Neural Mechanisms of Working Memory Cortical Networks

This dissertation is aimed at understanding the cortical networks that maintain working memory information. By leveraging patterns of information degradation in spatial working memory encoding we reveal new neural mechanisms that support working memory function and challenge existing models of working memory circuits. First we examine how interference from previous memoranda influences memory of a currently remembered location. We find that memory for a currently remembered location is biased toward the previously memorized location. This interference is graded, not all-or-none. Interference is strongest when the previous and current targets are close and activate overlapping populations of neurons. Contrary to the attractive behavioral bias, the neural representation of a currently remembered location in the frontal eye fields appears to be biased away from the previous target location, not toward it. We reconcile this discrepancy by proposing a model in which receptive fields of memory cells converge toward memorized locations. This reallocation of neural resources at task-relevant parts of space reduces overall error in the memory network but introduces systematic behavioral biases toward prior memoranda. We also find that attractive behavioral bias asymptotically increases as a function of the memory period length. Critically, the increase in bias depends only on the current trial’s memory period. That is, the effect of the previous target progressively increases in the current trial after that target’s memory has become irrelevant. We modeled this finding using a two-store model with a transient but unbiased visual sensory store and a sustained store with constant bias. Initially behavior is driven by the veridical visual sensory store and is therefore unbiased. As the visual sensory store decays in the current trial, behavioral responses are increasingly driven by the sustained but biased store, leading to an asymptotic increase of behavioral bias with increasing memory period length. Finally, we look at how memory activity is encoded over long (15 second) memory periods. Memory cells tend to turn on early in the memory period and stay active for a fixed amount of time. Most memory cells shut off prior to the end of the memory period. Within each cell, offset times are repeatable from one trial to the next. Across cells, offset times are broadly distributed throughout the entire memory period. Once a cell shuts off, it remains off for the rest of the memory period. On the one hand, these findings challenge the leading model for working memory, the attractor network framework, which predicts a single homogenous time course from all cells. On the other hand, the findings also show that the patterns of activity seen in memory circuits are much more structured than the heterogeneous patterns suggested by the leading competitors to the attractor models. Our findings are not predicted by current models of working memory circuits and indicate that new network models need to be developed

Neural Mechanisms of Working Memory Cortical Networks

This dissertation is aimed at understanding the cortical networks that maintain working memory information. By leveraging patterns of information degradation in spatial working memory encoding we reveal new neural mechanisms that support working memory function and challenge existing models of working memory circuits. First we examine how interference from previous memoranda influences memory of a currently remembered location. We find that memory for a currently remembered location is biased toward the previously memorized location. This interference is graded, not all-or-none. Interference is strongest when the previous and current targets are close and activate overlapping populations of neurons. Contrary to the attractive behavioral bias, the neural representation of a currently remembered location in the frontal eye fields appears to be biased away from the previous target location, not toward it. We reconcile this discrepancy by proposing a model in which receptive fields of memory cells converge toward memorized locations. This reallocation of neural resources at task-relevant parts of space reduces overall error in the memory network but introduces systematic behavioral biases toward prior memoranda. We also find that attractive behavioral bias asymptotically increases as a function of the memory period length. Critically, the increase in bias depends only on the current trial’s memory period. That is, the effect of the previous target progressively increases in the current trial after that target’s memory has become irrelevant. We modeled this finding using a two-store model with a transient but unbiased visual sensory store and a sustained store with constant bias. Initially behavior is driven by the veridical visual sensory store and is therefore unbiased. As the visual sensory store decays in the current trial, behavioral responses are increasingly driven by the sustained but biased store, leading to an asymptotic increase of behavioral bias with increasing memory period length. Finally, we look at how memory activity is encoded over long (15 second) memory periods. Memory cells tend to turn on early in the memory period and stay active for a fixed amount of time. Most memory cells shut off prior to the end of the memory period. Within each cell, offset times are repeatable from one trial to the next. Across cells, offset times are broadly distributed throughout the entire memory period. Once a cell shuts off, it remains off for the rest of the memory period. On the one hand, these findings challenge the leading model for working memory, the attractor network framework, which predicts a single homogenous time course from all cells. On the other hand, the findings also show that the patterns of activity seen in memory circuits are much more structured than the heterogeneous patterns suggested by the leading competitors to the attractor models. Our findings are not predicted by current models of working memory circuits and indicate that new network models need to be developed

Neural network mechanisms of working memory interference

[eng] Our ability to memorize is at the core of our cognitive abilities. How could we effectively make decisions without considering memories of previous experiences? Broadly, our memories can be divided in two categories: long-term and short-term memories. Sometimes, short-term memory is also called working memory and throughout this thesis I will use both terms interchangeably. As the names suggest, long-term memory is the memory you use when you remember concepts for a long time, such as your name or age, while short-term memory is the system you engage while choosing between different wines at the liquor store. As your attention jumps from one bottle to another, you need to hold in memory characteristics of previous ones to pick your favourite. By the time you pick your favourite bottle, you might remember the prices or grape types of the other bottles, but you are likely to forget all of those details an hour later at home, opening the wine in front of your guests. The overall goal of this thesis is to study the neural mechanisms that underlie working memory interference, as reflected in quantitative, systematic behavioral biases. Ultimately, the goal of each chapter, even when focused exclusively on behavioral experiments, is to nail down plausible neural mechanisms that can produce specific behavioral and neurophysiological findings. To this end, we use the bump-attractor model as our working hypothesis, with which we often contrast the synaptic working memory model. The work performed during this thesis is described here in 3 main chapters, encapsulation 5 broad goals: In Chapter 4.1, we aim at testing behavioral predictions of a bump-attractor (1) network when used to store multiple items. Moreover, we connected two of such networks aiming to model feature-binding through selectivity synchronization (2). In Chapter 4.2, we aim to clarify the mechanisms of working memory interference from previous memories (3), the so-called serial biases. These biases provide an excellent opportunity to contrast activity-based and activity-silent mechanisms because both mechanisms have been proposed to be the underlying cause of those biases. In Chapter 4.3, armed with the same techniques used to seek evidence for activity-silent mechanisms, we test a prediction of the bump-attractor model with short-term plasticity (4). Finally, in light of the results from aim 4 and simple computer simulations, we reinterpret previous studies claiming evidence for activity-silent mechanisms (5)

Oscillatory Control over Representational States in Working Memory

In the visual world, attention is guided by perceptual goals activated in visual working memory (VWM). However, planning multiple-task sequences also requires VWM to store representations for future goals. These future goals need to be prevented from interfering with the current perceptual task. Recent findings have implicated neural oscillations as a control mechanism serving the implementation and switching of different states of prioritization of VWM representations. We review recent evidence that posterior alpha-band oscillations underlie the flexible activation and deactivation of VWM representations and that frontal delta-to-theta-band oscillations play a role in the executive control of this process. That is, frontal delta-to-theta appears to orchestrate posterior alpha through long-range oscillatory networks to flexibly set up and change VWM states during multitask sequences

The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: An individual-differences perspective

We provide an "executive-attention" framework for organizing the cognitive neuroscience research on the constructs of working-memory capacity (WMC), general fluid intelligence, and prefrontal cortex (PFC) function. Rather than provide a novel theory of PFC function, we synthesize a wealth of single-cell, brain-imaging, and neuropsychological research through the lens of our theory of normal individual differences in WMC and attention control (Engle, Kane, & Tuholski, 1999; Engle, Tuholski, Laughlin, & Conway, 1999). Our critical review confirms the prevalent view that dorsolateral PFC circuitry is critical to executive-attention functions. Moreover, although the dorsolateral PFC is but one critical structure in a network of anterior and posterior "attention control" areas, it does have a unique executive-attention role in actively maintaining access to stimulus representations and goals in interference-rich contexts. Our review suggests the utility of an executive-attention framework for guiding future re-search on both PFC function and cognitive control

The cognitive neuroscience of visual working memory

Visual working memory allows us to temporarily maintain and manipulate visual information in order to solve a task. The study of the brain mechanisms underlying this function began more than half a century ago, with Scoville and Milner’s (1957) seminal discoveries with amnesic patients. This timely collection of papers brings together diverse perspectives on the cognitive neuroscience of visual working memory from multiple fields that have traditionally been fairly disjointed: human neuroimaging, electrophysiological, behavioural and animal lesion studies, investigating both the developing and the adult brain

Limitations of Human Visual Working Memory

The present empirical study investigates limitations of human visual working memory (VWM). The experiments of the present work involve the experimental paradigm of change detection using simple geometrical objects in the form of rectangles of different colour, length, and orientation. It can be shown, that a limited performance in the temporary storage of visual information has multiple sources. Limitations of VWM can be attributed to a limited capacity or a limited duration, but also to limitations in retrieval, which so far has found only little attention. Key findings of the present study show, that a capacity limitation cannot be described by a simple and generally valid size of the store. It is in fact generally acknowledged that the capacity limitation of VWM is object-based, which means that the capacity can roughly be determined by the number of visual objects. However, it could be shown that the complexity of the objects has an influence on memory performance: Experimental evidence shows, e.g., that memory performance decreases, when an object is characterized not only by one feature (e.g. colour), but by a number of features (e.g. colour, orientation and size). The results are explained by increased storage demands for the binding of the features. Other key findings of the present study relate to the process of retrieval of information from VWM. For example, an asymmetric performance pattern could be observed: In a change detection task a memory performance was observed that corresponds to a capacity of 3 - 4 objects. In contrast a drastic decrease of performance corresponding to a capacity of only 1 object was observed, when the task was to find a matching item among changed distractors. These results lead to the idea of a change signal, by which the empirical data can be explained. The change signal is elicited by a local mismatch between the information stored in VSTM and perceptual online information. The retrieval process is efficient, when the change signal can be used in the memory task. However, retrieval is extremely limited, when in the presence of multiple changes a less efficient strategy has to be applied. In the course of the present study, moreover, it became evident that there are various links between VWM processes and visual attention. Visual attention is probably crucial for feature binding in VWM. In retrieval the change signal probably involves mechanisms of visual attention. The present study could, therefore, contribute to a clarification of the relation between VWM and visual attentionDie vorliegende empirische Arbeit untersucht Begrenzungen des menschlichen visuellen Kurzzeitgedächtnisses (VKZG). In den Experimenten dieser Arbeit wird unter Verwendung von visuell einfachen geometrischen Figuren in Form von Rechtecken verschiedener Farbe, Länge und Orientierung das experimentelle Paradigma der Veränderungsdetektion eingesetzt. Es wird gezeigt, dass eine begrenzte Gedächtnisleistung bei der kurzfristigen Speicherung visueller Information unterschiedliche Ursprünge hat. Diese Ursprünge können sowohl in einer begrenzten Kapazität und in einer begrenzten Speicherdauer liegen, jedoch auch in den Bedingungen des Abrufs, was bislang in der Forschung nur wenig Beachtung gefunden hat. Wesentliche Befunde der vorliegenden Arbeit zeigen, dass die Limitierung in der Speicherkapazität nicht durch eine einfache und allgemeingültige Angabe der Größe des Speichers zu beschreiben ist. Zwar ist es als erwiesen anzunehmen, dass die Kapazitätslimitierung des VKZG objektbasiert ist, d. h. dass sich die Kapazität des VKZG grob durch die Anzahl der zu speichernden visuellen Objekte bestimmen lässt. Jedoch spielt auch die Komplexität dieser Objekte eine Rolle: Die empirischen Ergebnisse zeigen, dass die Gedächtnisleistung abnimmt, wenn ein Objekt nicht nur durch ein Merkmal (z.B. Farbe) charakterisiert ist, sondern durch mehrere Merkmale (z.B. Farbe, Orientierung und Länge). Diese Befund wird mit einem erhöhten Speicheraufwand für die Merkmalsbindung erklärt. Andere zentrale Befunde dieser Arbeit wurden im Zusammenhang mit der Untersuchung des Abrufs von Informationen aus dem VKZG erhoben. So konnte z.B. eine asymmetrische Gedächtnisleistung beobachtet werden: Wurde das experimentelle Paradigma der Veränderungsdetektion eingesetzt, entsprach die Gedächtnisleistung einer Kapazität von etwa 3 - 4 Objekten. Im Gegensatz dazu wurde ein drastischer Leistungsabfall, entsprechend einer Kapazität von 1 Objekt, beobachtet, wenn unter Abwandlung des Paradigmas nun nicht ein verändertes, sondern ein unverändertes Objekt das Zielobjekt unter sich geänderten Distraktoren war. Aufgrund der Ergebnisse wird die Idee eines Veränderungssignals entwickelt, wodurch die Befunde erklärt werden können. Das Veränderungssignal wird durch eine lokale Inkongruenz zwischen im VKZG gespeicherter und wahrgenommener Information hervorgerufen. Wenn ein solches Veränderungssignal zur Lösung der Gedächtnisaufgabe ausgenutzt werden kann, ist der Abruf effektiv. Der Abruf ist dagegen extrem limitiert, wenn aufgrund multipler Änderungen eine weniger effektive Strategie zur Lösung der Aufgabe eingesetzt werden muss. Im Zuge der vorliegenden Arbeit haben sich darüber hinaus vielfältige Zusammenhänge zwischen Prozessen des VKZG und der visuellen Aufmerksamkeit gezeigt. So ist visuelle Aufmerksamkeit vermutlich für die Merkmalsbindung im VKZG wichtig. Und auch beim Abruf ist visuelle Aufmerksamkeit beteiligt, indem nämlich die Verarbeitung des postulierten Veränderungssignals Mechanismen der visuellen Aufmerksamkeit involviert. Die vorliegende Dissertation konnte auf diese Weise Hinweise zur Klärung der Zusammenhänge von VKZG und visueller Aufmerksamkeit liefer

The Role Of Working Memory In Implementing Computational Elements Of Visuo-Spatial Decision-Making

Decision making is a dynamic process by which a person integrates sensory evidence with prior expectations to select an action to achieve a desired outcome. Often, this process requires working memory to hold and manipulate the relevant information. Working memory has several known limitations and models of implementation that have not been widely considered in the context of decision-making. For decisions regarding spatial stimuli, the dorsolateral prefrontal cortex (dlPFC) is likely to be essential. Activity in this area has been shown to relate to both decision making and working memory, and has led to concrete models of how spatial information may be stored by population activity. To investigate how this activity might be leveraged to implement several computational elements of decision making, we performed two related experiments.In the first, we had human participants perform a working memory task that required the reporting of a decision variable (average location) to determine how working memory limitations impacted decision precision. We also used models of working memory to predict and interpret what information was being actively maintained. We report not only the novel finding that decision variables held in working memory lose precision over time but also that the degree of precision loss depends on the strategy used to make the decision, which differed across participants and conditions. In our second study, we trained monkeys to perform an adaptive oculomotor delayed response task in which they had to integrate cue information with context to select the target most likely to be rewarded. The goal of this study was to investigate whether the tuning properties of working-memory related dlPFC neurons adjusted according to the statistics of the task, corresponding to adaptive behavior. We found that not all monkeys display adaptive behavior, but developed method for interrogating the dynamics of neural responses in those that do. This body of work contributes to our understanding of how working memory may implement the representation of a decision variable or prior information used to interpret incoming evidence. Such understanding may ultimately lead to more effective approaches to addressing disorders of maladaptive decisions, such as addiction and PTSD

Comparison of Neural Activity Related to Working Memory in Primate Dorsolateral Prefrontal and Posterior Parietal Cortex

Neurons in a distributed network of cortical and subcortical areas continue to discharge after the presentation and disappearance of stimuli, providing a neural correlate for working memory. While it is thought that the prefrontal cortex plays a central role in this network, the relative contributions of other brain areas are not as well understood. In order to compare the contributions of the dorsolateral prefrontal and posterior parietal cortex, we recorded neurophysiological activity in monkeys trained to perform two different visuo-spatial working memory tasks: a Match/Nonmatch task, and a Spatial Delayed-Match-to-Sample Task. Neurons in both areas exhibited discharges in the delay periods of the tasks that could be classified in two forms. Sustained discharges persisted after the presentation of a stimulus in the receptive field with a constant or declining rate. Anticipatory responses increased in rate during the delay period, often appearing after presentation of a stimulus out of the receptive field. Despite similarities, we uncovered distinct differences between patterns of delay period in each brain area. Only in the prefrontal cortex sustained responses related to the original stimulus survived presentation of a second stimulus, in the context of the Match/Nonmatch task. Our results provide insights on the nature of processing in two areas active during working memory, and on the unique role of the prefrontal cortex in memory maintenance

Sensorimotor Encoding In Prefrontal Cortex

When processing the vast visual world in front of us, eye movements allow us to focus on specific relevant stimuli. To gather information about a stimulus, the brain must determine its location and plan an eye movement to that location. It is not fully understood how populations of neurons store the incoming visual input in a memory signal and then retrieve it to produce a fast and accurate motor output. We sought answers to this question by recording the activity of populations of neurons in cortical regions thought to be involved in the maintenance of working memory and the generation of eye movements. First, we analyzed how populations of neurons coordinated their activity during the period after a visual stimulus was presented, but before an eye movement was made. Specifically, we were interested in identifying the optimal activity profiles for generating a fast eye movement. We recorded from groups of neurons in the frontal eye fields (FEF), an area known to be important for saccade generation. We found neurons change their activity at both the individual level and by covarying their activity with other neurons to generate fast eye movements. We then recorded from neurons in prefrontal cortex (PFC) to determine how visual and motor signals are encoded at the single neuron and population level. For single neurons, we observed rich dynamics, including neurons that encoded the entire visual field, and neurons that shifted their tuning between visual and motor epochs. At the population level, these shifts in tuning created a dynamic population code. These single neuron properties were less likely to be observed in FEF, which resulted in FEF having a more stable population code when compared to PFC. In summary, the visual and motor representations associated with processing a stimulus and preparing an eye movement manifest in the activity of single neurons and populations, and their dynamics over time. These results lead to a richer view of working memory and eye movement planning at the level of populations of neurons than has previously been appreciated