Delay-related activity of prefrontal neurons in rhesus monkeys performing delayed response

The use of eye-tracking technology in supporting children and adolescents with attention deficit/hyperactivity disorder: a comprehensive review

MATERIALS AND METHODS

Through the analysis of 74 studies, we examine how eye-tracking technology has been applied to address ADHD-related challenges, the specific methods used, and the correlations between eye-tracking metrics and ADHD symptoms in youth.Results and conclusions: The findings suggest that eye-tracking offers a non-invasive and reliable approach to objectively assess domains such as visual processing characteristics, early detection or interventions. By discussing both the achievements and limitations of the reviewed studies, this paper not only consolidates existing knowledge but also proposes avenues for future research to enhance support for children and adolescents with ADHD.

The computational foundations of dynamic coding in working memory

Working memory (WM) is a fundamental aspect of cognition. WM maintenance is classically thought to rely on stable patterns of neural activities. However, recent evidence shows that neural population activities during WM maintenance undergo dynamic variations before settling into a stable pattern. Although this has been difficult to explain theoretically, neural network models optimized for WM typically also exhibit such dynamics. Here, we examine stable versus dynamic coding in neural data, classical models, and task-optimized networks. We review principled mathematical reasons for why classical models do not, while task-optimized models naturally do exhibit dynamic coding. We suggest an update to our understanding of WM maintenance, in which dynamic coding is a fundamental computational feature rather than an epiphenomenon.

Maintenance of color memoranda in activity-quiescent working memory states: Evidence from impulse perturbation

In the present study, we used an impulse perturbation method to probe working memory maintenance of colors in neurally active and activity-quiescent states, focusing on a set of pre-registered analyses. We analyzed the electroencephalograph (EEG) data of 30 participants who completed a delayed match-to-sample working memory task, in which one of the two items that were presented was retro-cued as task relevant. The analyses revealed that both cued and uncued colors were decodable from impulse-evoked activity, the latter in contrast to previous reports of working memory for orientation gratings. Decoding of colors from oscillations in the alpha band showed that cued items could be decoded therein whereas uncued items could not. Overall, the outcomes suggest that subtle differences exist between the representation of colors, and that of stimuli with spatial properties, but the present results also demonstrate that regardless of their specific neural state, both are accessible through visual impulse perturbation.

Population Neuroscience: Principles and Advances

In population neuroscience, three disciplines come together to advance our knowledge of factors that shape the human brain: neuroscience, genetics, and epidemiology (Paus, Human Brain Mapping 31:891-903, 2010). Here, I will come back to some of the background material reviewed in more detail in our previous book (Paus, Population Neuroscience, 2013), followed by a brief overview of current advances and challenges faced by this integrative approach.

Opening Questions in Visual Working Memory

In this reflective piece on visual working memory, I depart from the laboriously honed skills of writing a review. Instead of integrating approaches, synthesizing evidence, and building a cohesive perspective, I scratch my head and share niggles and puzzlements. I expose where my scholarship and understanding are stumped by findings and standard views in the literature.

Stable Working Memory and Perceptual Representations in Macaque Lateral Prefrontal Cortex during Naturalistic Vision

Primates use perceptual and mnemonic visuospatial representations to perform everyday functions. Neurons in the lateral prefrontal cortex (LPFC) have been shown to encode both of these representations during tasks where eye movements are strictly controlled and visual stimuli are reduced in complexity. This raises the question of whether perceptual and mnemonic representations encoded by LPFC neurons remain robust during naturalistic vision-in the presence of a rich visual scenery and during eye movements. Here we investigate this issue by training macaque monkeys to perform working memory and perception tasks in a visually complex virtual environment that requires navigation using a joystick and allows for free visual exploration of the scene. We recorded the activity of 3950 neurons in the LPFC (areas 8a and 9/46) of two male rhesus macaques using multielectrode arrays, and measured eye movements using video tracking. We found that navigation trajectories to target locations and eye movement behavior differed between the perception and working memory tasks, suggesting that animals used different behavioral strategies. Single neurons were tuned to target location during cue encoding and working memory delay, and neural ensemble activity was predictive of the behavior of the animals. Neural decoding of the target location was stable throughout the working memory delay epoch. However, neural representations of similar target locations differed between the working memory and perception tasks. These findings indicate that during naturalistic vision, LPFC neurons maintain robust and distinct neural codes for mnemonic and perceptual visuospatial representations.SIGNIFICANCE STATEMENT We show that lateral prefrontal cortex neurons encode working memory and perceptual representations during a naturalistic task set in a virtual environment. We show that despite eye movement and complex visual input, neurons maintain robust working memory representations of space, which are distinct from neuronal representations for perception. We further provide novel insight into the use of virtual environments to construct behavioral tasks for electrophysiological experiments.

Oscillation/Coincidence-Detection Models of Reward-Related Timing in Corticostriatal Circuits

The major tenets of beat-frequency/coincidence-detection models of reward-related timing are reviewed in light of recent behavioral and neurobiological findings. This includes the emphasis on a core timing network embedded in the motor system that is comprised of a corticothalamic-basal ganglia circuit. Therein, a central hub provides timing pulses (i.e., predictive signals) to the entire brain, including a set of distributed satellite regions in the cerebellum, cortex, amygdala, and hippocampus that are selectively engaged in timing in a manner that is more dependent upon the specific sensory, behavioral, and contextual requirements of the task. Oscillation/coincidence-detection models also emphasize the importance of a tuned ‘perception’ learning and memory system whereby target durations are detected by striatal networks of medium spiny neurons (MSNs) through the coincidental activation of different neural populations, typically utilizing patterns of oscillatory input from the cortex and thalamus or derivations thereof (e.g., population coding) as a time base. The measure of success of beat-frequency/coincidence-detection accounts, such as the Striatal Beat-Frequency model of reward-related timing (SBF), is their ability to accommodate new experimental findings while maintaining their original framework, thereby making testable experimental predictions concerning diagnosis and treatment of issues related to a variety of dopamine-dependent basal ganglia disorders, including Huntington’s and Parkinson’s disease.

Prefrontal Neuronal Activities During Active Retrieval of Information From Long-Term Memory

Abstract. Single-neuron studies performed in the primate prefrontal cortex (PFC) revealed that retaining information in working memory (WM) is associated with sustained firing during the delay period in a match-to-sample task. On the other hand, single-neuron studies using a pair-association task have shown that retrieving information from long-term memory (LTM) is related to two kinds of neural activities: decreasing activity representing information linked to the sample stimulus and increasing activity predicting information for the forthcoming matching stimulus. To further examine neuronal behavioral patterns during LTM retrieval, we used a partial correlation coefficient analysis to analyze single-neuron activities in the PFC while monkeys performed the visual pair-association task. Results showed that, for most of the task-related neurons, firing activity depicted information from the sample stimulus. Nevertheless, some neurons showed an opposite pattern, this is, increasing activity during the delay period, possibly indicating a prospective memory coding from LTM. Interestingly, both activities seem to be present at different degrees as the delay period progresses. Together, these results unveil a new aspect of PFC neurons when retrieving unseen information from LTM.

Shape and Rule Information Is Reflected in Different Local Field Potential Frequencies and Different Areas of the Primate Lateral Prefrontal Cortex

The lateral prefrontal cortex (LFPC) plays a crucial role in executive function by adaptively storing behavior-relevant information as working memory. Neural mechanisms associated with local field potentials (LFPs) may underlie the adaptive properties of the LFPC. Here, we analyzed how LFPs recorded from the monkey LFPC are modulated by the crucial factors of a shape manipulation task. In this task, the test shape is transformed by manipulating a lever to match the size and orientation of the sample shape. The subject is required to temporarily memorize the rules such as the arm-movement-manipulation relationship and the sample shape to generate the sequential behavior of operations. In the present study, we focused on task variables about shape and rules, and examined among which aspects distinguish the ventral and dorsal sides of the LFPC. We found that the transformed shape in the sample period strongly affected the theta and delta waves in the delay period on the ventral side, while the arm-manipulation assignment influenced the gamma components on the dorsal side. These findings suggest that area- and frequency-selective LFP modulations are involved in dynamically recruiting different behavior-relevant information in the LFPC.

Delay activity during visual working memory: A meta-analysis of 30 fMRI experiments

Visual working memory refers to the temporary maintenance and manipulation of task-related visual information. Recent debate on the underlying neural substrates of visual working memory has focused on the delay period of relevant tasks. Persistent neural activity throughout the delay period has been recognized as a correlate of working memory, yet regions demonstrating sustained hemodynamic responses show inconsistency across individual studies. To develop a more precise understanding of delay-period activations during visual working memory, we conducted a coordinate-based meta-analysis on 30 fMRI experiments involving 515 healthy adults with a mean age of 25.65 years. The main analysis revealed a widespread frontoparietal network associated with delay-period activity, as well as activation in the right inferior temporal cortex. These findings were replicated using different meta-analytical algorithms and were shown to be robust against between-study heterogeneity and publication bias. Further meta-analyses on different subgroups of experiments with specific task demands and stimulus types revealed similar delay-period networks, with activations distributed across the frontal and parietal cortices. The roles of prefrontal regions, posterior parietal regions, and inferior temporal areas are reviewed and discussed in the context of content-specific storage. We conclude that cognitive operations that occur during the unfilled delay period in visual working memory tasks can be flexibly expressed across a frontoparietal-temporal network depending on experimental parameters.

Dependence of Working Memory on Coordinated Activity Across Brain Areas

Neural signatures of working memory (WM) have been reported in numerous brain areas, suggesting a distributed neural substrate for memory maintenance. In the current manuscript we provide an updated review of the literature focusing on intracranial neurophysiological recordings during WM in primates. Such signatures of WM include changes in firing rate or local oscillatory power within an area, along with measures of coordinated activity between areas based on synchronization between oscillations. In comparing the ability of various neural signatures in any brain area to predict behavioral performance, we observe that synchrony between areas is more frequently and robustly correlated with WM performance than any of the within-area neural signatures. We further review the evidence for alteration of inter-areal synchrony in brain disorders, consistent with an important role for such synchrony during behavior. Additionally, results of causal studies indicate that manipulating synchrony across areas is especially effective at influencing WM task performance. Each of these lines of research supports the critical role of inter-areal synchrony in WM. Finally, we propose a framework for interactions between prefrontal and sensory areas during WM, incorporating a range of experimental findings and offering an explanation for the observed link between intra-areal measures and WM performance.

Delayed-matching-to-position working memory in mice relies on NMDA-receptors in prefrontal pyramidal cells

A hypofunction of N-methyl-D-aspartate glutamate receptors (NMDARs) has been implicated in the pathogenesis of schizophrenia by clinical and rodent studies. However, to what extent NMDAR-hypofunction in distinct cell-types across the brain causes different symptoms of this disease is largely unknown. One pharmaco-resistant core symptom of schizophrenia is impaired working memory (WM). NMDARs have been suggested to mediate sustained firing in excitatory neurons of the prefrontal cortex (PFC) that might underlie WM storage. However, if NMDAR-hypofunction in prefrontal excitatory neurons may indeed entail WM impairments is unknown. We here investigated this question in mice, in which NMDARs were genetically-ablated in PFC excitatory cells. This cell type-selective NMDAR-hypofunction caused a specific deficit in a delayed-matching-to-position (DMTP) 5-choice-based operant WM task. In contrast, T-maze rewarded alternation and several psychological functions including attention, spatial short-term habituation, novelty-processing, motivation, sociability, impulsivity, and hedonic valuation remained unimpaired at the level of GluN1-hypofunction caused by our manipulation. Our data suggest that a hypofunction of NMDARs in prefrontal excitatory neurons may indeed cause WM impairments, but are possibly not accounting for most other deficits in schizophrenia.

Primate Spatial Memory Cells Become Tuned Early and Lose Tuning at Cell-Specific Times

Working memory, the ability to maintain and transform information, is critical for cognition. Spatial working memory is particularly well studied. The premier model for spatial memory is the continuous attractor network, which posits that cells maintain constant activity over memory periods. Alternative models propose complex dynamics that result in a variety of cell activity time courses. We recorded from neurons in the frontal eye fields and dorsolateral prefrontal cortex of 2 macaques during long (5-15 s) memory periods. We found that memory cells turn on early after stimulus presentation, sustain activity for distinct and fixed lengths of time, then turn off and stay off for the remainder of the memory period. These dynamics are more complex than the dynamics of a canonical bump attractor network model (either decaying or nondecaying) but more constrained than the dynamics of fully heterogeneous memory models. We speculate that memory may be supported by multiple attractor networks working in parallel, with each network having its own characteristic mean turn-off time such that mnemonic resources are gradually freed up over time.

Probing the structure-function relationship with neural networks constructed by solving a system of linear equations

Neural network models are an invaluable tool to understand brain function since they allow us to connect the cellular and circuit levels with behaviour. Neural networks usually comprise a huge number of parameters, which must be chosen carefully such that networks reproduce anatomical, behavioural, and neurophysiological data. These parameters are usually fitted with off-the-shelf optimization algorithms that iteratively change network parameters and simulate the network to evaluate its performance and improve fitting. Here we propose to invert the fitting process by proceeding from the network dynamics towards network parameters. Firing state transitions are chosen according to the transition graph associated with the solution of a task. Then, a system of linear equations is constructed from the network firing states and membrane potentials, in a way that guarantees the consistency of the system. This allows us to uncouple the dynamical features of the model, like its neurons firing rate and correlation, from the structural features, and the task-solving algorithm implemented by the network. We employed our method to probe the structure–function relationship in a sequence memory task. The networks obtained showed connectivity and firing statistics that recapitulated experimental observations. We argue that the proposed method is a complementary and needed alternative to the way neural networks are constructed to model brain function.

Distinct prefrontal top-down circuits differentially modulate sensorimotor behavior

Sensorimotor behaviors require processing of behaviorally relevant sensory cues and the ability to select appropriate responses from a vast behavioral repertoire. Modulation by the prefrontal cortex (PFC) is thought to be key for both processes, but the precise role of specific circuits remains unclear. We examined the sensorimotor function of anatomically distinct outputs from a subdivision of the mouse PFC, the anterior cingulate cortex (ACC). Using a visually guided two-choice behavioral paradigm with multiple cue-response mappings, we dissociated the sensory and motor response components of sensorimotor control. Projection-specific two-photon calcium imaging and optogenetic manipulations show that ACC outputs to the superior colliculus, a key midbrain structure for response selection, principally coordinate specific motor responses. Importantly, ACC outputs exert control by reducing the innate response bias of the superior colliculus. In contrast, ACC outputs to the visual cortex facilitate sensory processing of visual cues. Our results ascribe motor and sensory roles to ACC projections to the superior colliculus and the visual cortex and demonstrate for the first time a circuit motif for PFC function wherein anatomically non-overlapping output pathways coordinate complementary but distinct aspects of visual sensorimotor behavior.

The neural circuit mechanisms for sensorimotor control by the prefrontal cortex (PFC) are unclear. Here, the authors show that PFC outputs to the visual cortex and superior colliculus respectively facilitate sensory processing and action selection, allowing the PFC to independently control complementary but distinct behavioral functions.

Neural activity underlying the detection of an object movement by an observer during forward self-motion: Dynamic decoding and temporal evolution of directional cortical connectivity

Relatively little is known about how the human brain identifies movement of objects while the observer is also moving in the environment. This is, ecologically, one of the most fundamental motion processing problems, critical for survival. To study this problem, we used a task which involved nine textured spheres moving in depth, eight simulating the observer's forward motion while the ninth, the target, moved independently with a different speed towards or away from the observer. Capitalizing on the high temporal resolution of magnetoencephalography (MEG) we trained a Support Vector Classifier (SVC) using the sensor-level data to identify correct and incorrect responses. Using the same MEG data, we addressed the dynamics of cortical processes involved in the detection of the independently moving object and investigated whether we could obtain confirmatory evidence for the brain activity patterns used by the classifier. Our findings indicate that response correctness could be reliably predicted by the SVC, with the highest accuracy during the blank period after motion and preceding the response. The spatial distribution of the areas critical for the correct prediction was similar but not exclusive to areas underlying the evoked activity. Importantly, SVC identified frontal areas otherwise not detected with evoked activity that seem to be important for the successful performance in the task. Dynamic connectivity further supported the involvement of frontal and occipital-temporal areas during the task periods. This is the first study to dynamically map cortical areas using a fully data-driven approach in order to investigate the neural mechanisms involved in the detection of moving objects during observer's self-motion.

Modules or Mean-Fields?

The segregation of neural processing into distinct streams has been interpreted by some as evidence in favour of a modular view of brain function. This implies a set of specialised ‘modules’, each of which performs a specific kind of computation in isolation of other brain systems, before sharing the result of this operation with other modules. In light of a modern understanding of stochastic non-equilibrium systems, like the brain, a simpler and more parsimonious explanation presents itself. Formulating the evolution of a non-equilibrium steady state system in terms of its density dynamics reveals that such systems appear on average to perform a gradient ascent on their steady state density. If this steady state implies a sufficiently sparse conditional independency structure, this endorses a mean-field dynamical formulation. This decomposes the density over all states in a system into the product of marginal probabilities for those states. This factorisation lends the system a modular appearance, in the sense that we can interpret the dynamics of each factor independently. However, the argument here is that it is factorisation, as opposed to modularisation, that gives rise to the functional anatomy of the brain or, indeed, any sentient system. In the following, we briefly overview mean-field theory and its applications to stochastic dynamical systems. We then unpack the consequences of this factorisation through simple numerical simulations and highlight the implications for neuronal message passing and the computational architecture of sentience.

The what, where and how of delay activity

Working memory is characterized by neural activity that persists during the retention interval of delay tasks. Despite the ubiquity of this delay activity across tasks, species and experimental techniques, our understanding of this phenomenon remains incomplete. Although initially there was a narrow focus on sustained activation in a small number of brain regions, methodological and analytical advances have allowed researchers to uncover previously unobserved forms of delay activity various parts of the brain. In light of these new findings, this Review reconsiders what delay activity is, where in the brain it is found, what roles it serves and how it may be generated.

A Temporal Signal-Processing Circuit Based on Spiking Neuron and Synaptic Learning

Time is a continuous, homogeneous, one-way, and independent signal that cannot be modified by human will. The mechanism of how the brain processes temporal information remains elusive. According to previous work, time-keeping in medial premotor cortex (MPC) is governed by four kinds of ramp cell populations (Merchant et al., 2011). We believe that these cell populations participate in temporal information processing in MPC. Hence, in this the present study, we present a model that uses spiking neuron, including these cell populations, to construct a complete circuit for temporal processing. By combining the time-adaptive drift-diffusion model (TDDM) with the transmission of impulse information between neurons, this new model is able to successfully reproduce the result of synchronization-continuation tapping task (SCT). We also discovered that the neurons that we used exhibited some of the firing properties of time-related neurons detected by electrophysiological experiments in other studies. Therefore, we believe that our model reflects many of the physiological of neural circuits in the biological brain and can explain some of the phenomena in the temporal-perception process.

Development of working memory in the male adolescent rat

Working memory develops over the course of adolescence, and neuroimaging studies find development-associated changes in the activity of prefrontal cortical brain regions. Establishment of a rodent model of working memory development would permit more comprehensive studies of the molecular and circuit basis for working memory development in health and disease. Thus, in this study, working memory performance was compared between adolescent and adult male Sprague-Dawley rats using an operant-based, delay-match-to-sample working memory task. Adolescent and adult rats showed similar rates of learning the task and similar performance at a low cognitive load (delays ≤ 6 s). However, when the cognitive load increased, adolescents exhibited impaired working memory performance relative to adults, until postnatal day 50 when performance was not significantly different. Despite evidence that cannabinoids disrupt working memory, we found no effect of acute treatment with the cannabinoid receptor agonist, WIN55212,2, at either age. Moreover, expression of glutamate and GABA receptor subunits was examined in the prelimbic and infralimbic prefrontal cortex across development. NMDA receptor subunit GluN2B expression significantly decreased with age in parallel with improvements in working memory. Thus, we show evidence that rats can be used as a model to study the molecular underpinnings of working memory development.

Putative dopamine neurons in the ventral tegmental area enhance information coding in the prefrontal cortex

It has been proposed that neuronal populations in the prefrontal cortex (PFC) robustly encode task-relevant information through an interplay with the ventral tegmental area (VTA). Yet, the precise computation underlying such functional interaction remains elusive. Here, we conducted simultaneous recordings of single-unit activity in PFC and VTA of rats performing a GO/NoGO task. We found that mutual information between stimuli and neural activity increases in the PFC as soon as stimuli are presented. Notably, it is the activity of putative dopamine neurons in the VTA that contributes critically to enhance information coding in the PFC. The higher the activity of these VTA neurons, the better the conditioned stimuli are encoded in the PFC.

Deep temporal models and active inference

How do we navigate a deeply structured world? Why are you reading this sentence first - and did you actually look at the fifth word? This review offers some answers by appealing to active inference based on deep temporal models. It builds on previous formulations of active inference to simulate behavioural and electrophysiological responses under hierarchical generative models of state transitions. Inverting these models corresponds to sequential inference, such that the state at any hierarchical level entails a sequence of transitions in the level below. The deep temporal aspect of these models means that evidence is accumulated over nested time scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour with Bayesian belief updating - and neuronal process theories - to simulate the epistemic foraging seen in reading. These simulations reproduce perisaccadic delay period activity and local field potentials seen empirically. Finally, we exploit the deep structure of these models to simulate responses to local (e.g., font type) and global (e.g., semantic) violations; reproducing mismatch negativity and P300 responses respectively.

Just above Chance: Is It Harder to Decode Information from Prefrontal Cortex Hemodynamic Activity Patterns?

The prefrontal cortex (PFC) is central to flexible, goal-directed cognition, and understanding its representational code is an important problem in cognitive neuroscience. In humans, multivariate pattern analysis (MVPA) of fMRI blood oxygenation level-dependent (BOLD) measurements has emerged as an important approach for studying neural representations. Many previous studies have implicitly assumed that MVPA of fMRI BOLD is just as effective in decoding information encoded in PFC neural activity as it is in visual cortex. However, MVPA studies of PFC have had mixed success. Here we estimate the base rate of decoding information from PFC BOLD activity patterns from a meta-analysis of published MVPA studies. We show that PFC has a significantly lower base rate (55.4%) than visual areas in occipital (66.6%) and temporal (71.0%) cortices and one that is close to chance levels. Our results have implications for the design and interpretation of MVPA studies of PFC and raise important questions about its functional organization.

Active Inference, Novelty and Neglect

In this chapter, we provide an overview of the principles of active inference. We illustrate how different forms of short-term memory are expressed formally (mathematically) through appealing to beliefs about the causes of our sensations and about the actions we pursue. This is used to motivate an approach to active vision that depends upon inferences about the causes of 'what I have seen' and learning about 'what I would see if I were to look there'. The former could manifest as persistent 'delay-period' activity - of the sort associated with working memory, while the latter is better suited to changes in synaptic efficacy - of the sort that underlies short-term learning and adaptation. We review formulations of these ideas in terms of active inference, their role in directing visual exploration and the consequences - for active vision - of their failures. To illustrate the latter, we draw upon some of our recent work on the computational anatomy of visual neglect.

The graphical brain: Belief propagation and active inference

This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference-and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models. Crucially, these models can entertain both discrete and continuous states, leading to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to elucidate the requisite message passing in terms of its form and scheduling. To accommodate mixed generative models (of discrete and continuous states), one also has to consider link nodes or factors that enable discrete and continuous representations to talk to each other. When mapping the implicit computational architecture onto neuronal connectivity, several interesting features emerge. For example, Bayesian model averaging and comparison, which link discrete and continuous states, may be implemented in thalamocortical loops. These and other considerations speak to a computational connectome that is inherently state dependent and self-organizing in ways that yield to a principled (variational) account. We conclude with simulations of reading that illustrate the implicit neuronal message passing, with a special focus on how discrete (semantic) representations inform, and are informed by, continuous (visual) sampling of the sensorium.

AUTHOR SUMMARY

This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference-and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models that can entertain both discrete and continuous states. This leads to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to characterize the requisite message passing, and link this formal characterization to canonical microcircuits and extrinsic connectivity in the brain.

Efficient enhancement of information in the prefrontal cortex during the presence of reward predicting stimuli

The prefrontal cortex (PFC) is a key brain structure for decision making, behavioural flexibility and working memory. Neurons in PFC encode relevant stimuli through changes in their firing rate, although the metabolic cost of spiking activity puts strong constrains to neural codes based on firing rate modulation. Thus, how PFC neural populations code relevant information in an efficient way is not clearly understood. To address this issue we made single unit recordings in the PFC of rats performing a GO/NOGO discrimination task and analysed how entropy between pairs of neurons changes during cue presentation. We found that entropy rises only during reward-predicting cues. Moreover, this change in entropy occurred along an increase in the efficiency of the whole process. We studied possible mechanisms behind the efficient gain in entropy by means of a two neuron leaky integrate-and-fire model, and found that a precise relationship between synaptic efficacy and firing rate is required to explain the experimentally observed results.

Neurons in the crow nidopallium caudolaterale encode varying durations of visual working memory periods

Adaptive sequential behaviors rely on the bridging and integration of temporally separate information for the realization of prospective goals. Corvids' remarkable behavioral flexibility is thought to depend on the workings of the nidopallium caudolaterale (NCL), a high-level avian associative forebrain area. We trained carrion crows to remember visual items for three alternating delay durations in a delayed match-to-sample task and recorded single-unit activity from the NCL. Sample-selective delay activity, a correlate of visual working memory, was maintained throughout different working memory durations. Delay responses remained selective for the same preferred sample item across blocks with different delay durations. However, selectivity strength decreased with increasing delay durations, mirroring worsened behavioral performance with longer memory delays. Behavioral relevance of delay activity was further evidenced by reduced encoding of the preferred sample item during error trials. In addition, NCL neurons adapted their time-dependent discharges to blocks of different memory durations, so that delay duration could be successfully classified based on population activity a few trials after the delay duration switched. Therefore, NCL neurons not only maintain information from individual trials, but also keep track of the duration for which this information is needed in the context of the task. These results strengthen the role of corvid NCL in maintaining working memory for flexible control of temporally extended goal-directed behavior.

Brain Mechanisms Underlying the Brief Maintenance of Seen and Unseen Sensory Information

Recent evidence of unconscious working memory challenges the notion that only visible stimuli can be actively maintained over time. In the present study, we investigated the neural dynamics underlying the maintenance of variably visible stimuli using magnetoencephalography. Subjects had to detect and mentally maintain the orientation of a masked grating. We show that the stimulus is fully encoded in early brain activity independently of visibility reports. However, the presence and orientation of the target are actively maintained throughout the brief retention period, even when the stimulus is reported as unseen. Source and decoding analyses revealed that perceptual maintenance recruits a hierarchical network spanning the early visual, temporal, parietal, and frontal cortices. Importantly, the representations coded in the late processing stages of this network specifically predicted visibility reports. These unexpected results challenge several theories of consciousness and suggest that invisible information can be briefly maintained within the higher processing stages of visual perception.

Deep temporal models and active inference

How do we navigate a deeply structured world? Why are you reading this sentence first - and did you actually look at the fifth word? This review offers some answers by appealing to active inference based on deep temporal models. It builds on previous formulations of active inference to simulate behavioural and electrophysiological responses under hierarchical generative models of state transitions. Inverting these models corresponds to sequential inference, such that the state at any hierarchical level entails a sequence of transitions in the level below. The deep temporal aspect of these models means that evidence is accumulated over nested time scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour with Bayesian belief updating - and neuronal process theories - to simulate the epistemic foraging seen in reading. These simulations reproduce perisaccadic delay period activity and local field potentials seen empirically. Finally, we exploit the deep structure of these models to simulate responses to local (e.g., font type) and global (e.g., semantic) violations; reproducing mismatch negativity and P300 responses respectively.

Sustained Activity Encoding Working Memories: Not Fully Distributed

Working memory (WM) is the ability to remember and manipulate information for short time intervals. Recent studies have proposed that sustained firing encoding the contents of WM is ubiquitous across cortical neurons. We review here the collective evidence supporting this claim. A variety of studies report that neurons in prefrontal, parietal, and inferotemporal association cortices show robust sustained activity encoding the location and features of memoranda during WM tasks. However, reports of WM-related sustained activity in early sensory areas are rare, and typically lack stimulus specificity. We propose that robust sustained activity that can support WM coding arises as a property of association cortices downstream from the early stages of sensory processing.

Working Memory in the Prefrontal Cortex

The prefrontal cortex participates in a variety of higher cognitive functions. The concept of working memory is now widely used to understand prefrontal functions. Neurophysiological studies have revealed that stimulus-selective delay-period activity is a neural correlate of the mechanism for temporarily maintaining information in working memory processes. The central executive, which is the master component of Baddeley's working memory model and is thought to be a function of the prefrontal cortex, controls the performance of other components by allocating a limited capacity of memory resource to each component based on its demand. Recent neurophysiological studies have attempted to reveal how prefrontal neurons achieve the functions of the central executive. For example, the neural mechanisms of memory control have been examined using the interference effect in a dual-task paradigm. It has been shown that this interference effect is caused by the competitive and overloaded recruitment of overlapping neural populations in the prefrontal cortex by two concurrent tasks and that the information-processing capacity of a single neuron is limited to a fixed level, can be flexibly allocated or reallocated between two concurrent tasks based on their needs, and enhances behavioral performance when its allocation to one task is increased. Further, a metamemory task requiring spatial information has been used to understand the neural mechanism for monitoring its own operations, and it has been shown that monitoring the quality of spatial information represented by prefrontal activity is an important factor in the subject's choice and that the strength of spatially selective delay-period activity reflects confidence in decision-making. Although further studies are needed to elucidate how the prefrontal cortex controls memory resource and supervises other systems, some important mechanisms related to the central executive have been identified.

Ghosts in the Machine II: Neural Correlates of Memory Interference from the Previous Trial

Previous memoranda interfere with working memory. For example, spatial memories are biased toward locations memorized on the previous trial. We predicted, based on attractor network models of memory, that activity in the frontal eye fields (FEFs) encoding a previous target location can persist into the subsequent trial and that this ghost will then bias the readout of the current target. Contrary to this prediction, we find that FEF memory representations appear biased away from (not toward) the previous target location. The behavioral and neural data can be reconciled by a model in which receptive fields of memory neurons converge toward remembered locations, much as receptive fields converge toward attended locations. Convergence increases the resources available to encode the relevant memoranda and decreases overall error in the network, but the residual convergence from the previous trial can give rise to an attractive behavioral bias on the next trial.

Dissociated sequential activity and stimulus encoding in the dorsomedial striatum during spatial working memory

Several lines of evidence suggest that the striatum has an important role in spatial working memory. The neural dynamics in the striatum have been described in tasks with short delay periods (1–4 s), but remain largely uncharacterized for tasks with longer delay periods. We collected and analyzed single unit recordings from the dorsomedial striatum of rats performing a spatial working memory task with delays up to 10 s. We found that neurons were activated sequentially, with the sequences spanning the entire delay period. Surprisingly, this sequential activity was dissociated from stimulus encoding activity, which was present in the same neurons, but preferentially appeared towards the onset of the delay period. These observations contrast with descriptions of sequential dynamics during similar tasks in other brains areas, and clarify the contribution of the striatum to spatial working memory.

DOI:http://dx.doi.org/10.7554/eLife.19507.001

Distinct roles of visual, parietal, and frontal motor cortices in memory-guided sensorimotor decisions

Mapping specific sensory features to future motor actions is a crucial capability of mammalian nervous systems. We investigated the role of visual (V1), posterior parietal (PPC), and frontal motor (fMC) cortices for sensorimotor mapping in mice during performance of a memory-guided visual discrimination task. Large-scale calcium imaging revealed that V1, PPC, and fMC neurons exhibited heterogeneous responses spanning all task epochs (stimulus, delay, response). Population analyses demonstrated unique encoding of stimulus identity and behavioral choice information across regions, with V1 encoding stimulus, fMC encoding choice even early in the trial, and PPC multiplexing the two variables. Optogenetic inhibition during behavior revealed that all regions were necessary during the stimulus epoch, but only fMC was required during the delay and response epochs. Stimulus identity can thus be rapidly transformed into behavioral choice, requiring V1, PPC, and fMC during the transformation period, but only fMC for maintaining the choice in memory prior to execution.

DOI:http://dx.doi.org/10.7554/eLife.13764.001

Enhancing Prefrontal Neuron Activity Enables Associative Learning of Temporally Disparate Events

The ability to link events that are separated in time is important for extracting meaning from experiences and guiding behavior in the future. This ability likely requires the brain to continue representing events even after they have passed, a process that may involve the prefrontal cortex and takes the form of sustained, event-specific neuron activity. Here, we show that experimentally increasing the activity of excitatory neurons in the medial prefrontal cortex (mPFC) enables rats to associate two stimuli separated by a 750-ms long temporal gap. Learning is accompanied by ramping increases in prefrontal theta and beta rhythms during the interval between stimuli. This ramping activity predicts memory-related behavioral responses on a trial-by-trial basis but is not correlated with the same muscular activity during non-memory conditions. Thus, the enhancement of prefrontal neuron excitability extends the time course of evoked prefrontal network activation and facilitates the formation of associations of temporally disparate, but correlated, events.

The Functional Anatomy of Time: What and When in the Brain

This Opinion article considers the implications for functional anatomy of how we represent temporal structure in our exchanges with the world. It offers a theoretical treatment that tries to make sense of the architectural principles seen in mammalian brains. Specifically, it considers a factorisation between representations of temporal succession and representations of content or, heuristically, a segregation into when and what. This segregation may explain the central role of the hippocampus in neuronal hierarchies while providing a tentative explanation for recent observations of how ordinal sequences are encoded. The implications for neuroanatomy and physiology may have something important to say about how self-organised cell assembly sequences enable the brain to exhibit purposeful behaviour that transcends the here and now.

Social Cognition and the Prefrontal Cortex

Social cognitive neuroscience is a rapidly emerging field that utilizes cognitive neuroscientific techniques (e.g., lesion studies, neuroimaging) to address concepts traditionally in the social psychological realm (e.g., attitudes, stereotypes). The purpose of this article is to review published neuroscientific and neuropsychological research into social cognition. The author focuses on the role of the prefrontal cortex in social behavior and presents a framework that provides cohesion of this research. The article proposes that this framework will be useful in guiding future social cognitive neuroscientific research.

Single Canonical Model of Reflexive Memory and Spatial Attention

Many neurons in the dorsal and ventral visual stream have the property that after a brief visual stimulus presentation in their receptive field, the spiking activity in these neurons persists above their baseline levels for several seconds. This maintained activity is not always correlated with the monkey’s task and its origin is unknown. We have previously proposed a simple neural network model, based on shape selective neurons in monkey lateral intraparietal cortex, which predicts the valence and time course of reflexive (bottom-up) spatial attention. In the same simple model, we demonstrate here that passive maintained activity or short-term memory of specific visual events can result without need for an external or top-down modulatory signal. Mutual inhibition and neuronal adaptation play distinct roles in reflexive attention and memory. This modest 4-cell model provides the first simple and unified physiologically plausible mechanism of reflexive spatial attention and passive short-term memory processes.

Brain activation during associative short-term memory maintenance is not predictive for subsequent retrieval

Performance on working memory (WM) tasks may partially be supported by long-term memory (LTM) processing. Hence, brain activation recently being implicated in WM may actually have been driven by (incidental) LTM formation. We examined which brain regions actually support successful WM processing, rather than being confounded by LTM processes, during the maintenance and probe phase of a WM task. We administered a four-pair (faces and houses) associative delayed-match-to-sample (WM) task using event-related functional MRI (fMRI) and a subsequent associative recognition LTM task, using the same stimuli. This enabled us to analyze subsequent memory effects for both the WM and the LTM test by contrasting correctly recognized pairs with incorrect pairs for either task. Critically, with respect to the subsequent WM effect, we computed this analysis exclusively for trials that were forgotten in the subsequent LTM recognition task. Hence, brain activity associated with successful WM processing was less likely to be confounded by incidental LTM formation. The subsequent LTM effect, in contrast, was analyzed exclusively for pairs that previously had been correctly recognized in the WM task, disclosing brain regions involved in successful LTM formation after successful WM processing. Results for the subsequent WM effect showed no significantly activated brain areas for WM maintenance, possibly due to an insensitivity of fMRI to mechanisms underlying active WM maintenance. In contrast, a correct decision at WM probe was linked to activation in the “retrieval success network” (anterior and posterior midline brain structures). The subsequent LTM analyses revealed greater activation in left dorsolateral prefrontal cortex and posterior parietal cortex in the early phase of the maintenance stage. No supra-threshold activation was found during the WM probe. Together, we obtained clearer insights in which brain regions support successful WM and LTM without the potential confound of the respective memory system.

Functions of delay-period activity in the prefrontal cortex and mnemonic scotomas revisited

Working memory (WM) is one of key concepts to understand functions of the prefrontal cortex. Delay-period activity is an important neural correlate to understand the role of WM in prefrontal functions. The importance of delay-period activity is that this activity can encode not only visuospatial information but also a variety of information including non-spatial visual features, auditory and tactile stimuli, task rules, expected reward, and numerical quantity. This activity also participates in a variety of information processing including sensory-to-motor information transformation. These mnemonic features of delay-period activity enable to perform various important operations that the prefrontal cortex participates in, such as executive controls, and therefore, support the notion that WM is an important function to understand prefrontal functions. On the other hand, although experiments using manual versions of the delayed-response task had revealed many important findings, an oculomotor version of this task enabled us to use multiple cue positions, exclude postural orientation during the delay period, and further prove the importance of mnemonic functions of the prefrontal cortex. In addition, monkeys with unilateral lesions exhibited specific impairment only in the performance of memory-guided saccades directed toward visual cues in the visual field contralateral to the lesioned hemisphere. This result indicates that memories for visuospatial coordinates in each hemifield are processed primarily in the contralateral prefrontal cortex. This result further strengthened the idea of mnemonic functions of the prefrontal cortex. Thus, the mnemonic functions of the prefrontal cortex and delay-period activity may not need to be reconsidered, but should be emphasized.

Oscillatory multiplexing of neural population codes for interval timing and working memory

Interval timing and working memory are critical components of cognition that are supported by neural oscillations in prefrontal-striatal-hippocampal circuits. In this review, the properties of interval timing and working memory are explored in terms of behavioral, anatomical, pharmacological, and neurophysiological findings. We then describe the various neurobiological theories that have been developed to explain these cognitive processes - largely independent of each other. Following this, a coupled excitatory - inhibitory oscillation (EIO) model of temporal processing is proposed to address the shared oscillatory properties of interval timing and working memory. Using this integrative approach, we describe a hybrid model explaining how interval timing and working memory can originate from the same oscillatory processes, but differ in terms of which dimension of the neural oscillation is utilized for the extraction of item, temporal order, and duration information. This extension of the striatal beat-frequency (SBF) model of interval timing (Matell and Meck, 2000, 2004) is based on prefrontal-striatal-hippocampal circuit dynamics and has direct relevance to the pathophysiological distortions observed in time perception and working memory in a variety of psychiatric and neurological conditions.

Memory-guided saccades in youth-onset psychosis and attention deficit hyperactivity disorder (ADHD)

AIM

Working memory deficits have been shown to be present in children and adolescents with schizophrenia and attention deficit hyperactivity disorder. Considering the differences in clinical characteristics between these disorders, it was the goal of this study to assess differences in the specific components of working memory in children and adolescents with psychosis and attention deficit hyperactivity disorder.

METHODS

Children and adolescents (age range 8-20 years) with either a non-affective psychotic disorder (n = 25), attention deficit hyperactivity disorder (n = 33) and controls (n = 58) were administered an oculomotor delayed-response task using both a recall and a control condition. Memory-guided saccades were measured during delay periods of 2, 8 and 20 s.

RESULTS

Although both clinical groups were less accurate than controls, there was no evidence of a disproportionate impairment in recall. In addition, there was no evidence of a delay-dependent impairment in psychosis; however, there was a delay-dependent impairment in attention deficit hyperactivity disorder when corrective saccades were included. Speed of information processing was correlated with distance errors in psychosis, suggesting that speed of encoding the stimulus location may have constrained the accuracy of the saccades.

CONCLUSIONS

Our findings support impairments during encoding in the psychosis group and a delay-dependent deficit in the attention deficit hyperactivity disorder group.

Two distinct dynamic modes subtend the detection of unexpected sounds

The brain response to auditory novelty comprises two main EEG components: an early mismatch negativity and a late P300. Whereas the former has been proposed to reflect a prediction error, the latter is often associated with working memory updating. Interestingly, these two proposals predict fundamentally different dynamics: prediction errors are thought to propagate serially through several distinct brain areas, while working memory supposes that activity is sustained over time within a stable set of brain areas. Here we test this temporal dissociation by showing how the generalization of brain activity patterns across time can characterize the dynamics of the underlying neural processes. This method is applied to magnetoencephalography (MEG) recordings acquired from healthy participants who were presented with two types of auditory novelty. Following our predictions, the results show that the mismatch evoked by a local novelty leads to the sequential recruitment of distinct and short-lived patterns of brain activity. In sharp contrast, the global novelty evoked by an unexpected sequence of five sounds elicits a sustained state of brain activity that lasts for several hundreds of milliseconds. The present results highlight how MEG combined with multivariate pattern analyses can characterize the dynamics of human cortical processes.

Tactile perception and working memory in rats and humans

Primates can store sensory stimulus parameters in working memory for subsequent manipulation, but until now, there has been no demonstration of this capacity in rodents. Here we report tactile working memory in rats. Each stimulus is a vibration, generated as a series of velocity values sampled from a normal distribution. To perform the task, the rat positions its whiskers to receive two such stimuli, "base" and "comparison," separated by a variable delay. It then judges which stimulus had greater velocity SD. In analogous experiments, humans compare two vibratory stimuli on the fingertip. We demonstrate that the ability of rats to hold base stimulus information (for up to 8 s) and their acuity in assessing stimulus differences overlap the performance demonstrated by humans. This experiment highlights the ability of rats to perceive the statistical structure of vibrations and reveals their previously unknown capacity to store sensory information in working memory.