Neural correlates of a decision variable before learning to perform a match/non-match task

Decoding working memory information from neurons with and without persistent activity in the primate prefrontal cortex

Persistent activity of neurons in the prefrontal cortex has been thought to represent the information maintained in working memory, though alternative models have challenged this idea. Theories that depend on the dynamic representation of information posit that stimulus information may be maintained by the activity pattern of neurons whose firing rate is not significantly elevated above their baseline during the delay period of working memory tasks. We thus tested the ability of neurons that do and do not generate persistent activity in the prefrontal cortex of monkeys to represent spatial and object information in working memory. Neurons that generated persistent activity represented more information about the stimuli in both spatial and object working memory tasks. The amount of information that could be decoded from neural activity depended on the choice of decoder and parameters used but neurons with persistent activity outperformed non-persistent neurons consistently. Averaged across all neurons and stimuli, the firing rate did not appear clearly elevated above baseline during the maintenance of neural activity particularly for object working memory; however, this grand average masked neurons that generated persistent activity selective for their preferred stimuli, which carried the majority of stimulus information. These results reveal that prefrontal neurons that generate persistent activity maintain information more reliably during working memory.NEW & NOTEWORTHY Competing theories suggest that neurons that generate persistent activity or do not are primarily responsible for the maintenance of information, particularly regarding object working memory. Although the two models have been debated on theoretical terms, direct comparison of empirical results has been lacking. Analysis of neural activity in a large database of prefrontal recordings revealed that neurons that generate persistent activity were primarily responsible for the maintenance of both spatial and object working memory.

Decoding working memory information from persistent and activity-silent neurons in the primate prefrontal cortex

Persistent activity of neurons in the prefrontal cortex has been thought to represent the information maintained in working memory, though alternative models have recently challenged this idea. Activity-silent theories posit that stimulus information may be maintained by the activity pattern of neurons that do not produce firing rate significantly elevated about their baseline during the delay period of working memory tasks. We thus tested the ability of neurons that do and do not generate persistent activity in the prefrontal cortex of monkeys to represent spatial and object information in working memory. Neurons that generated persistent activity represented more information about the stimuli in both spatial and object working memory tasks. The amount of information that could be decoded from neural activity depended on the choice of decoder and parameters used but neurons with persistent activity outperformed non-persistent neurons consistently. Although averaged across all neurons and stimuli, firing rate did not appear clearly elevated above baseline during the maintenance of neural activity particularly for object working memory, this grant average masked neurons that generated persistent activity selective for their preferred stimuli, which carried the majority of information about the stimulus identity. These results reveal that prefrontal neurons with generate persistent activity constitute the primary mechanism of working memory maintenance in the cortex.

NEW AND NOTEWORTHY

Competing theories suggest that neurons that generate persistent activity or do not are primarily responsible for the maintenance of information, particularly regarding object working memory. While the two models have been debated on theoretical terms, direct comparison of empirical results have been lacking. Analysis of neural activity in a large database of prefrontal recordings revealed that neurons that generate persistent activity were primarily responsible for the maintenance of both spatial and object working memory.

Neuronal selectivity for stimulus information determines prefrontal LFP gamma power regardless of task execution

Local field potential (LFP) power in the gamma frequency is modulated by cognitive variables during task execution. We sought to examine whether such modulations only emerge when task rules are established. We therefore analyzed neuronal firing and LFPs in different prefrontal subdivisions before and after the same monkeys were trained to perform cognitive tasks. Prior to task rule learning, sites containing neurons selective for stimuli already exhibited increased gamma power during and after the passive viewing of stimuli compared to the baseline period. Unexpectedly, when the same monkeys learned to maintain these stimuli in working memory, the elevation of gamma power above the baseline was diminished, despite an overall increase in firing rate. Learning and executing the task further decoupled LFP power from single neuron firing. Gamma power decreased at the time when subjects needed to make a judgment about whether two stimuli were the same or not, and differential gamma power was observed for matching and nonmatching stimuli. Our results indicate that prefrontal gamma power emerges spontaneously, not necessarily tied to a cognitive task being executed.

Prefrontal cortical plasticity during learning of cognitive tasks

Training in working memory tasks is associated with lasting changes in prefrontal cortical activity. To assess the neural activity changes induced by training, we recorded single units, multi-unit activity (MUA) and local field potentials (LFP) with chronic electrode arrays implanted in the prefrontal cortex of two monkeys, throughout the period they were trained to perform cognitive tasks. Mastering different task phases was associated with distinct changes in neural activity, which included recruitment of larger numbers of neurons, increases or decreases of their firing rate, changes in the correlation structure between neurons, and redistribution of power across LFP frequency bands. In every training phase, changes induced by the actively learned task were also observed in a control task, which remained the same across the training period. Our results reveal how learning to perform cognitive tasks induces plasticity of prefrontal cortical activity, and how activity changes may generalize between tasks.

Working Memory: From Neural Activity to the Sentient Mind

Working memory (WM) is the ability to maintain and manipulate information in the conscious mind over a timescale of seconds. This ability is thought to be maintained through the persistent discharges of neurons in a network of brain areas centered on the prefrontal cortex, as evidenced by neurophysiological recordings in nonhuman primates, though both the localization and the neural basis of WM has been a matter of debate in recent years. Neural correlates of WM are evident in species other than primates, including rodents and corvids. A specialized network of excitatory and inhibitory neurons, aided by neuromodulatory influences of dopamine, is critical for the maintenance of neuronal activity. Limitations in WM capacity and duration, as well as its enhancement during development, can be attributed to properties of neural activity and circuits. Changes in these factors can be observed through training-induced improvements and in pathological impairments. WM thus provides a prototypical cognitive function whose properties can be tied to the spiking activity of brain neurons. © 2021 American Physiological Society. Compr Physiol 11:1-41, 2021.

Task-specific modulation of PFC activity for matching-rule governed decision-making

Storing information from incoming stimuli in working memory (WM) is essential for decision-making. The prefrontal cortex (PFC) plays a key role to support this process. Previous studies have characterized different neuronal populations in the PFC for working memory judgements based on whether an originally presented stimulus matches a subsequently presented one (matching-rule decision-making). However, much remains to be understood about this mechanism at the population level of PFC neurons. Here, we hypothesized differences in processing of feature vs. spatial WM within the PFC during a matching-rule decision-making task. To test this hypothesis, the modulation of neural activity within the PFC during two types of decision-making tasks (spatial WM and feature WM) in comparison to a passive fixation task was determined. We discovered that neural population-level activity within the PFC is different for the match vs. non-match condition exclusively in the case of the feature-specific decision-making task. For this task, the non-match condition exhibited a greater firing rate and lower trial-to-trial variability in spike count compared to the feature-match condition. Furthermore, the feature-match condition exhibited lower variability compared to the spatial-match condition. This was accompanied by a faster behavioral response time for the feature-match compared to the spatial-match WM task. We attribute this lower across-trial spiking variability and behavioral response time to a higher task-relevant attentional level in the feature WM compared to the spatial WM task. The findings support our hypothesis for task-specific differences in the processing of feature vs. spatial WM within the PFC. This also confirms the general conclusion that PFC neurons play an important role during the process of matching-rule governed decision-making.

Working memory training effects across the lifespan: Evidence from human and experimental animal studies

Working memory refers to a cognitive function that provides temporary storage and manipulation of the information necessary for complex cognitive tasks. Due to its central role in general cognition, several studies have investigated the possibility that training on working memory tasks could improve not only working memory function but also increase other cognitive abilities or modulate other behaviors. This possibility is still highly controversial, with prior studies providing contradictory findings. The lack of systematic approaches and methodological shortcomings complicates this debate even more. This review highlights the impact of working memory training at different ages on humans. Finally, it demonstrates several findings about the neural substrate of training in both humans and experimental animals, including non-human primates and rodents.

Probing the early phase of rapid instructed rule encoding

Humans can rapidly convert instructions about a rule into functional neural structures used to apply the rule. The early stages of this encoding process are poorly understood. We designed a stimulus-response (SR) task in which participants were first shown a SR rule on a screen for 200 ms, and then had to apply it to a test stimulus T, which either matched the S in the rule (SR trial) or not (catch trial). To investigate the early stages of rule encoding, the delay between the end of rule display and the onset of the test stimulus was manipulated and chosen between values of 50 ms to 1300 ms. Participants conducted three sessions of 288 trials each, separated by a median of 9 h. Random sequences of 20 rules were used. We then analysed the reaction times and the types of errors made by participants in the different conditions. The analysis of practice effects in session 1 suggests that the neural networks that process SR and catch trials are at least partially distinct, and improve separately during the practice of respectively SR and catch trials. The rule-encoding process, however, is common to both tasks and improves with the number of trials, irrespective of the trial type. Rule encoding shows interesting dynamic properties that last for 500 ms after the end of the stimulus presentation. The encoding process increases the response time in a non-stochastic way, simply adding a reaction time cost to all responses. The rule-retrieval system is functional before the encoding has stabilized, as early as 50 ms after the end of SR rule presentation, with low response errors. It is sensitive to masking however, producing errors with brief (100 ms) test stimulus presentations. Once encoding has stabilized, the sensitivity to masking disappears. It is suggested that participants do encode rules as a parametrized function, using the same neural encoding structure for each trial, rather than reconfiguring their brain anew for each new SR rule. This structure would have been implemented from instructions received prior to the experiment, by using a library of neural functions available in the brain. The observed errors are consistent with this view.

Working memory capacity is enhanced by distributed prefrontal activation and invariant temporal dynamics

The amount of information that can be stored in working memory is limited but may be improved with practice. The basis of improved efficiency at the level of neural activity is unknown. To investigate this question, we trained monkeys to perform a working memory task that required memory for multiple stimuli. Performance decreased as a function of number of stimuli to be remembered, but improved as the animals practiced the task. Neuronal recordings acquired during this training revealed two hitherto unknown mechanisms of working memory capacity improvement. First, more prefrontal neurons became active as working memory improved, but their baseline activity decreased. Second, improved working memory capacity was characterized by less variable temporal dynamics, resulting in a more consistent firing rate at each time point during the course of a trial. Our results reveal that improved performance of working memory tasks is achieved through more distributed activation and invariant neuronal dynamics.

Anterior-posterior gradient of plasticity in primate prefrontal cortex

The functional organization of the primate prefrontal cortex has been a matter of debate with some models speculating dorso-ventral and rostro-caudal specialization while others suggesting that information is represented dynamically by virtue of plasticity across the entire prefrontal cortex. To address functional properties and capacity for plasticity, we recorded from different prefrontal sub-regions and analyzed changes in responses following training in a spatial working memory task. This training induces more pronounced changes in anterior prefrontal regions, including increased firing rate during the delay period, selectivity, reliability, information for stimuli, representation of whether a test stimulus matched the remembered cue or not, and variability and correlation between neurons. Similar results are obtained for discrete subdivisions or when treating position along the anterior-posterior axis as a continuous variable. Our results reveal that anterior aspects of the lateral prefrontal cortex of non-human primates possess greater plasticity based on task demands.

Functional specialization of areas along the anterior-posterior axis of the primate prefrontal cortex

Functional specialization of areas along the anterior-posterior axis of the lateral prefrontal cortex has been speculated but little evidence exists about distinct neurophysiological properties between prefrontal sub-regions. To address this issue we divided the lateral prefrontal cortex into a posterior-dorsal, a mid-dorsal, an anterior-dorsal, a posterior-ventral, and an anterior ventral region. Selectivity for spatial locations, shapes, and colors was evaluated in six monkeys never trained in working memory tasks, while they viewed the stimuli passively. Recordings from over two thousand neurons revealed systematic differences between anterior and posterior regions. In the dorsal prefrontal cortex, anterior regions exhibited the largest receptive fields, longest response latencies, and lowest amount of information for stimuli. In the ventral prefrontal cortex, posterior regions were characterized by a low percentage of responsive neurons to any stimuli we used, consistent with high specialization for stimulus features. Additionally, spatial information was more prominent in the dorsal and color in ventral regions. Our results provide neurophysiological evidence for a rostral-caudal gradient of stimulus selectivity through the prefrontal cortex, suggesting that posterior areas are selective for stimuli even when these are not releant for execution of a task, and that anterior areas are likely engaged in more abstract operations.

The neuroscience of working memory capacity and training

Working memory - the ability to maintain and manipulate information over a period of seconds - is a core component of higher cognitive functions. The storage capacity of working memory is limited but can be expanded by training, and evidence of the neural mechanisms underlying this effect is accumulating. Human imaging studies and neurophysiological recordings in non-human primates, together with computational modelling studies, reveal that training increases the activity of prefrontal neurons and the strength of connectivity in the prefrontal cortex and between the prefrontal and parietal cortex. Dopaminergic transmission could have a facilitatory role. These changes more generally inform us of the plasticity of higher cognitive functions.

Demixed principal component analysis of neural population data

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.

DOI:http://dx.doi.org/10.7554/eLife.10989.001

Many neuroscience experiments today involve using electrodes to record from the brain of an animal, such as a mouse or a monkey, while the animal performs a task. The goal of such experiments is to understand how a particular brain region works. However, modern experimental techniques allow the activity of hundreds of neurons to be recorded simultaneously. Analysing such large amounts of data then becomes a challenge in itself.

This is particularly true for brain regions such as the prefrontal cortex that are involved in the cognitive processes that allow an animal to acquire knowledge. Individual neurons in the prefrontal cortex encode many different types of information relevant to a given task. Imagine, for example, that an animal has to select one of two objects to obtain a reward. The same group of prefrontal cortex neurons will encode the object presented to the animal, the animal’s decision and its confidence in that decision. This simultaneous representation of different elements of a task is called a ‘mixed’ representation, and is difficult to analyse.

Kobak, Brendel et al. have now developed a data analysis tool that can ‘demix’ neural activity. The tool breaks down the activity of a population of neurons into its individual components. Each of these relates to only a single aspect of the task and is thus easier to interpret. Information about stimuli, for example, is distinguished from information about the animal’s confidence levels.

Kobak, Brendel et al. used the demixing tool to reanalyse existing datasets recorded from several different animals, tasks and brain regions. In each case, the tool provided a complete, concise and transparent summary of the data. The next steps will be to apply the analysis tool to new datasets to see how well it performs in practice. At a technical level, the tool could also be extended in a number of different directions to enable it to deal with more complicated experimental designs in future.

DOI:http://dx.doi.org/10.7554/eLife.10989.002

Role of Prefrontal Persistent Activity in Working Memory

The prefrontal cortex is activated during working memory, as evidenced by fMRI results in human studies and neurophysiological recordings in animal models. Persistent activity during the delay period of working memory tasks, after the offset of stimuli that subjects are required to remember, has traditionally been thought of as the neural correlate of working memory. In the last few years several findings have cast doubt on the role of this activity. By some accounts, activity in other brain areas, such as the primary visual and posterior parietal cortex, is a better predictor of information maintained in visual working memory and working memory performance; dynamic patterns of activity may convey information without requiring persistent activity at all; and prefrontal neurons may be ill-suited to represent non-spatial information about the features and identity of remembered stimuli. Alternative interpretations about the role of the prefrontal cortex have thus been suggested, such as that it provides a top-down control of information represented in other brain areas, rather than maintaining a working memory trace itself. Here we review evidence for and against the role of prefrontal persistent activity, with a focus on visual neurophysiology. We show that persistent activity predicts behavioral parameters precisely in working memory tasks. We illustrate that prefrontal cortex represents features of stimuli other than their spatial location, and that this information is largely absent from early cortical areas during working memory. We examine memory models not dependent on persistent activity, and conclude that each of those models could mediate only a limited range of memory-dependent behaviors. We review activity decoded from brain areas other than the prefrontal cortex during working memory and demonstrate that these areas alone cannot mediate working memory maintenance, particularly in the presence of distractors. We finally discuss the discrepancy between BOLD activation and spiking activity findings, and point out that fMRI methods do not currently have the spatial resolution necessary to decode information within the prefrontal cortex, which is likely organized at the micrometer scale. Therefore, we make the case that prefrontal persistent activity is both necessary and sufficient for the maintenance of information in working memory.

Automatic comparison of stimulus durations in the primate prefrontal cortex: the neural basis of across-task interference

Rhesus monkeys performed two tasks, both requiring a choice between a red square and a blue circle. In the duration task, the two stimuli appeared sequentially on each trial, for varying durations, and, later, during the choice phase of the task, the monkeys needed to choose the one that had lasted longer. In the matching-to-sample task, one of the two stimuli appeared twice as a sample, with durations matching those in the duration task, and the monkey needed to choose that stimulus during the choice phase. Although stimulus duration was irrelevant in the matching-to-sample task, the monkeys made twice as many errors when the second stimulus was shorter. This across-task interference supports an order-dependent model of the monkeys' choice and reveals something about their strategy in the duration task. The monkeys tended to choose the second stimulus when its duration exceeded the first and to choose the alternative stimulus otherwise. For the duration task, this strategy obviated the need to store stimulus-duration conjunctions for both stimuli, but it generated errors on the matching-to-sample task. We examined duration coding in prefrontal neurons and confirmed that a population of cells encoded relative duration during the matching-to-sample task, as expected from the order-dependent errors.

Modulation of network excitability by persistent activity: how working memory affects the response to incoming stimuli

Persistent activity and match effects are widely regarded as neuronal correlates of short-term storage and manipulation of information, with the first serving active maintenance and the latter supporting the comparison between memory contents and incoming sensory information. The mechanistic and functional relationship between these two basic neurophysiological signatures of working memory remains elusive. We propose that match signals are generated as a result of transient changes in local network excitability brought about by persistent activity. Neurons more active will be more excitable, and thus more responsive to external inputs. Accordingly, network responses are jointly determined by the incoming stimulus and the ongoing pattern of persistent activity. Using a spiking model network, we show that this mechanism is able to reproduce most of the experimental phenomenology of match effects as exposed by single-cell recordings during delayed-response tasks. The model provides a unified, parsimonious mechanistic account of the main neuronal correlates of working memory, makes several experimentally testable predictions, and demonstrates a new functional role for persistent activity.

Over short time periods, memories are stored by sustained patterns of spiking activity which, once initiated by the stimulus, persist over the entire retention interval. How the information stored by such persistent activity is later retrieved is presently unclear. Here we propose that, besides temporarily storing memories, persistent activity is also instrumental in their retrieval by transiently modifying the tuning properties of the underlying neuronal networks. We show that the mechanism proposed parsimoniously recapitulates the extensive experimental phenomenology on match effects observed in delayed-response tasks, where the information held in memory has to be compared with incoming, sensory-related information to act appropriately. The theory makes very specific, straightforwardly testable predictions.

On the relationship between persistent delay activity, repetition enhancement and priming

Human efficiency in processing incoming stimuli (in terms of speed and/or accuracy) is typically enhanced by previous exposure to the same, or closely related stimuli—a phenomenon referred to as priming. In spite of the large body of knowledge accumulated in behavioral studies about the conditions conducive to priming, and its relationship with other forms of memory, the underlying neuronal correlates of priming are still under debate. The idea has repeatedly been advanced that a major neuronal mechanism supporting behaviorally-expressed priming is repetition suppression, a widespread reduction of spiking activity upon stimulus repetition which has been routinely exposed by single-unit recordings in non-human primates performing delayed-response, as well as passive fixation tasks. This proposal is mainly motivated by the observation that, in human fMRI studies, priming is associated to a significant reduction of the BOLD signal (widely interpreted as a proxy of the level of spiking activity) upon stimulus repetition. Here, we critically re-examine a large part of the electrophysiological literature on repetition suppression in non-human primates and find that repetition suppression is systematically accompanied by stimulus-selective delay period activity, together with repetition enhancement, an increase of spiking activity upon stimulus repetition in small neuronal populations. We argue that repetition enhancement constitutes a more viable candidate for a putative neuronal substrate of priming, and propose a minimal framework that links together, mechanistically and functionally, repetition suppression, stimulus-selective delay activity and repetition enhancement.

A scalar neural code for categories in parietal cortex: representing cognitive variables as "more" or "less"

In this issue, Fitzgerald et al. (2013) show that LIP neurons in monkeys encode categorically distinct task conditions using a scalar code. Activity scales up or down to encode different categories, with neurons maintaining proportional levels of activity in relation to one another.

Neural changes after training to perform cognitive tasks

Cognitive operations requiring working memory rely on the activity of neurons in areas of the association cortex, most prominently the lateral prefrontal cortex. Human imaging and animal neurophysiological studies indicate that this activity is shaped by learning, though much is unknown about how much training alters neural activity and cortical organization. Results from non-human primates demonstrate that prior to any training in cognitive tasks, prefrontal neurons respond to stimuli, exhibit persistent activity after their offset, and differentiate between matching and non-matching stimuli presented in sequence. A number of important changes also occur after training in a working memory task. More neurons are recruited by the stimuli and exhibit higher firing rates, particularly during the delay period. Operant stimuli that need to be recognized in order to perform the task elicit higher overall rates of responses, while the variability of individual discharges and correlation of discharges between neurons decrease after training. New information is incorporated in the activity of a small population of neurons highly specialized for the task and in a larger population of neurons that exhibit modest task related information, while information about other aspects of stimuli remains present in neuronal activity. Despite such changes, the relative selectivity of the dorsal and ventral aspect of the lateral prefrontal cortex is not radically altered with regard to spatial and non-spatial stimuli after training. Collectively, these results provide insights on the nature and limits of cortical plasticity mediating cognitive tasks.

Correlated discharges in the primate prefrontal cortex before and after working memory training

The correlation of discharges between single neurons can provide information about the computations and network properties of neuronal populations during the performance of cognitive tasks. In recent years, dynamic modulation of neuronal correlations by attention has been revealed during the execution of behavioral tasks. Much less is known about the influence of learning and performing a task itself. We therefore sought to quantify the correlated firing of simultaneously recorded pairs of neurons in the prefrontal cortex of naïve monkeys that were only required to fixate, and to examine how this correlation was altered after they had learned to perform a working memory task. We found that the trial-to-trial correlation of discharge rates between pairs of neurons (noise correlation) differed across neurons depending on their responsiveness and selectivity for stimuli, even before training in a working memory task. After monkeys had learned to perform the task, correlated firing decreased overall, although the effects varied according to the functional properties of the neurons. The greatest decreases were observed on comparison of populations of neurons that exhibited elevated firing rates during the trial events and those that had more similar spatial and temporal tuning. Greater decreases in noise correlation were also observed for pairs comprising one fast spiking neuron (putative interneuron) and one regular spiking neuron (putative pyramidal neuron) than pairs comprising regular spiking neurons only. Our results demonstrate that learning and performance of a cognitive task alters the correlation structure of neuronal firing in the prefrontal cortex.

Variability of prefrontal neuronal discharges before and after training in a working memory task

Variability of neural discharges can be revealing about the computations and network properties of neuronal populations during the performance of cognitive tasks. We sought to quantify neuronal variability in the prefrontal cortex of naïve monkeys that were only required to fixate, and to examine how this measure was altered by learning and execution of a working memory task. We therefore performed analysis of a large database of recordings in the same animals, using the same stimuli, before and after training. Our results indicate that the Fano Factor, a measure of variability, differs across neurons depending on their functional properties both before and after learning. Fano Factor generally decreased after learning the task. Variability was modulated by task events and displayed lowest values during the stimulus presentation. Nonetheless, the decrease in variability after training was present even prior to the presentation of any stimuli, in the fixation period. The greatest decreases were observed comparing populations of neurons that exhibited elevated firing rate during the trial events. Our results offer insights on how properties of the prefrontal network are affected by performance of a cognitive task.

Unique and shared roles of the posterior parietal and dorsolateral prefrontal cortex in cognitive functions

The dorsolateral prefrontal cortex (PFC) and posterior parietal cortex (PPC) are two parts of a broader brain network involved in the control of cognitive functions such as working-memory, spatial attention, and decision-making. The two areas share many functional properties and exhibit similar patterns of activation during the execution of mental operations. However, neurophysiological experiments in non-human primates have also documented subtle differences, revealing functional specialization within the fronto-parietal network. These differences include the ability of the PFC to influence memory performance, attention allocation, and motor responses to a greater extent, and to resist interference by distracting stimuli. In recent years, distinct cellular and anatomical differences have been identified, offering insights into how functional specialization is achieved. This article reviews the common functions and functional differences between the PFC and PPC, and their underlying mechanisms.