Simultaneous oscillatory encoding of "hot" and "cold" information during social interactions in the monkey medial prefrontal cortex.

Social interactions in primates require social cognition abilities such as anticipating the partner's future choices as well as pure cognitive skills involving processing task-relevant information. The medial prefrontal cortex (mPFC) has been implicated in these cognitive processes. Here, we investigated the neural oscillations underlying the complex social behaviors involving the interplay of social roles (Actor vs. Observer) and interaction types (whether working with a "Good" or "Bad" partner). We found opposite power modulations of the beta and gamma bands by social roles, indicating dedicated processing for task-related information. Concurrently, the interaction type was conveyed by lower frequencies, which are commonly associated with neural circuits linked to performance and reward monitoring. Thus, the mPFC exhibits parallel coding of both "cold" processes (purely cognitive) and "hot" processes (reward and social-related). This allocation of neural resources gives the mPFC a key neural node, flexibly integrating multiple sources of information during social interactions.

Neurons in the monkey frontopolar cortex encode learning stage and goal during a fast learning task.

The frontopolar cortex (FPC) is, to date, one of the least understood regions of the prefrontal cortex. The current understanding of its function suggests that it plays a role in the control of exploratory behaviors by coordinating the activities of other prefrontal cortex areas involved in decision-making and exploiting actions based on their outcomes. Based on this hypothesis, FPC would drive fast-learning processes through a valuation of the different alternatives. In our study, we used a modified version of a well-known paradigm, the object-in-place (OIP) task, to test this hypothesis in electrophysiology. This paradigm is designed to maximize learning, enabling monkeys to learn in one trial, which is an ability specifically impaired after a lesion of the FPC. We showed that FPC neurons presented an extremely specific pattern of activity by representing the learning stage, exploration versus exploitation, and the goal of the action. However, our results do not support the hypothesis that neurons in the frontal pole compute an evaluation of different alternatives. Indeed, the position of the chosen target was strongly encoded at its acquisition, but the position of the unchosen target was not. Once learned, this representation was also found at the problem presentation, suggesting a monitoring activity of the synthetic goal preceding its acquisition. Our results highlight important features of FPC neurons in fast-learning processes without confirming their role in the disengagement of cognitive control from the current goals.

Connecting the dots in the zona incerta: A study of neural assemblies and motifs of inter-area coordination in mice.

The zona incerta (ZI), a subthalamic area connected to numerous brain regions, has raised clinical interest because its stimulation alleviates the motor symptoms of Parkinson's disease. To explore its coordinative nature, we studied the assembly formation in a dataset of neural recordings in mice and quantified the degree of functional coordination of ZI with other 24 brain areas. We found that the ZI is a highly integrative area. The analysis in terms of "loop-like" motifs, directional assemblies composed of three neurons spanning two areas, has revealed reciprocal functional interactions with reentrant signals that, in most cases, start and end with the activation of ZI units. In support of its proposed integrative role, we found that almost one-third of the ZI's neurons formed assemblies with more than half of the other recorded areas and that loop-like assemblies may stand out as hyper-integrative motifs compared to other types of activation patterns.

Disentangling the identity of the zona incerta: a review of the known connections and latest implications.

The zona incerta (ZI) is a subthalamic region composed by loosely packed neurochemically mixed neurons, juxtaposed to the main ascending and descending bundles. The extreme neurochemical diversity that characterizes this area, together with the diffuseness of its connections with the entire neuraxis and its hard-to-reach positioning in the brain caused the ZI to keep its halo of mystery for over a century. However, in the last decades, a rich albeit fragmentary body of knowledge regarding both the incertal anatomical connections and functional implications has been built mostly based on rodent studies and its lack of cohesion makes difficult to depict an integrated, exhaustive picture regarding the ZI and its roles. This review aims to provide a unified resource that summarizes the current knowledge regarding the anatomical profile of interactions of the ZI in rodents and non-human primates and the functional significance of its connections, highlighting the aspects still unbeknown to research.

Static and dynamic coding in distinct cell types during associative learning in the prefrontal cortex.

The prefrontal cortex maintains information in memory through static or dynamic population codes depending on task demands, but whether the population coding schemes used are learning-dependent and differ between cell types is currently unknown. We investigate the population coding properties and temporal stability of neurons recorded from male macaques in two mapping tasks during and after stimulus-response associative learning, and then we use a Strategy task with the same stimuli and responses as control. We identify a heterogeneous population coding for stimuli, responses, and novel associations: static for putative pyramidal cells and dynamic for putative interneurons that show the strongest selectivity for all the variables. The population coding of learned associations shows overall the highest stability driven by cell types, with interneurons changing from dynamic to static coding after successful learning. The results support that prefrontal microcircuitry expresses mixed population coding governed by cell types and changes its stability during associative learning.

Effects of contraction bias on the decision process in the macaque prefrontal cortex.

Our representation of magnitudes such as time, distance, and size is not always veridical because it is affected by multiple biases. From a Bayesian perspective, estimation errors are considered to be the result of an optimization mechanism for the behavior in a noisy environment by integrating previous experience with the incoming sensory information. One influence of the distribution of past stimuli on perceptual decisions is represented by the regression toward the mean, a type of contraction bias. Using a spatial discrimination task with 2 stimuli presented sequentially at different distances from the center, we show that this bias is also present in macaques when comparing the magnitude of 2 distances. We found that the contraction of the first stimulus magnitude toward the center of the distribution accounted for some of the changes in performance, even more so than the effect of difficulty related to the ratio between stimulus magnitudes. At the neural level in the dorsolateral prefrontal cortex, the coding of the decision after the presentation of the second stimulus reflected the effect of the contraction bias on the discriminability of the stimuli at the behavioral level.

Social monitoring of actions in the macaque frontopolar cortex.

The frontopolar cortex (FPC) of primates appeared as a main innovation in the evolution of anthropoid primates and it has been placed at the top of the prefrontal hierarchy. The only study to date that investigated the activity of FPC neurons in monkeys performing a cognitive task suggested that these cells were involved in the monitoring of self-generated actions. We recorded the activity of neurons in the FPCs of two rhesus monkeys while they performed a social variant of a nonmatch-to-goal task that required monitoring the actions of a human or computer agent. We discovered that the role of FPC neurons extends beyond self-generated actions to include monitoring others' actions. Their monitoring activity was very specific. First, neurons in the FPC encoded the spatial position of the target but not its object features. Second, a dedicated representation of the human agent actions was tied to the time of target acquisition, while it was reduced or absent in the successive epochs of the trial. Finally, this other-specific neural substrate did not emerge during the interaction with a virtual agent such as the computer. These results provide a new perspective on the functions of a uniquely primate brain area, suggesting that FPC might play an important role in social behaviors.

Neural representation of others during action observation in posterior medial prefrontal cortex.

Making decisions based on the actions of others is critical to daily interpersonal interactions. We investigated the representations of other's actions at single neural level in posterior medial prefrontal cortex (pmPFC) in two monkeys during the observation of actions of another agent, in a social interaction task. Each monkey separately interacted with a human partner. The monkey and the human alternated turns as actor and observer. The actor was required to reach one of two visual targets, avoiding the previously chosen target, while the observer monitored that action. pmPFC neurons decoupled in most cases self from others during both the execution and the observation of explicit actions. pmPFC neurons showed selective directional tuning specific for the agent who was executing the task. Moreover, we assessed the relationship of the response coding between the periods immediately before and after the action, by using a cross-modal decoding analysis. We found neural network stability from the action anticipation period to the observation of other's actions, suggesting a strong relationship between the anticipation and the execution of an action. When the monkey was the actor, the population coding appeared dynamic, possibly reflecting a goal-action transformation unique to the monkey's own action execution.

Dedicated Representation of Others in the Macaque Frontal Cortex: From Action Monitoring and Prediction to Outcome Evaluation.

Social neurophysiology has increasingly addressed how several aspects of self and other are distinctly represented in the brain. In social interactions, the self-other distinction is fundamental for discriminating one's own actions, intentions, and outcomes from those that originate in the external world. In this paper, we review neurophysiological experiments using nonhuman primates that shed light on the importance of the self-other distinction, focusing mainly on the frontal cortex. We start by examining how the findings are impacted by the experimental paradigms that are used, such as the type of social partner or whether a passive or active interaction is required. Next, we describe the 2 sociocognitive systems: mirror and mentalizing. Finally, we discuss how the self-other distinction can occur in different domains to process different aspects of social information: the observation and prediction of others' actions and the monitoring of others' rewards.

Intrinsic timescales across the basal ganglia.

Recent studies have shown that temporal stability of the neuronal activity over time can be estimated by the structure of the spike-count autocorrelation of neuronal populations. This estimation, called the intrinsic timescale, has been computed for several cortical areas and can be used to propose a cortical hierarchy reflecting a scale of temporal receptive windows between areas. In this study, we performed an autocorrelation analysis on neuronal populations of three basal ganglia (BG) nuclei, including the striatum and the subthalamic nucleus (STN), the input structures of the BG, and the external globus pallidus (GPe). The analysis was performed during the baseline period of a motivational visuomotor task in which monkeys had to apply different amounts of force to receive different amounts of reward. We found that the striatum and the STN have longer intrinsic timescales than the GPe. Moreover, our results allow for the placement of these subcortical structures within the already-defined scale of cortical temporal receptive windows. Estimates of intrinsic timescales are important in adding further constraints in the development of computational models of the complex dynamics among these nuclei and throughout cortico-BG-thalamo-cortical loops.

The importance of urgency in decision making based on dynamic information.

A standard view in the literature is that decisions are the result of a process that accumulates evidence in favor of each alternative until such accumulation reaches a threshold and a decision is made. However, this view has been recently questioned by an alternative proposal that suggests that, instead of accumulated, evidence is combined with an urgency signal. Both theories have been mathematically formalized and supported by a variety of decision-making tasks with constant information. However, recently, tasks with changing information have shown to be more effective to study the dynamics of decision making. Recent research using one of such tasks, the tokens task, has shown that decisions are better described by an urgency mechanism than by an accumulation one. However, the results of that study could depend on a task where all fundamental information was noiseless and always present, favoring a mechanism of non-integration, such as the urgency one. Here, we wanted to address whether the same conclusions were also supported by an experimental paradigm in which sensory evidence was removed shortly after it was provided, making working memory necessary to properly perform the task. Here, we show that, under such condition, participants' behavior could be explained by an urgency-gating mechanism that low-pass filters the mnemonic information and combines it with an urgency signal that grows with time but not by an accumulation process that integrates the same mnemonic information. Thus, our study supports the idea that, under certain situations with dynamic sensory information, decisions are better explained by an urgency-gating mechanism than by an accumulation one.

Separable neuronal contributions to covertly attended locations and movement goals in macaque frontal cortex.

We investigated the spatial representation of covert attention and movement planning in monkeys performing a task that used symbolic cues to decouple the locus of covert attention from the motor target. In the three frontal areas studied, most spatially tuned neurons reflected either where attention was allocated or the planned saccade. Neurons modulated by both covert attention and the motor plan were in the minority. Such dual-purpose neurons were especially rare in premotor and prefrontal cortex but were more common just rostral to the arcuate sulcus. The existence of neurons that indicate where the monkey was attending but not its movement goal runs counter to the idea that the control of spatial attention is entirely reliant on the neuronal circuits underlying motor planning. Rather, the presence of separate neuronal populations for each cognitive process suggests that endogenous attention is under flexible control and can be dissociated from motor intention.

Slower prefrontal metastable dynamics during deliberation predicts error trials in a distance discrimination task.

Cortical activity related to erroneous behavior in discrimination or decision-making tasks is rarely analyzed, yet it can help clarify which computations are essential during a specific task. Here, we use a hidden Markov model (HMM) to perform a trial-by-trial analysis of the ensemble activity of dorsolateral prefrontal cortex (PFdl) neurons of rhesus monkeys performing a distance discrimination task. By segmenting the neural activity into sequences of metastable states, HMM allows us to uncover modulations of the neural dynamics related to internal computations. We find that metastable dynamics slow down during error trials, while state transitions at a pivotal point during the trial take longer in difficult correct trials. Both these phenomena occur during the decision interval, with errors occurring in both easy and difficult trials. Our results provide further support for the emerging role of metastable cortical dynamics in mediating complex cognitive functions and behavior.

Macaque monkeys learn and perform a non-match-to-goal task using an automated home cage training procedure.

In neurophysiology, nonhuman primates represent an important model for studying the brain. Typically, monkeys are moved from their home cage to an experimental room daily, where they sit in a primate chair and interact with electronic devices. Refining this procedure would make the researchers' work easier and improve the animals' welfare. To address this issue, we used home-cage training to train two macaque monkeys in a non-match-to-goal task, where each trial required a switch from the choice made in the previous trial to obtain a reward. The monkeys were tested in two versions of the task, one in which they acted as the agent in every trial and one in which some trials were completed by a "ghost agent". We evaluated their involvement in terms of their performance and their interaction with the apparatus. Both monkeys were able to maintain a constant involvement in the task with good, stable performance within sessions in both versions of the task. Our study confirms the feasibility of home-cage training and demonstrates that even with challenging tasks, monkeys can complete a large number of trials at a high performance level, which is a prerequisite for electrophysiological studies of monkey behavior.

Low-dimensional dynamics for working memory and time encoding.

Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences and, simultaneously, estimate the timing between events. To understand the mechanisms underlying working memory and time encoding, we analyze neural activity recorded during delays in four experiments on nonhuman primates. To disambiguate potential mechanisms, we propose two analyses, namely, decoding the passage of time from neural data and computing the cumulative dimensionality of the neural trajectory over time. Time can be decoded with high precision in tasks where timing information is relevant and with lower precision when irrelevant for performing the task. Neural trajectories are always observed to be low-dimensional. In addition, our results further constrain the mechanisms underlying time encoding as we find that the linear "ramping" component of each neuron's firing rate strongly contributes to the slow timescale variations that make decoding time possible. These constraints rule out working memory models that rely on constant, sustained activity and neural networks with high-dimensional trajectories, like reservoir networks. Instead, recurrent networks trained with backpropagation capture the time-encoding properties and the dimensionality observed in the data.

Dorsal Premotor Cortex Neurons Signal the Level of Choice Difficulty during Logical Decisions.

Studies on the neuronal correlates of decision making have demonstrated that the continuous flow of sensorial information is integrated by sensorimotor brain areas in order to select one among simultaneously represented targets and potential actions. In contrast, little is known about how these areas integrate memory information to lead to similar decisions. Using serial order learning, we explore how fragments of information, learned and stored independently (e.g., A > B and B > C), are linked in an abstract representation according to their reciprocal relations (such as A > B > C) and how this representation can be accessed and manipulated to make decisions. We show that manipulating information after learning occurs with increased difficulty as logical relationships get closer in the mental map and that the activity of neurons in the dorsal premotor cortex (PMd) encodes the difficulty level during target selection for motor decision making at the single-neuron and population levels.

Neural Correlates of Strategy Switching in the Macaque Orbital Prefrontal Cortex.

We can adapt flexibly to environment changes and search for the most appropriate rule to a context. The orbital prefrontal cortex (PFo) has been associated with decision making, rule generation and maintenance, and more generally has been considered important for behavioral flexibility. To better understand the neural mechanisms underlying the flexible behavior, we studied the ability to generate a switching signal in monkey PFo when a strategy is changed. In the strategy task, we used a visual cue to instruct two male rhesus monkeys either to repeat their most recent choice (i.e., stay strategy) or to change it (i.e., shift strategy). To identify the strategy switching-related signal, we compared nonswitch and switch trials, which cued the same or a different strategy from the previous trial, respectively. We found that the switching-related signal emerged during the cue presentation and it was combined with the strategy signal in a subpopulation of cells. Moreover, the error analysis showed that the activity of the switch-related cells reflected whether the monkeys erroneously switched or not the strategy, rather than what was required for that trial. The function of the switching signal could be to prompt the use of different strategies when older strategies are no longer appropriate, conferring the ability to adapt flexibly to environmental changes. In our task, the switching signal might contribute to the implementation of the strategy cued, overcoming potential interference effects from the strategy previously cued. Our results support the idea that ascribes to PFo an important role for behavioral flexibility.SIGNIFICANCE STATEMENT We can flexibly adapt our behavior to a changing environment. One of the prefrontal areas traditionally associated with the ability to adapt to new contingencies is the orbital prefrontal cortex (PFo). We analyzed the switching related activity using a strategy task in which two rhesus monkeys were instructed by a visual cue either to repeat or change their most recent choice, respectively using a stay or a shift strategy. We found that PFo neurons were modulated by the strategy switching signal, pointing to the importance of PFo in behavioral flexibility by generating control over the switching of strategies.

Hidden Markov Models Predict the Future Choice Better Than a PSTH-Based Method.

Beyond average firing rate, other measurable signals of neuronal activity are fundamental to an understanding of behavior. Recently, hidden Markov models (HMMs) have been applied to neural recordings and have described how neuronal ensembles process information by going through sequences of different states. Such collective dynamics are impossible to capture by just looking at the average firing rate. To estimate how well HMMs can decode information contained in single trials, we compared HMMs with a recently developed classification method based on the peristimulus time histogram (PSTH). The accuracy of the two methods was tested by using the activity of prefrontal neurons recorded while two monkeys were engaged in a strategy task. In this task, the monkeys had to select one of three spatial targets based on an instruction cue and on their previous choice. We show that by using the single trial's neural activity in a period preceding action execution, both models were able to classify the monkeys' choice with an accuracy higher than by chance. Moreover, the HMM was significantly more accurate than the PSTH-based method, even in cases in which the HMM performance was low, although always above chance. Furthermore, the accuracy of both methods was related to the number of neurons exhibiting spatial selectivity within an experimental session. Overall, our study shows that neural activity is better described when not only the mean activity of individual neurons is considered and that therefore, the study of other signals rather than only the average firing rate is fundamental to an understanding of the dynamics of neuronal ensembles.

A Network Activity Reconfiguration Underlies the Transition from Goal to Action.

Neurons in prefrontal cortex (PF) represent mnemonic information about current goals until the action can be selected and executed. However, the neuronal dynamics underlying the transition from goal into specific actions are poorly understood. Here, we show that the goal-coding PF network is dynamically reconfigured from mnemonic to action selection states and that such reconfiguration is mediated by cell assemblies with heterogeneous excitability. We recorded neuronal activity from PF while monkeys selected their actions on the basis of memorized goals. Many PF neurons encoded the goal, but only a minority of them did so across both memory retention and action selection stages. Interestingly, about half of this minority of neurons switched their goal preference across the goal-action transition. Our computational model led us to propose a PF network composed of heterogeneous cell assemblies with single-state and bistable local dynamics able to produce both dynamical stability and input susceptibility simultaneously.

Effects of reward size and context on learning in macaque monkeys.

The outcome of an action plays a crucial role in decision-making and reinforcement learning processes. Indeed, both human and animal behavioural studies have shown that different expected reward values, either quantitatively or qualitatively, modulate the motivation of subjects to perform an action and, as a consequence, affect their behavioural performance. Here, we investigated the effect of different amounts of reward on the learning of macaque monkeys using a modified version of the object-in-place task. This task offers the opportunity to shape rapid learning based on a set of external stimuli that enhance an animal's accuracy in terms of solving a problem. We compared the learning of three monkeys among three different reward conditions. Our results demonstrate that the larger the reward, the better the monkey's ability to learn the associations starting with the second presentation of the problem. Moreover, we compared the present results with those of our previous work using the same monkeys in the same task but with a unique reward condition, the intermediate one. Interestingly, the performance of our animals in our previous work matched with their performance in the largest and not intermediate reward condition of the present study These results suggest that learning is mostly influenced by the reward context and not by its absolute value.