Controlling Working Memory Operations by Selective Gating: The Roles of Oscillations and Synchrony

Focal changes in alpha oscillations during short-term memorization of pain: a high-density electroencephalogram study with source localization

Memories of painful events constitute the basis for assessing patients' pain. This study explores the brain oscillatory activity during short-term memorization of a nociceptive stimulus. High-density EEG activity (128 electrodes) was recorded in 13 healthy subjects during a match-to-sample sensory discrimination task, whereby participants compared the intensity of a thumb-located electric shock (S2) with a prior stimulus to the same location (S1) delivered 8-10 s earlier. Stimuli were above or below the individual nociceptive threshold. EEG activity with intracortical source localization via LORETA source reconstruction was analysed during the inter-stimuli period and contrasted with a non-memory-related control task. The inter-stimulus memorization phase was characterized by a focal alpha-activity enhancement, significant during the nociceptive condition only, which progressed from bilateral occipital regions (cuneus and mid-occipital gyri) during the first encoding-memorization phase towards the right-superior and right mid-temporal gyri during the 2-4 s immediately preceding S2. Initial alpha enhancement in occipital areas/cuneus is consistent with rapid non-specific inhibition of task-irrelevant visual processing during initial stimulus encoding. Its transfer to the right-temporal regions was concomitant to the temporary upholding of the stimulus perceptual representation, previous to receiving S2, and suggests an active and local blockade of external interferences while these regions actively maintain internal information. These results add to a growing field indicating that alpha oscillations, while indicating local inhibitory processes, can also indirectly reveal active stimulus handling, including maintenance in short-term memory buffers, by objectivizing the filtering out of irrelevant and potentially disrupting inputs in brain regions engaged in internally driven operations.

Mechanistic role of alpha oscillations in a computational model of working memory

Brain oscillations are believed to be involved in the different operations necessary to manipulate information during working memory tasks. We propose a mechanistic role for the observed inhibition effect of the alpha rhythm based on its interference with the theta rhythm. Using the Lisman-Idiart model for multi-item working memory, we show that the interaction between these two oscillations is capable of creating a long lasting destructive interference that prevents the cyclic reactivation of neuronal ensembles and, as a consequence, memory maintenance. Additionally, to ensure robustness we propose a modular version of the model and implement oscillations as traveling waves. Using this model, we show that the interactions between theta and gamma determine the allocation of multiple memories in distinct modules, while the interference between theta and alpha disrupts the maintenance of the information already stored in them. The effect of alpha in erasing or blocking storage is robust and seems fairly independent of frequency, as long as it stays within the alpha range. This model helps us to understand why the alpha and theta oscillations, which have close frequency bands, could have opposite roles in working memory.

The association between working memory precision and the nonlinear dynamics of frontal and parieto-occipital EEG activity

Electrophysiological working memory (WM) research shows brain areas communicate via macroscopic oscillations across frequency bands, generating nonlinear amplitude modulation (AM) in the signal. Traditionally, AM is expressed as the coupling strength between the signal and a prespecified modulator at a lower frequency. Therefore, the idea of AM and coupling cannot be studied separately. In this study, 33 participants completed a color recall task while their brain activity was recorded through EEG. The AM of the EEG data was extracted using the Holo-Hilbert spectral analysis (HHSA), an adaptive method based on the Hilbert-Huang transforms. The results showed that WM load modulated parieto-occipital alpha/beta power suppression. Furthermore, individuals with higher frontal theta power and lower parieto-occipital alpha/beta power exhibited superior WM precision. In addition, the AM of parieto-occipital alpha/beta power predicted WM precision after presenting a target-defining probe array. The phase-amplitude coupling (PAC) between the frontal theta phase and parieto-occipital alpha/beta AM increased with WM load while processing incoming stimuli, but the PAC itself did not predict the subsequent recall performance. These results suggest frontal and parieto-occipital regions communicate through theta-alpha/beta PAC. However, the overall recall precision depends on the alpha/beta AM following the onset of the retro cue.

Neuropsychological effect of working memory capacity on mental rotation under hypoxia environment

High-altitude exposure induces the decline of spatial manipulation such as mental rotation which is limited by working memory capacity, but the underlying neuropsychological effect remains to be identified. We evaluated the mental rotation task and the contralateral delay activity (CDA) task under hypoxia environment using the event-related potential. When compared with the controls, the behavior response was slowed on two tasks in the high-altitude group. The declined mental rotation and the decreased working memory capacity were synchronously related to the amplitudes of P50 and CDA, respectively. The P50 during mental rotation was positively correlated to that of rotation-related negativity (RRN) component, so was with the CDA. Time-frequency analysis showed that the beta/alpha power in mental rotation and the theta/alpha/beta power in CDA were enhanced in the high-altitude group. The present study might suggest that the decline of working memory capacity induced poor performance of mental rotation, which may be derived from a bottom-up sensory gating deficit reflected by P50.

Exact neural mass model for synaptic-based working memory

A synaptic theory of Working Memory (WM) has been developed in the last decade as a possible alternative to the persistent spiking paradigm. In this context, we have developed a neural mass model able to reproduce exactly the dynamics of heterogeneous spiking neural networks encompassing realistic cellular mechanisms for short-term synaptic plasticity. This population model reproduces the macroscopic dynamics of the network in terms of the firing rate and the mean membrane potential. The latter quantity allows us to gain insight of the Local Field Potential and electroencephalographic signals measured during WM tasks to characterize the brain activity. More specifically synaptic facilitation and depression integrate each other to efficiently mimic WM operations via either synaptic reactivation or persistent activity. Memory access and loading are related to stimulus-locked transient oscillations followed by a steady-state activity in the β-γ band, thus resembling what is observed in the cortex during vibrotactile stimuli in humans and object recognition in monkeys. Memory juggling and competition emerge already by loading only two items. However more items can be stored in WM by considering neural architectures composed of multiple excitatory populations and a common inhibitory pool. Memory capacity depends strongly on the presentation rate of the items and it maximizes for an optimal frequency range. In particular we provide an analytic expression for the maximal memory capacity. Furthermore, the mean membrane potential turns out to be a suitable proxy to measure the memory load, analogously to event driven potentials in experiments on humans. Finally we show that the γ power increases with the number of loaded items, as reported in many experiments, while θ and β power reveal non monotonic behaviours. In particular, β and γ rhythms are crucially sustained by the inhibitory activity, while the θ rhythm is controlled by excitatory synapses.

Working Memory (WM) is the ability to temporarily store and manipulate stimuli representations that are no longer available to the senses. We have developed an innovative coarse-grained population model able to mimic several operations associated to WM. The novelty of the model consists in reproducing exactly the dynamics of spiking neural networks with realistic synaptic plasticity composed of hundreds of thousands of neurons in terms of a few macroscopic variables. These variables give access to experimentally measurable quantities such as local field potentials and electroencephalographic signals. Memory operations are joined to sustained or transient oscillations emerging in different frequency bands, in accordance with experimental results for primate and humans performing WM tasks. We have designed an architecture composed of many excitatory populations and a common inhibitory pool able to store and retain several memory items. The capacity of our multi-item architecture is around 3–5 items, a value similar to the WM capacities measured in many experiments. Furthermore, the maximal capacity is achievable only for presentation rates within an optimal frequency range. Finally, we have defined a measure of the memory load analogous to the event-related potentials employed to test humans’ WM capacity during visual memory tasks.

Is It Possible to Improve Working Memory With Prefrontal tDCS? Bridging Currents to Working Memory Models

A great deal of research has been performed with the promise of improving such critical cognitive functions as working memory (WM), with transcranial direct current stimulation (tDCS), a well-tolerated, inexpensive, easy-to-use intervention. Under the assumption that by delivering currents through electrodes placed in suitable locations on the scalp, it is possible to increase prefrontal cortex excitability and therefore improve WM. A growing number of studies have led to mixed results, leading to the realization that such oversimplified assumptions need revision. Models spanning currents to behavior have been advocated in order to reconcile and inform neurostimulation investigations. We articulate such multilevel exploration to tDCS/WM by briefly reviewing critical aspects at each level of analysis but focusing on the circuit level and how available biophysical WM models could inform tDCS. Indeed, such models should replace vague reference to cortical excitability changes with relevant tDCS net effects affecting neural computation and behavior in a more predictable manner. We will refer to emerging WM models and explore to what extent the general concept of excitation-inhibition (E/I) balance is a meaningful intermediate level of analysis, its relationship with gamma oscillatory activity, and the extent to which it can index tDCS effects. We will highlight some predictions that appear consistent with empirical evidence – such as non-linearities and trait dependency of effects and possibly a preferential effect on WM control functions – as well as limitations that appear related to the dynamical aspects of coding by persistent activity.

What Can Computational Models Learn From Human Selective Attention? A Review From an Audiovisual Unimodal and Crossmodal Perspective

Selective attention plays an essential role in information acquisition and utilization from the environment. In the past 50 years, research on selective attention has been a central topic in cognitive science. Compared with unimodal studies, crossmodal studies are more complex but necessary to solve real-world challenges in both human experiments and computational modeling. Although an increasing number of findings on crossmodal selective attention have shed light on humans' behavioral patterns and neural underpinnings, a much better understanding is still necessary to yield the same benefit for intelligent computational agents. This article reviews studies of selective attention in unimodal visual and auditory and crossmodal audiovisual setups from the multidisciplinary perspectives of psychology and cognitive neuroscience, and evaluates different ways to simulate analogous mechanisms in computational models and robotics. We discuss the gaps between these fields in this interdisciplinary review and provide insights about how to use psychological findings and theories in artificial intelligence from different perspectives.

Cholecystokinin-Expressing Interneurons of the Medial Prefrontal Cortex Mediate Working Memory Retrieval

Distinct components of working memory are coordinated by different classes of inhibitory interneurons in the PFC, but the role of cholecystokinin (CCK)-positive interneurons remains enigmatic. In humans, this major population of interneurons shows histological abnormalities in schizophrenia, an illness in which deficient working memory is a core defining symptom and the best predictor of long-term functional outcome. Yet, CCK interneurons as a molecularly distinct class have proved intractable to examination by typical molecular methods due to widespread expression of CCK in the pyramidal neuron population. Using an intersectional approach in mice of both sexes, we have succeeded in labeling, interrogating, and manipulating CCK interneurons in the mPFC. Here, we describe the anatomical distribution, electrophysiological properties, and postsynaptic connectivity of CCK interneurons, and evaluate their role in cognition. We found that CCK interneurons comprise a larger proportion of the mPFC interneurons compared with parvalbumin interneurons, targeting a wide range of neuronal subtypes with a distinct connectivity pattern. Phase-specific optogenetic inhibition revealed that CCK, but not parvalbumin, interneurons play a critical role in the retrieval of working memory. These findings shine new light on the relationship between cortical CCK interneurons and cognition and offer a new set of tools to investigate interneuron dysfunction and cognitive impairments associated with schizophrenia.SIGNIFICANCE STATEMENT Cholecystokinin-expressing interneurons outnumber other interneuron populations in key brain areas involved in cognition and memory, including the mPFC. However, they have proved intractable to examination as experimental techniques have lacked the necessary selectivity. To the best of our knowledge, the present study is the first to report detailed properties of cortical cholecystokinin interneurons, revealing their anatomical organization, electrophysiological properties, postsynaptic connectivity, and behavioral function in working memory.

How to Stop Cognitive Processes is as Important as How to Start Them. Commentary on Dipoppa et al

We are used to dealing with concepts which provide explanations for how cognitive processes are initiated. But we hardly ever spend time on trying to explain how such processes are turned off again and, thus, do not compromise subsequent processes.

Editorial Special Issue: Neuronus

This special issue of the 12th volume of Advances in Cognitive Psychology is devoted to the Neuronus conference that took place in Kraków in 2015. In this editorial letter, we will focus on a selection of the materials and some follow-up research that was presented during this conference. We will also briefly introduce the conference contributions that successfully passed an external reviewing process.