, and how these computations are realized in the collective action of single neurons. A potential access point into this issue is provided by 'splitter cells', hippocampal neurons that fire differentially on the overlapping segment of trajectories that differ in their past and/or future. However, the literature on splitter cells has been fragmented and confusing, owing to differences in terminology, behavioral tasks, and analysis methods across studies. In this review, we synthesize consistent findings from this literature, establish a common set of terms, and translate between single-cell and ensemble perspectives. Most importantly, we examine the combined findings through the lens of two major theoretical ideas about hippocampal function: representation of temporal context and latent state inference. We find that unique signature properties of each of these models are necessary to account for the data, but neither theory, by itself, explains all of its features. Specifically, the temporal gradedness of the splitter signal is strong support for temporal context, but is hard to explain using state models, while its flexibility and task-dependence is naturally accounted for using state inference, but poses a challenge otherwise. These theories suggest a number of avenues for future work, and we believe their application to splitter cells is a timely and informative domain for testing and refining theoretical ideas about hippocampal function.

Efficient stabilization of imprecise statistical inference through conditional belief updating

Statistical inference is the optimal process for forming and maintaining accurate beliefs about uncertain environments. However, human inference comes with costs due to its associated biases and limited precision. Indeed, biased or imprecise inference can trigger variable beliefs and unwarranted changes in behaviour. Here, by studying decisions in a sequential categorization task based on noisy visual stimuli, we obtained converging evidence that humans reduce the variability of their beliefs by updating them only when the reliability of incoming sensory information is judged as sufficiently strong. Instead of integrating the evidence provided by all stimuli, participants actively discarded as much as a third of stimuli. This conditional belief updating strategy shows good test-retest reliability, correlates with perceptual confidence and explains human behaviour better than previously described strategies. This seemingly suboptimal strategy not only reduces the costs of imprecise computations but also, counterintuitively, increases the accuracy of resulting decisions.

Reward expectation extinction restructures and degrades CA1 spatial maps through loss of a dopaminergic reward proximity signal

Hippocampal place cells support reward-related spatial memories by forming a cognitive map that over-represents reward locations. The strength of these memories is modulated by the extent of reward expectation during encoding. However, the circuit mechanisms underlying this modulation are unclear. Here we find that when reward expectation is extinguished in mice, they remain engaged with their environment, yet place cell over-representation of rewards vanishes, place field remapping throughout the environment increases, and place field trial-to-trial reliability decreases. Interestingly, Ventral Tegmental Area (VTA) dopaminergic axons in CA1 exhibit a ramping reward-proximity signal that depends on reward expectation and inhibiting VTA dopaminergic neurons largely replicates the effects of extinguishing reward expectation. We conclude that changing reward expectation restructures CA1 cognitive maps and determines map reliability by modulating the dopaminergic VTA-CA1 reward-proximity signal. Thus, internal states of high reward expectation enhance encoding of spatial memories by reinforcing hippocampal cognitive maps associated with reward.

The principle of 'brain energy on demand' and its predictive power for stress, sleep, stroke, obesity and diabetes

Does the brain actively draw energy from the body when needed? There are different schools of thought regarding energy metabolism. In this study, the various theoretical models are classified into one of two categories: (1) conceptualizations of the brain as being purely passively supplied, which we call 'P-models,' and (2) models understanding the brain as not only passively receiving energy but also actively procuring energy for itself on demand, which we call 'A-models.' One prominent example of such theories making use of an A-model is the selfish-brain theory. The ability to make predictions was compared between the A- and P-models. A-models were able to predict and coherently explain all data examined, which included stress, sleep, caloric restriction, stroke, type-1-diabetes mellitus, obesity, and type-2-diabetes, whereas the predictions of P-models failed in most cases. The strength of the evidence supporting A-models is based on the coherence of accurate predictions across a spectrum of metabolic states. The theory test conducted here speaks to a brain that pulls its energy from the body on-demand.

Neuroprotection in late life attention-deficit/hyperactivity disorder: A review of pharmacotherapy and phenotype across the lifespan

For decades, psychostimulants have been the gold standard pharmaceutical treatment for attention-deficit/hyperactivity disorder (ADHD). In the United States, an astounding 9% of all boys and 4% of girls will be prescribed stimulant drugs at some point during their childhood. Recent meta-analyses have revealed that individuals with ADHD have reduced brain volume loss later in life (>60 y.o.) compared to the normal aging brain, which suggests that either ADHD or its treatment may be neuroprotective. Crucially, these neuroprotective effects were significant in brain regions (e.g., hippocampus, amygdala) where severe volume loss is linked to cognitive impairment and Alzheimer's disease. Historically, the ADHD diagnosis and its pharmacotherapy came about nearly simultaneously, making it difficult to evaluate their effects in isolation. Certain evidence suggests that psychostimulants may normalize structural brain changes typically observed in the ADHD brain. If ADHD itself is neuroprotective, perhaps exercising the brain, then psychostimulants may not be recommended across the lifespan. Alternatively, if stimulant drugs are neuroprotective, then this class of medications may warrant further investigation for their therapeutic effects. Here, we take a bottom-up holistic approach to review the psychopharmacology of ADHD in the context of recent models of attention. We suggest that future studies are greatly needed to better appreciate the interactions amongst an ADHD diagnosis, stimulant treatment across the lifespan, and structure-function alterations in the aging brain.

Belief traps: Tackling the inertia of harmful beliefs

Beliefs can be highly resilient in the sense that they are not easily abandoned in the face of counterevidence. This has the advantage of guiding consistent behavior and judgments but may also have destructive consequences for individuals, nature, and society. For instance, pathological beliefs can sustain psychiatric disorders, the belief that rhinoceros horn is an aphrodisiac may drive a species extinct, beliefs about gender or race may fuel discrimination, and belief in conspiracy theories can undermine democracy. Here, we present a unifying framework of how self-amplifying feedbacks shape the inertia of beliefs on levels ranging from neuronal networks to social systems. Sustained exposure to counterevidence can destabilize rigid beliefs but requires organized rational override as in cognitive behavioral therapy for pathological beliefs or institutional control of discrimination to reduce racial biases. Black-and-white thinking is a major risk factor for the formation of resilient beliefs associated with psychiatric disorders as well as prejudices and conspiracy thinking. Such dichotomous thinking is characteristic of a lack of cognitive resources, which may be exacerbated by stress. This could help explain why conspiracy thinking and psychiatric disorders tend to peak during crises. A corollary is that addressing social factors such as poverty, social cleavage, and lack of education may be the most effective way to prevent the emergence of rigid beliefs, and thus of problems ranging from psychiatric disorders to prejudices, conspiracy theories, and posttruth politics.

Cingulate-motor circuits update rule representations for sequential choice decisions

Anterior cingulate cortex mediates the flexible updating of an animal’s choice responses upon rule changes in the environment. However, how anterior cingulate cortex entrains motor cortex to reorganize rule representations and generate required motor outputs remains unclear. Here, we demonstrate that chemogenetic silencing of the terminal projections of cingulate cortical neurons in secondary motor cortex in the rat disrupts choice performance in trials immediately following rule switches, suggesting that these inputs are necessary to update rule representations for choice decisions stored in the motor cortex. Indeed, the silencing of cingulate cortex decreases rule selectivity of secondary motor cortical neurons. Furthermore, optogenetic silencing of cingulate cortical neurons that is temporally targeted to error trials immediately after rule switches exacerbates errors in the following trials. These results suggest that cingulate cortex monitors behavioral errors and updates rule representations in motor cortex, revealing a critical role for cingulate-motor circuits in adaptive choice behaviors.

The anterior cingulate cortex allows an animal to update its behaviour when the environment changes. In this work, the authors identify a pathway from cingulate to secondary motor cortex, critical for updating motor rules following behavioural errors.

Obesity and Type 2 Diabetes Mellitus Explained by the Free Energy Principle

According to the free energy principle, all sentient beings strive to minimize surprise or, in other words, an information-theoretical quantity called variational free energy. Consequently, psychosocial “stress” can be redefined as a state of “heightened expected free energy,” that is, a state of “expected surprise” or “uncertainty.” Individuals experiencing stress primarily attempt to reduce uncertainty, or expected free energy, with the help of what is called an uncertainty resolution program (URP). The URP consists of three subroutines: First, an arousal state is induced that increases cerebral information transmission and processing to reduce uncertainty as quickly as possible. Second, these additional computations cost the brain additional energy, which it demands from the body. Third, the program controls which stress reduction measures are learned for future use and which are not. We refer to an episode as “good” stress, when the URP has successfully reduced uncertainty. Failure of the URP to adequately reduce uncertainty results in either stress habituation or prolonged toxic stress. Stress habituation reduces uncertainty by flattening/broadening individual goal beliefs so that outcomes previously considered as untenable become acceptable. Habituated individuals experience so-called “tolerable” stress. Referring to the Selfish Brain theory and the experimental evidence supporting it, we show that habituated people, who lack stress arousals and therefore have decreased average brain energy consumption, tend to develop an obese type 2 diabetes mellitus phenotype. People, for whom habituation is not the free-energy-optimal solution, do not reduce their uncertainty by changing their goal preferences, and are left with nothing but “toxic” stress. Toxic stress leads to recurrent or persistent arousal states and thus increased average brain energy consumption, which in turn promotes the development of a lean type 2 diabetes mellitus phenotype. In conclusion, we anchor the psychosomatic concept of stress in the information-theoretical concept of uncertainty as defined by the free energy principle. In addition, we detail the neurobiological mechanisms underlying uncertainty reduction and illustrate how uncertainty can lead to psychosomatic illness.

Unsupervised discovery of behaviorally relevant brain states in rats playing hide-and-seek

In classical neuroscience experiments, neural activity is measured across many identical trials of animals performing simple tasks and is then analyzed, associating neural responses to pre-defined experimental parameters. This type of analysis is not suitable for patterns of behavior that unfold freely, such as play behavior. Here, we attempt an alternative approach for exploratory data analysis on a single-trial level, applicable in more complex and naturalistic behavioral settings in which no two trials are identical. We analyze neural population activity in the prefrontal cortex (PFC) of rats playing hide-and-seek and show that it is possible to discover what aspects of the task are reflected in the recorded activity with a limited number of simultaneously recorded cells (≤ 31). Using hidden Markov models, we cluster population activity in the PFC into a set of neural states, each associated with a pattern of neural activity. Despite high variability in behavior, relating the inferred states to the events of the hide-and-seek game reveals neural states that consistently appear at the same phases of the game. Furthermore, we show that by applying the segmentation inferred from neural data to the animals' behavior, we can explore and discover novel correlations between neural activity and behavior. Finally, we replicate the results in a second dataset and show that population activity in the PFC displays distinct sets of states during playing hide-and-seek and observing others play the game. Overall, our results reveal robust, state-like representations in the rat PFC during unrestrained playful behavior and showcase the applicability of population analyses in naturalistic neuroscience.

How Stress Can Change Our Deepest Preferences: Stress Habituation Explained Using the Free Energy Principle

People who habituate to stress show a repetition-induced response attenuation—neuroendocrine, cardiovascular, neuroenergetic, and emotional—when exposed to a threatening environment. But the exact dynamics underlying stress habituation remain obscure. The free energy principle offers a unifying account of self-organising systems such as the human brain. In this paper, we elaborate on how stress habituation can be explained and modelled using the free energy principle. We introduce habituation priors that encode the agent’s tendency for stress habituation and incorporate them in the agent’s decision-making process. Using differently shaped goal priors—that encode the agent’s goal preferences—we illustrate, in two examples, the optimising (and thus habituating) behaviour of agents. We show that habituation minimises free energy by reducing the precision (inverse variance) of goal preferences. Reducing the precision of goal priors means that the agent accepts adverse (previously unconscionable) states (e.g., lower social status and poverty). Acceptance or tolerance of adverse outcomes may explain why habituation causes people to exhibit an attenuation of the stress response. Given that stress habituation occurs in brain regions where goal priors are encoded, i.e., in the ventromedial prefrontal cortex and that these priors are encoded as sufficient statistics of probability distributions, our approach seems plausible from an anatomical-functional and neuro-statistical point of view. The ensuing formal and generalisable account—based on the free energy principle—further motivate our novel treatment of stress habituation. Our analysis suggests that stress habituation has far-reaching consequences, protecting against the harmful effects of toxic stress, but on the other hand making the acceptability of precarious living conditions and the development of the obese type 2 diabetes mellitus phenotype more likely.

Inhibitory control of frontal metastability sets the temporal signature of cognition

Cortical dynamics are organized over multiple anatomical and temporal scales. The mechanistic origin of the temporal organization and its contribution to cognition remain unknown. Here we demonstrate the cause of this organization by studying a specific temporal signature (time constant and latency) of neural activity. In monkey frontal areas, recorded during flexible decisions, temporal signatures display specific area-dependent ranges, as well as anatomical and cell-type distributions. Moreover, temporal signatures are functionally adapted to behaviorally relevant timescales. Fine-grained biophysical network models, constrained to account for experimentally observed temporal signatures, reveal that after-hyperpolarization potassium and inhibitory GABA-B conductances critically determine areas' specificity. They mechanistically account for temporal signatures by organizing activity into metastable states, with inhibition controlling state stability and transitions. As predicted by models, state durations non-linearly scale with temporal signatures in monkey, matching behavioral timescales. Thus, local inhibitory-controlled metastability constitutes the dynamical core specifying the temporal organization of cognitive functions in frontal areas.

Activity Subspaces in Medial Prefrontal Cortex Distinguish States of the World

Medial prefrontal cortex (mPfC) activity represents information about the state of the world, including present behavior, such as decisions, and the immediate past, such as short-term memory. Unknown is whether information about different states of the world are represented in the same mPfC neural population and, if so, how they are kept distinct. To address this, we analyze here mPfC population activity of male rats learning rules in a Y-maze, with self-initiated choice trials to an arm end followed by a self-paced return during the intertrial interval (ITI). We find that trial and ITI population activity from the same population fall into different low-dimensional subspaces. These subspaces encode different states of the world: multiple features of the task can be decoded from both trial and ITI activity, but the decoding axes for the same feature are roughly orthogonal between the two task phases, and the decodings are predominantly of features of the present during the trial but features of the preceding trial during the ITI. These subspace distinctions are carried forward into sleep, where population activity is preferentially reactivated in post-training sleep but differently for activity from the trial and ITI subspaces. Our results suggest that the problem of interference when representing different states of the world is solved in mPfC by population activity occupying different subspaces for the world states, which can be independently decoded by downstream targets and independently addressed by upstream inputs.SIGNIFICANCE STATEMENT Activity in the medial prefrontal cortex plays a role in representing the current and past states of the world. We show that during a maze task, the activity of a single population in medial prefrontal cortex represents at least two different states of the world. These representations were sequential and sufficiently distinct that a downstream population could separately read out either state from that activity. Moreover, the activity representing different states is differently reactivated in sleep. Different world states can thus be represented in the same medial prefrontal cortex population but in such a way that prevents potentially catastrophic interference between them.

Subjective confidence reflects representation of Bayesian probability in cortex

What gives rise to the human sense of confidence? Here, we tested the Bayesian hypothesis that confidence is based on a probability distribution represented in neural population activity. We implemented several computational models of confidence, and tested their predictions using psychophysics and fMRI. Using a generative model-based fMRI decoding approach, we extracted probability distributions from neural population activity in human visual cortex. We found that subjective confidence tracks the shape of the decoded distribution. That is, when sensory evidence was more precise, as indicated by the decoded distribution, observers reported higher levels of confidence. We furthermore found that neural activity in the insula, anterior cingulate, and prefrontal cortex was linked to both the shape of the decoded distribution and reported confidence, in ways consistent with the Bayesian model. Altogether, our findings support recent statistical theories of confidence and suggest that probabilistic information guides the computation of one’s sense of confidence.

Interactions between ventrolateral prefrontal and anterior cingulate cortex during learning and behavioural change

Hypotheses and beliefs guide credit assignment - the process of determining which previous events or actions caused an outcome. Adaptive hypothesis formation and testing are crucial in uncertain and changing environments in which associations and meanings are volatile. Despite primates' abilities to form and test hypotheses, establishing what is causally responsible for the occurrence of particular outcomes remains a fundamental challenge for credit assignment and learning. Hypotheses about what surprises are due to stochasticity inherent in an environment as opposed to real, systematic changes are necessary for identifying the environment's predictive features, but are often hard to test. We review evidence that two highly interconnected frontal cortical regions, anterior cingulate cortex and ventrolateral prefrontal area 47/12o, provide a biological substrate for linking two crucial components of hypothesis-formation and testing: the control of information seeking and credit assignment. Neuroimaging, targeted disruptions, and neurophysiological studies link an anterior cingulate - 47/12o circuit to generation of exploratory behaviour, non-instrumental information seeking, and interpretation of subsequent feedback in the service of credit assignment. Our observations support the idea that information seeking and credit assignment are linked at the level of neural circuits and explain why this circuit is important for ensuring behaviour is flexible and adaptive.

Computational models of adaptive behavior and prefrontal cortex

The real world is uncertain, and while ever changing, it constantly presents itself in terms of new sets of behavioral options. To attain the flexibility required to tackle these challenges successfully, most mammalian brains are equipped with certain computational abilities that rely on the prefrontal cortex (PFC). By examining learning in terms of internal models associating stimuli, actions, and outcomes, we argue here that adaptive behavior relies on specific interactions between multiple systems including: (1) selective models learning stimulus-action associations through rewards; (2) predictive models learning stimulus- and/or action-outcome associations through statistical inferences anticipating behavioral outcomes; and (3) contextual models learning external cues associated with latent states of the environment. Critically, the PFC combines these internal models by forming task sets to drive behavior and, moreover, constantly evaluates the reliability of actor task sets in predicting external contingencies to switch between task sets or create new ones. We review different models of adaptive behavior to demonstrate how their components map onto this unifying framework and specific PFC regions. Finally, we discuss how our framework may help to better understand the neural computations and the cognitive architecture of PFC regions guiding adaptive behavior.

The population doctrine in cognitive neuroscience

A major shift is happening within neurophysiology: a population doctrine is drawing level with the single-neuron doctrine that has long dominated the field. Population-level ideas have so far had their greatest impact in motor neuroscience, but they hold great promise for resolving open questions in cognition as well. Here, we codify the population doctrine and survey recent work that leverages this view to specifically probe cognition. Our discussion is organized around five core concepts that provide a foundation for population-level thinking: (1) state spaces, (2) manifolds, (3) coding dimensions, (4) subspaces, and (5) dynamics. The work we review illustrates the progress and promise that population-level thinking holds for cognitive neuroscience-for delivering new insight into attention, working memory, decision-making, executive function, learning, and reward processing.

Multisensory-Guided Associative Learning Enhances Multisensory Representation in Primary Auditory Cortex

Sensory cortices, classically considered to represent modality-specific sensory information, are also found to engage in multisensory processing. However, how sensory processing in sensory cortices is cross-modally modulated remains an open question. Specifically, we understand little of cross-modal representation in sensory cortices in perceptual tasks and how perceptual learning modifies this process. Here, we recorded neural responses in primary auditory cortex (A1) both while freely moving rats discriminated stimuli in Go/No-Go tasks and when anesthetized. Our data show that cross-modal representation in auditory cortices varies with task contexts. In the task of an audiovisual cue being the target associating with water reward, a significantly higher proportion of auditory neurons showed a visually evoked response. The vast majority of auditory neurons, if processing auditory-visual interactions, exhibit significant multisensory enhancement. However, when the rats performed tasks with unisensory cues being the target, cross-modal inhibition, rather than enhancement, predominated. In addition, multisensory associational learning appeared to leave a trace of plastic change in A1, as a larger proportion of A1 neurons showed multisensory enhancement in anesthesia. These findings indicate that multisensory processing in principle sensory cortices is not static, and having cross-modal interaction in the task requirement can substantially enhance multisensory processing in sensory cortices.

Dichotomous dopaminergic and noradrenergic neural states mediate distinct aspects of exploitative behavioral states

The balance between exploiting known actions and exploring alternatives is critical for survival and hypothesized to rely on shifts in neuromodulation. We developed a behavioral paradigm to capture exploitative and exploratory states and imaged calcium dynamics in genetically identified dopaminergic and noradrenergic neurons. During exploitative states, characterized by motivated repetition of the same action choice, dopamine neurons in SNc encoding movement vigor showed sustained elevation of basal activity that lasted many seconds. This sustained activity emerged from longer positive responses, which accumulated during exploitative action-reward bouts, and hysteretic dynamics. Conversely, noradrenergic neurons in LC showed sustained inhibition of basal activity due to the accumulation of longer negative responses in LC. Chemogenetic manipulation of these sustained dynamics revealed that dopaminergic activity mediates action drive, whereas noradrenergic activity modulates choice diversity. These data uncover the emergence of sustained neural states in dopaminergic and noradrenergic networks that mediate dissociable aspects of exploitative bouts.

Spontaneous activity competes with externally evoked responses in sensory cortex

The interaction between spontaneous and externally evoked neuronal activity is fundamental for a functional brain. Increasing evidence suggests that bursts of high-power oscillations in the 15- to 30-Hz beta-band represent activation of internally generated events and mask perception of external cues. Yet demonstration of the effect of beta-power modulation on perception in real time is missing, and little is known about the underlying mechanism. Here, we used a closed-loop stimulus-intensity adjustment system based on online burst-occupancy analyses in rats involved in a forepaw vibrotactile detection task. We found that the masking influence of burst occupancy on perception can be counterbalanced in real time by adjusting the vibration amplitude. Offline analysis of firing rates (FRs) and local field potentials across cortical layers and frequency bands confirmed that beta-power in the somatosensory cortex anticorrelated with sensory evoked responses. Mechanistically, bursts in all bands were accompanied by transient synchronization of cell assemblies, but only beta-bursts were followed by a reduction of FR. Our closed loop approach reveals that spontaneous beta-bursts reflect a dynamic state that competes with external stimuli.

Mouse prefrontal cortex represents learned rules for categorization

The ability to categorize sensory stimuli is crucial for an animal’s survival in a complex environment. Memorizing categories instead of individual exemplars enables greater behavioural flexibility and is computationally advantageous. Neurons that show category selectivity have been found in several areas of the mammalian neocortex^1–4, but the prefrontal cortex seems to have a prominent role^4,5 in this context. Specifically, in primates that are extensively trained on a categorization task, neurons in the prefrontal cortex rapidly and flexibly represent learned categories^6,7. However, how these representations first emerge in naive animals remains unexplored, leaving it unclear whether flexible representations are gradually built up as part of semantic memory or assigned more or less instantly during task execution^8,9. Here we investigate the formation of a neuronal category representation throughout the entire learning process by repeatedly imaging individual cells in the mouse medial prefrontal cortex. We show that mice readily learn rule-based categorization and generalize to novel stimuli. Over the course of learning, neurons in the prefrontal cortex display distinct dynamics in acquiring category selectivity and are differentially engaged during a later switch in rules. A subset of neurons selectively and uniquely respond to categories and reflect generalization behaviour. Thus, a category representation in the mouse prefrontal cortex is gradually acquired during learning rather than recruited ad hoc. This gradual process suggests that neurons in the medial prefrontal cortex are part of a specific semantic memory for visual categories.

Neurons in the mouse medial prefrontal cortex acquire category-selective responses with learning.

The anterior cingulate cortex directs exploration of alternative strategies

The ability to adjust one's behavioral strategy in complex environments is at the core of cognition. Doing so efficiently requires monitoring the reliability of the ongoing strategy and, when appropriate, switching away from it to evaluate alternatives. Studies in humans and non-human primates have uncovered signals in the anterior cingulate cortex (ACC) that reflect the pressure to switch away from the ongoing strategy, whereas other ACC signals relate to the pursuit of alternatives. However, whether these signals underlie computations that actually underpin strategy switching or merely reflect tracking of related variables remains unclear. Here we provide causal evidence that the rodent ACC actively arbitrates between persisting with the ongoing behavioral strategy and temporarily switching away to re-evaluate alternatives. Furthermore, by individually perturbing distinct output pathways, we establish that the two associated computations-determining whether to switch strategy and committing to the pursuit of a specific alternative-are segregated in the ACC microcircuitry.

Coordinated Prefrontal State Transition Leads Extinction of Reward-Seeking Behaviors

Extinction learning suppresses conditioned reward responses and is thus fundamental to adapt to changing environmental demands and to control excessive reward seeking. The medial prefrontal cortex (mPFC) monitors and controls conditioned reward responses. Abrupt transitions in mPFC activity anticipate changes in conditioned responses to altered contingencies. It remains, however, unknown whether such transitions are driven by the extinction of old behavioral strategies or by the acquisition of new competing ones. Using in vivo multiple single-unit recordings of mPFC in male rats, we studied the relationship between single-unit and population dynamics during extinction learning, using alcohol as a positive reinforcer in an operant conditioning paradigm. To examine the fine temporal relation between neural activity and behavior, we developed a novel behavioral model that allowed us to identify the number, onset, and duration of extinction-learning episodes in the behavior of each animal. We found that single-unit responses to conditioned stimuli changed even under stable experimental conditions and behavior. However, when behavioral responses to task contingencies had to be updated, unit-specific modulations became coordinated across the whole population, pushing the network into a new stable attractor state. Thus, extinction learning is not associated with suppressed mPFC responses to conditioned stimuli, but is anticipated by single-unit coordination into population-wide transitions of the internal state of the animal.SIGNIFICANCE STATEMENT The ability to suppress conditioned behaviors when no longer beneficial is fundamental for the survival of any organism. While pharmacological and optogenetic interventions have shown a critical involvement of the mPFC in the suppression of conditioned responses, the neural dynamics underlying such a process are still largely unknown. Combining novel analysis tools to describe behavior, single-neuron response, and population activity, we found that widespread changes in neuronal firing temporally coordinate across the whole mPFC population in anticipation of behavioral extinction. This coordination leads to a global transition in the internal state of the network, driving extinction of conditioned behavior.

Multiple time-scales of decision-making in the hippocampus and prefrontal cortex

The prefrontal cortex and hippocampus are crucial for memory-guided decision-making. Neural activity in the hippocampus exhibits place-cell sequences at multiple timescales, including slow behavioral sequences (~seconds) and fast theta sequences (~100-200 ms) within theta oscillation cycles. How prefrontal ensembles interact with hippocampal sequences to support decision-making is unclear. Here, we examined simultaneous hippocampal and prefrontal ensemble activity in rats during learning of a spatial working-memory decision task. We found clear theta sequences in prefrontal cortex, nested within its behavioral sequences. In both regions, behavioral sequences maintained representations of current choices during navigation. In contrast, hippocampal theta sequences encoded alternatives for deliberation and were coordinated with prefrontal theta sequences that predicted upcoming choices. During error trials, these representations were preserved to guide ongoing behavior, whereas replay sequences during inter-trial periods were impaired prior to navigation. These results establish cooperative interaction between hippocampal and prefrontal sequences at multiple timescales for memory-guided decision-making.

A salience misattribution model for addictive-like behaviors

Adapting to the changing environment is a key component of optimal decision-making. Internal-models that accurately represent and selectively update from behaviorally relevant/salient stimuli may facilitate adaptive behaviors. Anterior cingulate cortex (ACC) and dopaminergic systems may produce these adaptive internal-models through selective updates from behaviorally relevant stimuli. Dysfunction of ACC and dopaminergic systems could therefore produce misaligned internal-models where updates are disproportionate to the salience of the cues. An aspect of addictive-like behaviors is reduced adaptation and, ACC and dopaminergic systems typically exhibit dysfunction in drug-dependents. We argue that ACC and dopaminergic dysfunction in dependents may produce misaligned internal-models such that drug-related stimuli are misattributed with a higher salience compared to non-drug related stimuli. Hence, drug-related rewarding stimuli generate over-weighted updates to the internal-model, while negative feedback and non-drug related rewarding stimuli generate down-weighted updates. This misaligned internal-model may therefore incorrectly reinforce maladaptive drug-related behaviors. We use the proposed framework to discuss ways behavior may be made more adaptive and how the framework may be supported or falsified experimentally.

The Best Laid Plans: Computational Principles of Anterior Cingulate Cortex

Despite continual debate for the past 30 years about the function of anterior cingulate cortex (ACC), its key contribution to neurocognition remains unknown. However, recent computational modeling work has provided insight into this question. Here we review computational models that illustrate three core principles of ACC function, related to hierarchy, world models, and cost. We also discuss four constraints on the neural implementation of these principles, related to modularity, binding, encoding, and learning and regulation. These observations suggest a role for ACC in hierarchical model-based hierarchical reinforcement learning (HMB-HRL), which instantiates a mechanism motivating the execution of high-level plans.