Persistent hippocampal neural firing and hippocampal-cortical coupling predict verbal working memory load

Beta-band oscillations and spike-local field potential synchronization in the motor cortex are correlated with movement deficits in an exercise-induced fatigue mouse model

Fatigue, a complex and multifaceted symptom, profoundly influences quality of life, particularly among individuals suffering from chronic medical conditions or neurological disorders. This symptom not only exacerbates existing conditions but also hinders daily functioning, thereby perpetuating a vicious cycle of worsening symptoms and reduced physical activity. Given the pivotal role of the motor cortex (M1) in coordinating and executing voluntary movements, understanding how the cortex regulates fatigue is crucial. Despite its importance, the neural mechanisms underlying fatigue remain inadequately explored. In this study, we employed electrophysiological recordings in the M1 region of mice to investigate how excitation-inhibition dynamics and neural oscillations are regulated during exercise-induced fatigue. We observed that fatigue led to decreased voluntary physical activity and cognitive performance, manifesting as reduced running wheel distance, mean speed, exercise intensity, and exploratory behaviour. At the neural level, we detected increased firing frequencies for M1 neurons, including both pyramidal neurons and interneurons, along with heightened beta-band oscillatory activity and stronger coupling between beta-band oscillations and interneurons. These findings enhance our understanding of the mechanisms underlying fatigue, offering insights into behavioural, excitability, and oscillatory changes. The results of this study could pave the way for the development of novel intervention strategies to combat fatigue.

Beyond Routine Maintenance: Current Trends in Working Memory Research

Working memory (WM) is an evolving concept. Our understanding of the neural functions that support WM develops iteratively alongside the approaches used to study it, and both can be profoundly shaped by available tools and prevailing theoretical paradigms. Here, the organizers of the 2024 Working Memory Symposium-inspired by this year's meeting-highlight current trends and looming questions in WM research. This review is organized into sections describing (1) ongoing efforts to characterize WM function across sensory modalities, (2) the growing appreciation that WM representations are malleable to context and future actions, (3) the enduring problem of how multiple WM items and features are structured and integrated, and (4) new insights about whether WM shares function with other cognitive processes that have conventionally been considered distinct. This review aims to chronicle where the field is headed and calls attention to issues that are paramount for future research.

Dynamics of hippocampal reactivation for temporal association memory in mice

Reactivation refers to the re-emergence of activity in neuronal ensembles that were active during information encoding. Hippocampal CA1 neuronal ensembles generate firing activities that encode the temporal association among time-separated events. However, whether and how temporal association memory-related CA1 neuronal ensembles reactivate during sleep and their role in temporal association memory consolidation remain unclear. We utilized multiple unit recordings to monitor CA1 neuronal activity in mice learning a trace eyeblink conditioning (tEBC) task, in which presentation of the conditioned stimulus (CS, a light flash) was paired with presentation of the unconditioned stimulus (US, corneal puff) by a time-separated interval. We found that the CS-US paired training mice exhibited few conditioned eyeblink responses (CRs) at the initial-learning stage (ILS) and an asymptotic level of CRs at the well-learning stage (WLS). More than one third of CA1 pyramidal cells (PYR) in the CS-US paired training mice manifested a CS-evoked firing activity that was sustained from the CS to time-separated interval. The CS-evoked PYR firing activity was required for the tEBC acquisition and was greater when the CRs occurred. Intriguingly, the CS-evoked firing PYR ensembles reactivated, which coincided with increased hippocampal ripples during post-training sleep. The reactivation of CS-evoked firing PYR ensembles diminished across learning stages, with greater strength in the ILS. Disrupting the ripple-associated PYR activity impaired both the reactivation of CS-evoked firing PYR ensembles and tEBC consolidation. Our findings highlight the features of hippocampal CA1 neuronal ensemble reactivation during sleep, which support the consolidation of temporal association memory.

Prenatal exposure to polycyclic aromatic hydrocarbons, reduced hippocampal subfield volumes, and word reading

Reading difficulties and exposure to air pollution are both disproportionately high among youth living in economically disadvantaged contexts. Critically, variance in reading skills in youth living in higher socioeconomic status (SES) contexts largely derives from genetic factors, whereas environmental factors explain more of the variance in reading skills among youth living in lower SES contexts. Although reading research has focused closely on the psychosocial environment, little focus has been paid to the effects of the chemical environment. In this study, we measured prenatal exposure to a common air pollutant, polycyclic aromatic hydrocarbons (PAH), via the presence (versus absence) of PAH-DNA adducts in maternal blood during the third trimester of pregnancy. We examined the impact of prenatal PAH exposure on adolescent hippocampal subfield volume and on word reading in a sample of youth followed prospectively since birth (N = 165). Compared to those without prenatal exposure, those with detectable PAH-DNA adducts (N = 63) exhibited significantly smaller hippocampal volumes (CA2/3 subfield, t = -2.413, p < .05), which was associated with worse pseudoword reading (t = 2.346, p < .05). Exploratory mediation analyses showed a significant effect of PAH on pseudoword reading through CA2/3 vol (p = .028), suggesting that prenatal PAH exposure affects hippocampal volume with downstream effects on reading ability.

Neural Dynamics of Visual Stream Interactions During Memory-Guided Actions Investigated by Intracranial EEG

The dorsal and ventral visual streams have been considered to play distinct roles in visual processing for action: the dorsal stream is assumed to support real-time actions, while the ventral stream facilitates memory-guided actions. However, recent evidence suggests a more integrated function of these streams. We investigated the neural dynamics and functional connectivity between them during memory-guided actions using intracranial EEG. We tracked neural activity in the inferior parietal lobule in the dorsal stream, and the ventral temporal cortex in the ventral stream as well as the hippocampus during a delayed action task involving object identity and location memory. We found increased alpha power in both streams during the delay, indicating their role in maintaining spatial visual information. In addition, we recorded increased alpha power in the hippocampus during the delay, but only when both object identity and location needed to be remembered. We also recorded an increase in theta band phase synchronization between the inferior parietal lobule and ventral temporal cortex and between the inferior parietal lobule and hippocampus during the encoding and delay. Granger causality analysis indicated dynamic and frequency-specific directional interactions among the inferior parietal lobule, ventral temporal cortex, and hippocampus that varied across task phases. Our study provides unique electrophysiological evidence for close interactions between dorsal and ventral streams, supporting an integrated processing model in which both streams contribute to memory-guided actions.

The calculating brain

The human brain possesses neural networks and mechanisms enabling the representation of numbers, basic arithmetic operations, and mathematical reasoning. Without the ability to represent numerical quantity and perform calculations, our scientifically and technically advanced culture would not exist. However, the origins of numerical abilities are grounded in an intuitive understanding of quantity deeply rooted in biology. Nevertheless, more advanced symbolic arithmetic skills require a cultural background with formal mathematical education. In the past two decades, cognitive neuroscience has seen significant progress in understanding the workings of the calculating brain through various methods and model systems. This review begins by exploring the mental and neuronal representations of nonsymbolic numerical quantity and then progresses to symbolic representations acquired in childhood. During arithmetic operations (addition, subtraction, multiplication, and division), these representations are processed and transformed according to arithmetic rules and principles, leveraging different mental strategies and types of arithmetic knowledge that can be dissociated in the brain. Although it was once believed that number processing and calculation originated from the language faculty, it is now evident that mathematical and linguistic abilities are primarily processed independently in the brain. Understanding how the healthy brain processes numerical information is crucial for gaining insights into debilitating numerical disorders, including acquired conditions like acalculia and learning-related calculation disorders such as developmental dyscalculia.

Persistent activity during working memory maintenance predicts long-term memory formation in the human hippocampus

Working memory (WM) and long-term memory (LTM) are often viewed as separate cognitive systems. Little is known about how these systems interact when forming memories. We recorded single neurons in the human medial temporal lobe while patients maintained novel items in WM and completed a subsequent recognition memory test for the same items. In the hippocampus, but not in the amygdala, the level of WM content-selective persistent activity during WM maintenance was predictive of whether the item was later recognized with high confidence or forgotten. By contrast, visually evoked activity in the same cells was not predictive of LTM formation. During LTM retrieval, memory-selective neurons responded more strongly to familiar stimuli for which persistent activity was high while they were maintained in WM. Our study suggests that hippocampal persistent activity of the same cells supports both WM maintenance and LTM encoding, thereby revealing a common single-neuron component of these two memory systems.

Re-evaluating human MTL in working memory: insights from intracranial recordings

The study of human working memory (WM) holds significant importance in neuroscience; yet, exploring the role of the medial temporal lobe (MTL) in WM has been limited by the technological constraints of noninvasive methods. Recent advancements in human intracranial neural recordings have indicated the involvement of the MTL in WM processes. These recordings show that different regions of the MTL are involved in distinct aspects of WM processing and also dynamically interact with each other and the broader brain network. These findings support incorporating the MTL into models of the neural basis of WM. This integration can better reflect the complex neural mechanisms underlying WM and enhance our understanding of WM's flexibility, adaptability, and precision.

Is working memory domain-general or domain-specific?

Given the fundamental role of working memory (WM) in all domains of cognition, a central question has been whether WM is domain-general. However, the term 'domain-general' has been used in different, and sometimes misleading, ways. By reviewing recent evidence and biologically plausible models of WM, we show that the level of domain-generality varies substantially between three facets of WM: in terms of computations, WM is largely domain-general. In terms of neural correlates, it contains both domain-general and domain-specific elements. Finally, in terms of application, it is mostly domain-specific. This variance encourages a shift of focus towards uncovering domain-general computational principles and away from domain-general approaches to the analysis of individual differences and WM training, favoring newer perspectives, such as training-as-skill-learning.

NeuroTD: A Time-Frequency Based Multimodal Learning Approach to Analyze Time Delays in Neural Activities

Studying temporal features of neural activities is crucial for understanding the functions of neurons as well as underlying neural circuits. To this end, recent researches employ emerging techniques including calcium imaging, Neuropixels, depth electrodes, and Patch-seq to generate multimodal time-series data that depict the activities of single neurons, groups of neurons, and behaviors. However, challenges persist, including the analysis of noisy, high-sampling-rate neuronal data, and the modeling of temporal dynamics across various modalities. To address these challenges, we developed NeuroTD, a novel deep learning approach to align multimodal time-series datasets and infer cross-modality temporal relationships such as time delays or shifts. Particularly, NeuroTD integrates Siamese neural networks with frequency domain transformations and complex value optimization for inference. We applied NeuroTD to three multimodal datasets to (1) analyze electrophysiological (ephys) time series measured by depth electrodes, identifying time delays among neurons across various positions, (2) investigate neural activity and behavioral time series data derived from Neuropixels and 3D motion captures, establishing causal relationships between neural activities and corresponding behavioral activities, and (3) explore gene expression and ephys data of single neurons from Patch-seq, identifying gene expression signatures highly correlated with time shifts in ephys responses. Finally, NeuroTD is open-source at https://github.com/daifengwanglab/NeuroTD for general use.

Timescales of learning in prefrontal cortex

The lateral prefrontal cortex (PFC) in humans and other primates is critical for immediate, goal-directed behaviour and working memory, which are classically considered distinct from the cognitive and neural circuits that support long-term learning and memory. Over the past few years, a reconsideration of this textbook perspective has emerged, in that different timescales of memory-guided behaviour are in constant interaction during the pursuit of immediate goals. Here, we will first detail how neural activity related to the shortest timescales of goal-directed behaviour (which requires maintenance of current states and goals in working memory) is sculpted by long-term knowledge and learning - that is, how the past informs present behaviour. Then, we will outline how learning across different timescales (from seconds to years) drives plasticity in the primate lateral PFC, from single neuron firing rates to mesoscale neuroimaging activity patterns. Finally, we will review how, over days and months of learning, dense local and long-range connectivity patterns in PFC facilitate longer-lasting changes in population activity by changing synaptic weights and recruiting additional neural resources to inform future behaviour. Our Review sheds light on how the machinery of plasticity in PFC circuits facilitates the integration of learned experiences across time to best guide adaptive behaviour.

Simultaneous invasive and non-invasive recordings in humans: A novel Rosetta stone for deciphering brain activity

Simultaneous noninvasive and invasive electrophysiological recordings provide a unique opportunity to achieve a comprehensive understanding of human brain activity, much like a Rosetta stone for human neuroscience. In this review we focus on the increasingly-used powerful combination of intracranial electroencephalography (iEEG) with scalp electroencephalography (EEG) or magnetoencephalography (MEG). We first provide practical insight on how to achieve these technically challenging recordings. We then provide examples from clinical research on how simultaneous recordings are advancing our understanding of epilepsy. This is followed by the illustration of how human neuroscience and methodological advances could benefit from these simultaneous recordings. We conclude with a call for open data sharing and collaboration, while ensuring neuroethical approaches and argue that only with a true collaborative approach the promises of simultaneous recordings will be fulfilled.

Scalp Electroencephalogram-Derived Involvement Indexes during a Working Memory Task Performed by Patients with Epilepsy

Electroencephalography (EEG) wearable devices are particularly suitable for monitoring a subject's engagement while performing daily cognitive tasks. EEG information provided by wearable devices varies with the location of the electrodes, the suitable location of which can be obtained using standard multi-channel EEG recorders. Cognitive engagement can be assessed during working memory (WM) tasks, testing the mental ability to process information over a short period of time. WM could be impaired in patients with epilepsy. This study aims to evaluate the cognitive engagement of nine patients with epilepsy, coming from a public dataset by Boran et al., during a verbal WM task and to identify the most suitable location of the electrodes for this purpose. Cognitive engagement was evaluated by computing 37 engagement indexes based on the ratio of two or more EEG rhythms assessed by their spectral power. Results show that involvement index trends follow changes in cognitive engagement elicited by the WM task, and, overall, most changes appear most pronounced in the frontal regions, as observed in healthy subjects. Therefore, involvement indexes can reflect cognitive status changes, and frontal regions seem to be the ones to focus on when designing a wearable mental involvement monitoring EEG system, both in physiological and epileptic conditions.

Persistent activity during working memory maintenance predicts long-term memory formation in the human hippocampus

Working Memory (WM) and Long-Term Memory (LTM) are often viewed as separate cognitive systems. Little is known about how these systems interact when forming memories. We recorded single neurons in the human medial temporal lobe while patients maintained novel items in WM and a subsequent recognition memory test for the same items. In the hippocampus but not the amygdala, the level of WM content-selective persist activity during WM maintenance was predictive of whether the item was later recognized with high confidence or forgotten. In contrast, visually evoked activity in the same cells was not predictive of LTM formation. During LTM retrieval, memory-selective neurons responded more strongly to familiar stimuli for which persistent activity was high while they were maintained in WM. Our study suggests that hippocampal persistent activity of the same cell supports both WM maintenance and LTM encoding, thereby revealing a common single-neuron component of these two memory systems.

Adaptive modeling and inference of higher-order coordination in neuronal assemblies: A dynamic greedy estimation approach

Central in the study of population codes, coordinated ensemble spiking activity is widely observable in neural recordings with hypothesized roles in robust stimulus representation, interareal communication, and learning and memory formation. Model-free measures of synchrony characterize coherent pairwise activity but not higher-order interactions, a limitation transcended by statistical models of ensemble spiking activity. However, existing model-based analyses often impose assumptions about the relevance of higher-order interactions and require repeated trials to characterize dynamics in the correlational structure of ensemble activity. To address these shortcomings, we propose an adaptive greedy filtering algorithm based on a discretized mark point-process model of ensemble spiking and a corresponding statistical inference framework to identify significant higher-order coordination. In the course of developing a precise statistical test, we show that confidence intervals can be constructed for greedily estimated parameters. We demonstrate the utility of our proposed methods on simulated neuronal assemblies. Applied to multi-electrode recordings from human and rat cortical assemblies, our proposed methods provide new insights into the dynamics underlying localized population activity during transitions between brain states.

Neuronal population representation of human emotional memory

Understanding how emotional processing modulates learning and memory is crucial for the treatment of neuropsychiatric disorders characterized by emotional memory dysfunction. We investigate how human medial temporal lobe (MTL) neurons support emotional memory by recording spiking activity from the hippocampus, amygdala, and entorhinal cortex during encoding and recognition sessions of an emotional memory task in patients with pharmaco-resistant epilepsy. Our findings reveal distinct representations for both remembered compared to forgotten and emotional compared to neutral scenes in single units and MTL population spiking activity. Additionally, we demonstrate that a distributed network of human MTL neurons exhibiting mixed selectivity on a single-unit level collectively processes emotion and memory as a network, with a small percentage of neurons responding conjointly to emotion and memory. Analyzing spiking activity enables a detailed understanding of the neurophysiological mechanisms underlying emotional memory and could provide insights into how emotion alters memory during healthy and maladaptive learning.

Alterations in hippocampus-centered morphological features and function of the progression from normal cognition to mild cognitive impairment

Mild cognitive impairment (MCI) is a significant precursor to dementia, highlighting the critical need for early identification of individuals at high risk of MCI to prevent cognitive decline. The study aimed to investigate the changes in brain structure and function before the onset of MCI. This study enrolled 19 older adults with progressive normal cognition (pNC) to MCI and 19 older adults with stable normal cognition (sNC). The gray matter (GM) volume and functional connectivity (FC) were estimated via magnetic resonance imaging during their normal cognition state 3 years prior. Additionally, spatial associations between FC maps and neurochemical profiles were examined using JuSpace. Compared to the sNC group, the pNC group showed decreased volume in the left hippocampus and left amygdala. The significantly positive correlation was observed between the GM volume of the left hippocampus and the MMSE scores after 3 years in pNC group. Besides, it showed that the pNC group had increased FC between the left hippocampus and the anterior-posterior cingulate gyrus, which was significantly correlated with the spatial distribution of dopamine D2 and noradrenaline transporter. Taken together, the study identified the abnormal brain characteristics before the onset of MCI, which might provide insight into clinical research.

Cross-frequency coupling in cortico-hippocampal networks supports the maintenance of sequential auditory information in short-term memory

It has been suggested that cross-frequency coupling in cortico-hippocampal networks enables the maintenance of multiple visuo-spatial items in working memory. However, whether this mechanism acts as a global neural code for memory retention across sensory modalities remains to be demonstrated. Intracranial EEG data were recorded while drug-resistant patients with epilepsy performed a delayed matched-to-sample task with tone sequences. We manipulated task difficulty by varying the memory load and the duration of the silent retention period between the to-be-compared sequences. We show that the strength of theta-gamma phase amplitude coupling in the superior temporal sulcus, the inferior frontal gyrus, the inferior temporal gyrus, and the hippocampus (i) supports the short-term retention of auditory sequences; (ii) decodes correct and incorrect memory trials as revealed by machine learning analysis; and (iii) is positively correlated with individual short-term memory performance. Specifically, we show that successful task performance is associated with consistent phase coupling in these regions across participants, with gamma bursts restricted to specific theta phase ranges corresponding to higher levels of neural excitability. These findings highlight the role of cortico-hippocampal activity in auditory short-term memory and expand our knowledge about the role of cross-frequency coupling as a global biological mechanism for information processing, integration, and memory in the human brain.

Human Hippocampal Ripples Signal Encoding of Episodic Memories

Direct human brain recordings have confirmed the presence of high-frequency oscillatory events, termed ripples, during awake behavior. While many prior studies have focused on medial temporal lobe (MTL) ripples during memory retrieval, here we investigate ripples during memory encoding. Specifically, we ask whether ripples during encoding predict whether and how memories are subsequently recalled. Detecting ripples from MTL electrodes implanted in 116 neurosurgical participants (n = 61 male) performing a verbal episodic memory task, we find that encoding ripples do not distinguish recalled from not recalled items in any MTL region, even as high-frequency activity during encoding predicts recall in these same regions. Instead, hippocampal ripples increase during encoding of items that subsequently lead to recall of temporally and semantically associated items during retrieval, a phenomenon known as clustering. This subsequent clustering effect arises specifically when hippocampal ripples co-occur during encoding and retrieval, suggesting that ripples mediate both encoding and reinstatement of episodic memories.

Temporal and Spatial Information Elicit Different Power and Connectivity Profiles during Working Memory Maintenance

Working memory (WM) is the cognitive ability to store and manipulate information necessary for ongoing tasks. Although frontoparietal areas are involved in the retention of visually presented information, oscillatory neural activity differs for temporal and spatial WM processing. In this study, we corroborated previous findings describing the modulation of neural oscillations and expanded our investigation to the network organization underlying the cognitive processing of temporal and spatial information. We utilized MEG recordings during a Sternberg visual WM task. The spectral oscillatory activity in the maintenance phase revealed increased frontal theta (4-8 Hz) and parietal beta (13-30 Hz) in the temporal condition. Source level coherence analysis delineated the prominent role of parietal areas in all frequency bands during the maintenance of temporal information, whereas frontal and central areas showed major contributions in theta and beta ranges during the maintenance of spatial information. Our study revealed distinct spectral profiles of neural oscillations for separate cognitive subdomains of WM processing. The delineation of specific functional networks might have important implications for clinical applications, enabling the development of stimulation protocols targeting cognitive disabilities associated with WM impairments.

Predictable and unpredictable deviance detection in the human hippocampus and amygdala

Our brains extract structure from the environment and form predictions given past experience. Predictive circuits have been identified in wide-spread cortical regions. However, the contribution of medial temporal structures in predictions remains under-explored. The hippocampus underlies sequence detection and is sensitive to novel stimuli, sufficient to gain access to memory, while the amygdala to novelty. Yet, their electrophysiological profiles in detecting predictable and unpredictable deviant auditory events remain unknown. Here, we hypothesized that the hippocampus would be sensitive to predictability, while the amygdala to unexpected deviance. We presented epileptic patients undergoing presurgical monitoring with standard and deviant sounds, in predictable or unpredictable contexts. Onsets of auditory responses and unpredictable deviance effects were detected earlier in the temporal cortex compared with the amygdala and hippocampus. Deviance effects in 1-20 Hz local field potentials were detected in the lateral temporal cortex, irrespective of predictability. The amygdala showed stronger deviance in the unpredictable context. Low-frequency deviance responses in the hippocampus (1-8 Hz) were observed in the predictable but not in the unpredictable context. Our results reveal a distributed network underlying the generation of auditory predictions and suggest that the neural basis of sensory predictions and prediction error signals needs to be extended.

Theta-Alpha Connectivity in the Hippocampal-Entorhinal Circuit Predicts Working Memory Load

Working memory (WM) maintenance relies on multiple brain regions and inter-regional communications. The hippocampus and entorhinal cortex (EC) are thought to support this operation. Besides, EC is the main gateway for information between the hippocampus and neocortex. However, the circuit-level mechanism of this interaction during WM maintenance remains unclear in humans. To address these questions, we recorded the intracranial electroencephalography from the hippocampus and EC while patients (N = 13, six females) performed WM tasks. We found that WM maintenance was accompanied by enhanced theta/alpha band (2-12 Hz) phase synchronization between the hippocampus to the EC. The Granger causality and phase slope index analyses consistently showed that WM maintenance was associated with theta/alpha band-coordinated unidirectional influence from the hippocampus to the EC. Besides, this unidirectional inter-regional communication increased with WM load and predicted WM load during memory maintenance. These findings demonstrate that WM maintenance in humans engages the hippocampal-entorhinal circuit, with the hippocampus influencing the EC in a load-dependent manner.

Wake slow waves in focal human epilepsy impact network activity and cognition

Slow waves of neuronal activity are a fundamental component of sleep that are proposed to have homeostatic and restorative functions. Despite this, their interaction with pathology is unclear and there is only indirect evidence of their presence during wakefulness. Using intracortical recordings from the temporal lobe of 25 patients with epilepsy, we demonstrate the existence of local wake slow waves (LoWS) with key features of sleep slow waves, including a down-state of neuronal firing. Consistent with a reduction in neuronal activity, LoWS were associated with slowed cognitive processing. However, we also found that LoWS showed signatures of a homeostatic relationship with interictal epileptiform discharges (IEDs): exhibiting progressive adaptation during the build-up of network excitability before an IED and reducing the impact of subsequent IEDs on network excitability. We therefore propose an epilepsy homeostasis hypothesis: that slow waves in epilepsy reduce aberrant activity at the price of transient cognitive impairment.

Adaptive modeling and inference of higher-order coordination in neuronal assemblies: a dynamic greedy estimation approach

Central in the study of population codes, coordinated ensemble spiking activity is widely observable in neural recordings with hypothesized roles in robust stimulus representation, interareal communication, and learning and memory formation. Model-free measures of synchrony characterize coherent pairwise activity but not higher-order interactions, a limitation transcended by statistical models of ensemble spiking activity. However, existing model-based analyses often impose assumptions about the relevance of higher-order interactions and require repeated trials to characterize dynamics in the correlational structure of ensemble activity. To address these shortcomings, we propose an adaptive greedy filtering algorithm based on a discretized mark point-process model of ensemble spiking and a corresponding statistical inference framework to identify significant higher-order coordination. In the course of developing a precise statistical test, we show that confidence intervals can be constructed for greedily estimated parameters. We demonstrate the utility of our proposed methods on simulated neuronal assemblies. Applied to multi-electrode recordings from human and rat cortical assemblies, our proposed methods provide new insights into the dynamics underlying localized population activity during transitions between brain states.

Neural evidence of switch processes during semantic and phonetic foraging in human memory

Humans may retrieve words from memory by exploring and exploiting in "semantic space" similar to how nonhuman animals forage for resources in physical space. This has been studied using the verbal fluency test (VFT), in which participants generate words belonging to a semantic or phonetic category in a limited time. People produce bursts of related items during VFT, referred to as "clustering" and "switching." The strategic foraging model posits that cognitive search behavior is guided by a monitoring process which detects relevant declines in performance and then triggers the searcher to seek a new patch or cluster in memory after the current patch has been depleted. An alternative body of research proposes that this behavior can be explained by an undirected rather than strategic search process, such as random walks with or without random jumps to new parts of semantic space. This study contributes to this theoretical debate by testing for neural evidence of strategically timed switches during memory search. Thirty participants performed category and letter VFT during functional MRI. Responses were classified as cluster or switch events based on computational metrics of similarity and participant evaluations. Results showed greater hippocampal and posterior cerebellar activation during switching than clustering, even while controlling for interresponse times and linguistic distance. Furthermore, these regions exhibited ramping activity which increased during within-patch search leading up to switches. Findings support the strategic foraging model, clarifying how neural switch processes may guide memory search in a manner akin to foraging in patchy spatial environments.

Working memory load impairs transfer learning in human adults

Transfer of learning refers to successful application of previously acquired knowledge or skills to novel settings. Although working memory (WM) is thought to play a role in transfer learning, direct evidence of the effect of limitations in WM on transfer learning is lacking. To investigate, we used an acquired equivalence paradigm that included tests of association and transfer learning. The effects of imposing an acute WM limitation on young adults was tested (within-subjects design: N = 27 adults; Mage = 24 years) by conducting learning transfer tests concurrent with a secondary task that required carrying a spatial WM load when performing the learned/transfer trial (Load condition) to acutely limit WM resources or no WM load (No-Load condition; WM was unloaded prior to performing the learned/transfer trial). Analyses showed that although success on the transfer trials was high in the No-Load condition, performance dropped to chance in the Load condition. Performance on tests of learned associations remained high in both conditions. These results indicate that transfer of learning depends on access to WM resources and suggest that even healthy young individuals may be affected in their ability to cross-utilize knowledge when cognitive resources become scarce, such as when engaging in two tasks simultaneously (e.g., using satellite navigation while driving).

Electrophysiological characteristics of CM-pf in diagnosis and outcome of patients with disorders of consciousness

BACKGROUND

Deep brain stimulation (DBS) in the centromedian-parafascicular complex (CM-pf) has been reported as a potential therapeutic option for disorders of consciousness (DoC). However, the lack of understanding of its electrophysiological characteristics limits the improvement of therapeutic effect.

OBJECTIVE

To investigate the CM-pf electrophysiological characteristics underlying disorders of consciousness (DoC) and its recovery.

METHODS

We collected the CM-pf electrophysiological signals from 23 DoC patients who underwent central thalamus DBS (CT-DBS) surgery. Five typical electrophysiological features were extracted, including neuronal firing properties, multiunit activity (MUA) properties, signal stability, spike-MUA synchronization strength (syncMUA), and the background noise level. Their correlations with the consciousness level, the outcome, and the primary clinical factors of DoC were analyzed.

RESULTS

11 out of 23 patients (0/2 chronic coma, 5/13 unresponsive wakefulness syndrome/vegetative state (UWS/VS), 6/8 minimally conscious state minus (MCS-)) exhibited an improvement in the level of consciousness after CT-DBS. In CM-pf, significantly stronger gamma band syncMUA strength and alpha band normalized MUA power were found in MCS- patients. In addition, higher firing rates, stronger high-gamma band MUA power and alpha band normalized power, and more stable theta oscillation were correlated with better outcomes. Besides, we also identified electrophysiological properties that are correlated with clinical factors, including etiologies, age, and duration of DoC.

CONCLUSION

We provide comprehensive analyses of the electrophysiological characteristics of CM-pf in DoC patients. Our results support the 'mesocircuit' hypothesis, one proposed mechanism of DoC recovery, and reveal CM-pf electrophysiological features that are crucial for understanding the pathogenesis of DoC, predicting its recovery, and explaining the effect of clinical factors on DoC.

Paving the Way for Memory Enhancement: Development and Examination of a Neurofeedback System Targeting the Medial Temporal Lobe

Neurofeedback (NF) shows promise in enhancing memory, but its application to the medial temporal lobe (MTL) still needs to be studied. Therefore, we aimed to develop an NF system for the memory function of the MTL and examine neural activity changes and memory task score changes through NF training. We created a memory NF system using intracranial electrodes to acquire and visualise the neural activity of the MTL during memory encoding. Twenty trials of a tug-of-war game per session were employed for NF and designed to control neural activity bidirectionally (Up/Down condition). NF training was conducted with three patients with drug-resistant epilepsy, and we observed an increasing difference in NF signal between conditions (Up-Down) as NF training progressed. Similarities and negative correlation tendencies between the transition of neural activity and the transition of memory function were also observed. Our findings demonstrate NF's potential to modulate MTL activity and memory encoding. Future research needs further improvements to the NF system to validate its effects on memory functions. Nonetheless, this study represents a crucial step in understanding NF's application to memory and provides valuable insights into developing more efficient memory enhancement strategies.

Cognitive neuroscience perspective on memory: overview and summary

This paper explores memory from a cognitive neuroscience perspective and examines associated neural mechanisms. It examines the different types of memory: working, declarative, and non-declarative, and the brain regions involved in each type. The paper highlights the role of different brain regions, such as the prefrontal cortex in working memory and the hippocampus in declarative memory. The paper also examines the mechanisms that underlie the formation and consolidation of memory, including the importance of sleep in the consolidation of memory and the role of the hippocampus in linking new memories to existing cognitive schemata. The paper highlights two types of memory consolidation processes: cellular consolidation and system consolidation. Cellular consolidation is the process of stabilizing information by strengthening synaptic connections. System consolidation models suggest that memories are initially stored in the hippocampus and are gradually consolidated into the neocortex over time. The consolidation process involves a hippocampal-neocortical binding process incorporating newly acquired information into existing cognitive schemata. The paper highlights the role of the medial temporal lobe and its involvement in autobiographical memory. Further, the paper discusses the relationship between episodic and semantic memory and the role of the hippocampus. Finally, the paper underscores the need for further research into the neurobiological mechanisms underlying non-declarative memory, particularly conditioning. Overall, the paper provides a comprehensive overview from a cognitive neuroscience perspective of the different processes involved in memory consolidation of different types of memory.

No convincing evidence the hippocampus is associated with working memory

In a previous discussion paper , twenty-six working memory fMRI studies that reported activity in the hippocampus were systematically analyzed. None of these studies provided convincing evidence that the hippocampus was active during the late delay phase, the only period in which working memory can be isolated from long-term memory processes. Based on these results, it was concluded that working memory does not activate the hippocampus. Six commentaries on the discussion paper were received from Courtney (2022), Kessels & Bergmann (2022), Peters and Reithler (2022), Rose and Chao (2022), Stern & Hasselmo (2022), and Wood, Clark, and Nee (2022). Based on these commentaries, the present response paper considered whether there is evidence of sustained hippocampal activity during the working memory delay period based on depth-electrode recording, whether there are activity-silent working memory mechanisms in the hippocampus, and whether there is hippocampal lesion evidence indicating this region is important for working memory. There was no convincing electrophysiological or neuropsychological evidence that the hippocampus is associated with working memory maintenance, and activity-silent mechanisms were arguably speculative. Given that only a small fraction (approximately 5%) of fMRI studies have reported hippocampal activity in working memory tasks and lesion evidence indicates the hippocampus is not necessary for working memory, the burden of proof is on proponents of view that the hippocampus is important for working memory to provide compelling evidence to support their position. To date, in my view, there is no convincing evidence that the hippocampus is associated with working memory.

Functional specialization and interaction in the amygdala-hippocampus circuit during working memory processing

Both the hippocampus and amygdala are involved in working memory (WM) processing. However, their specific role in WM is still an open question. Here, we simultaneously recorded intracranial EEG from the amygdala and hippocampus of epilepsy patients while performing a WM task, and compared their representation patterns during the encoding and maintenance periods. By combining multivariate representational analysis and connectivity analyses with machine learning methods, our results revealed a functional specialization of the amygdala-hippocampal circuit: The mnemonic representations in the amygdala were highly distinct and decreased from encoding to maintenance. The hippocampal representations, however, were more similar across different items but remained stable in the absence of the stimulus. WM encoding and maintenance were associated with bidirectional information flow between the amygdala and the hippocampus in low-frequency bands (1-40 Hz). Furthermore, the decoding accuracy on WM load was higher by using representational features in the amygdala during encoding and in the hippocampus during maintenance, and by using information flow from the amygdala during encoding and that from the hippocampus during maintenance, respectively. Taken together, our study reveals that WM processing is associated with functional specialization and interaction within the amygdala-hippocampus circuit.

Abnormal dynamic functional connectivity of hippocampal subregions associated with working memory impairment in melancholic depression

BACKGROUND

Previous studies have demonstrated structural and functional changes of the hippocampus in patients with major depressive disorder (MDD). However, no studies have analyzed the dynamic functional connectivity (dFC) of hippocampal subregions in melancholic MDD. We aimed to reveal the patterns for dFC variability in hippocampus subregions - including the bilateral rostral and caudal areas and its associations with cognitive impairment in melancholic MDD.

METHODS

Forty-two treatment-naive MDD patients with melancholic features and 55 demographically matched healthy controls were included. The sliding-window analysis was used to evaluate whole-brain dFC for each hippocampal subregions seed. We assessed between-group differences in the dFC variability values of each hippocampal subregion in the whole brain and cognitive performance on the MATRICS Consensus Cognitive Battery (MCCB). Finally, association analysis was conducted to investigate their relationships.

RESULTS

Patients with melancholic MDD showed decreased dFC variability between the left rostral hippocampus and left anterior lobe of cerebellum compared with healthy controls (voxel p < 0.005, cluster p < 0.0125, GRF corrected), and poorer cognitive scores in working memory, verbal learning, visual learning, and social cognition (all p < 0.05). Association analysis showed that working memory was positively correlated with the dFC variability values of the left rostral hippocampus-left anterior lobe of the cerebellum (r = 0.338, p = 0.029) in melancholic MDD.

CONCLUSIONS

These findings confirmed the distinct dynamic functional pathway of hippocampal subregions in patients with melancholic MDD, and suggested that the dysfunction of hippocampus-cerebellum connectivity may be underlying the neural substrate of working memory impairment in melancholic MDD.

Control of working memory maintenance by theta-gamma phase amplitude coupling of human hippocampal neurons

Retaining information in working memory (WM) is a demanding process that relies on cognitive control to protect memoranda-specific persistent activity from interference. How cognitive control regulates WM storage, however, remains unknown. We hypothesized that interactions of frontal control and hippocampal persistent activity are coordinated by theta-gamma phase amplitude coupling (TG-PAC). We recorded single neurons in the human medial temporal and frontal lobe while patients maintained multiple items in WM. In the hippocampus, TG-PAC was indicative of WM load and quality. We identified cells that selectively spiked during nonlinear interactions of theta phase and gamma amplitude. These PAC neurons were more strongly coordinated with frontal theta activity when cognitive control demand was high, and they introduced information-enhancing and behaviorally relevant noise correlations with persistently active neurons in the hippocampus. We show that TG-PAC integrates cognitive control and WM storage to improve the fidelity of WM representations and facilitate behavior.

The medial temporal lobe supports the quality of visual short-term memory representation

The quality of short-term memory (STM) underlies our ability to recall the exact details of a recent event, yet how the human brain enables this core cognitive function remains poorly understood. Here we use multiple experimental approaches to test the hypothesis that the quality of STM, such as its precision or fidelity, relies on the medial temporal lobe (MTL), a region commonly associated with the ability to distinguish similar information remembered in long-term memory. First, with intracranial recordings, we find that delay-period MTL activity retains item-specific STM content that is predictive of subsequent recall precision. Second, STM recall precision is associated with an increase in the strength of intrinsic MTL-to-neocortical functional connections during a brief retention interval. Finally, perturbing the MTL through electrical stimulation or surgical removal can selectively reduce STM precision. Collectively, these findings provide converging evidence that the MTL is critically involved in the quality of STM representation.

The entorhinal-DG/CA3 pathway in the medial temporal lobe retains visual working memory of a simple surface feature

Classic models consider working memory (WM) and long-term memory as distinct mental faculties that are supported by different neural mechanisms. Yet, there are significant parallels in the computation that both types of memory require. For instance, the representation of precise item-specific memory requires the separation of overlapping neural representations of similar information. This computation has been referred to as pattern separation, which can be mediated by the entorhinal-DG/CA3 pathway of the medial temporal lobe (MTL) in service of long-term episodic memory. However, although recent evidence has suggested that the MTL is involved in WM, the extent to which the entorhinal-DG/CA3 pathway supports precise item-specific WM has remained elusive. Here, we combine an established orientation WM task with high-resolution fMRI to test the hypothesis that the entorhinal-DG/CA3 pathway retains visual WM of a simple surface feature. Participants were retrospectively cued to retain one of the two studied orientation gratings during a brief delay period and then tried to reproduce the cued orientation as precisely as possible. By modeling the delay-period activity to reconstruct the retained WM content, we found that the anterior-lateral entorhinal cortex (aLEC) and the hippocampal DG/CA3 subfield both contain item-specific WM information that is associated with subsequent recall fidelity. Together, these results highlight the contribution of MTL circuitry to item-specific WM representation.

An explainable artificial intelligence approach to spatial navigation based on hippocampal circuitry

Learning to navigate a complex environment is not a difficult task for a mammal. For example, finding the correct way to exit a maze following a sequence of cues, does not need a long training session. Just a single or a few runs through a new environment is, in most cases, sufficient to learn an exit path starting from anywhere in the maze. This ability is in striking contrast with the well-known difficulty that any deep learning algorithm has in learning a trajectory through a sequence of objects. Being able to learn an arbitrarily long sequence of objects to reach a specific place could take, in general, prohibitively long training sessions. This is a clear indication that current artificial intelligence methods are essentially unable to capture the way in which a real brain implements a cognitive function. In previous work, we have proposed a proof-of-principle model demonstrating how, using hippocampal circuitry, it is possible to learn an arbitrary sequence of known objects in a single trial. We called this model SLT (Single Learning Trial). In the current work, we extend this model, which we will call e-STL, to introduce the capability of navigating a classic four-arms maze to learn, in a single trial, the correct path to reach an exit ignoring dead ends. We show the conditions under which the e-SLT network, including cells coding for places, head-direction, and objects, can robustly and efficiently implement a fundamental cognitive function. The results shed light on the possible circuit organization and operation of the hippocampus and may represent the building block of a new generation of artificial intelligence algorithms for spatial navigation.

Network instability dynamics drive a transient bursting period in the developing hippocampus in vivo

Spontaneous correlated activity is a universal hallmark of immature neural circuits. However, the cellular dynamics and intrinsic mechanisms underlying network burstiness in the intact developing brain are largely unknown. Here, we use two-photon Ca2+ imaging to comprehensively map the developmental trajectories of spontaneous network activity in the hippocampal area CA1 of mice in vivo. We unexpectedly find that network burstiness peaks after the developmental emergence of effective synaptic inhibition in the second postnatal week. We demonstrate that the enhanced network burstiness reflects an increased functional coupling of individual neurons to local population activity. However, pairwise neuronal correlations are low, and network bursts (NBs) recruit CA1 pyramidal cells in a virtually random manner. Using a dynamic systems modeling approach, we reconcile these experimental findings and identify network bi-stability as a potential regime underlying network burstiness at this age. Our analyses reveal an important role of synaptic input characteristics and network instability dynamics for NB generation. Collectively, our data suggest a mechanism, whereby developing CA1 performs extensive input-discrimination learning prior to the onset of environmental exploration.

Hippocampal involvement in working memory following refreshing

Working memory (WM) and long-term memory (LTM) tests have both overlapping and distinct neurocognitive processes. Hippocampal activity in fMRI studies-a hallmark of LTM-also occurs on WM tasks, typically during encoding or retrieval and sometimes (albeit rarely) through 'late-delay' periods. The Synaptic Theory of WM suggests that 'activity-silent' synaptic weights retain temporary, WM-relevant codes without sustained, elevated activity. The hippocampus temporarily retains item-context bindings during WM-delays that are typically 'silent' to fMRI, probably via oscillatory patterns of informational connectivity among task-relevant regions of cortex. Advancing WM theory will require modeling this dynamic interplay, as in the 'Dynamic Processing Model of WM.

Extreme conditions affect neuronal oscillations of cerebral cortices in humans in the China Space Station and on Earth

Rhythmical oscillations of neural populations can reflect working memory performance. However, whether neuronal oscillations of the cerebral cortex change in extreme environments, especially in a space station, remains unclear. Here, we recorded electroencephalography (EEG) signals when volunteers and astronauts were executing a memory task in extreme working conditions. Our experiments showed that two extreme conditions affect neuronal oscillations of the cerebral cortex and manifest in different ways. Lengthy periods of mental work impairs the gating mechanism formed by theta-gamma phase-amplitude coupling of two cortical areas, and sleep deprivation disrupts synaptic homeostasis, as reflected by the substantial increase in theta wave activity in the cortical frontal-central area. In addition, we excluded the possibility that nutritional supply or psychological situations caused decoupled theta-gamma phase-amplitude coupling or an imbalance in theta wave activity increase. Therefore, we speculate that the decoupled theta-gamma phase-amplitude coupling detected in astronauts results from their lengthy periods of mental work in the China Space Station. Furthermore, comparing preflight and inflight experiments, we find that long-term spaceflight and other hazards in the space station could worsen this decoupling evolution. This particular neuronal oscillation mechanism in the cerebral cortex could guide countermeasures for the inadaptability of humans working in spaceflight.

Information flows from hippocampus to auditory cortex during replay of verbal working memory items

The maintenance of items in working memory (WM) relies on a widespread network of cortical areas and hippocampus where synchronization between electrophysiological recordings reflects functional coupling. We investigated the direction of information flow between auditory cortex and hippocampus while participants heard and then mentally replayed strings of letters in WM by activating their phonological loop. We recorded local field potentials from the hippocampus, reconstructed beamforming sources of scalp EEG, and – additionally in four participants – recorded from subdural cortical electrodes. When analyzing Granger causality, the information flow was from auditory cortex to hippocampus with a peak in the [4 8] Hz range while participants heard the letters. This flow was subsequently reversed during maintenance while participants maintained the letters in memory. The functional interaction between hippocampus and the cortex and the reversal of information flow provide a physiological basis for the encoding of memory items and their active replay during maintenance.

Every day, the brain’s ability to temporarily store and recall information – called working memory – enables us to reason, solve complex problems or to speak. Holding pieces of information in working memory for short periods of times is a skill that relies on communication between neural circuits that span several areas of the brain. The hippocampus, a seahorse-shaped area at the centre of the brain, is well-known for its role in learning and memory. Less clear, however, is how brain regions that process sensory inputs, including visual stimuli and sounds, contribute to working memory.

To investigate, Dimakopoulos et al. studied the flow of information between the hippocampus and the auditory cortex, which processes sound. To do so, various types of electrodes were placed on the scalp or surgically implanted in the brains of people with drug-resistant epilepsy. These electrodes measured the brain activity of participants as they read, heard and then mentally replayed strings of up to 8 letters. The electrical signals analysed reflected the flow of information between brain areas.

When participants read and heard the sequence of letters, brain signals flowed from the auditory cortex to the hippocampus. The flow of electrical activity was reversed while participants recalled the letters. This pattern was found only in the left side of the brain, as expected for a language related task, and only if participants recalled the letters correctly.

This work by Dimakopoulos et al. provides the first evidence of bidirectional communication between brain areas that are active when people memorise and recall information from their working memory. In doing so, it provides a physiological basis for how the brain encodes and replays information stored in working memory, which evidently relies on the interplay between the hippocampus and sensory cortex.

Differences in memory for what, where, and when components of recently formed episodes

An integral feature of human memory is the ability to recall past events. What distinguishes such episodic memory from semantic or associative memory is the joint encoding and retrieval of "what," "where," and "when" (WWW) for such events. Surprisingly, little work has addressed whether all three components of WWW are retrieved with equal fidelity when remembering episodes. To study this question, we created a novel task where human participants identified matched or mismatched still images sampled from recently viewed synthetic movies. The mismatch images only probe one of the three WWW components at a time, allowing us to separately test accuracies for each component of the episodes. Crucially, each WWW component in the movies is easily distinguishable in isolation, thereby making any differences in accuracy between components due to how they are joined in memory. We find that memory for "when" has the lowest accuracy, with it being the component most influenced by primacy and recency. Furthermore, the memory of "when" is most susceptible to interference due to changes in task load. These findings suggest that episodes are not stored and retrieved as a coherent whole but instead their components are either stored or retrieved differentially as part of an active reconstruction process. NEW & NOTEWORTHY When we store and subsequently retrieve episodes, does the brain encode them holistically or in separate parts that are later reconstructed? Using a task where participants study abstract episodes and on any given trial are probed on the what, where, and when components, we find mnemonic differences between them. Accuracy for "when" memory is the lowest, as it is most influenced by primacy, recency, and interference, suggesting that episodes are not treated holistically by the brain.

Sequence Learning in a Single Trial: A Spiking Neurons Model Based on Hippocampal Circuitry

In contrast with our everyday experience using brain circuits, it can take a prohibitively long time to train a computational system to produce the correct sequence of outputs in the presence of a series of inputs. This suggests that something important is missing in the way in which models are trying to reproduce basic cognitive functions. In this work, we introduce a new neuronal network architecture that is able to learn, in a single trial, an arbitrary long sequence of any known objects. The key point of the model is the explicit use of mechanisms and circuitry observed in the hippocampus, which allow the model to reach a level of efficiency and accuracy that, to the best of our knowledge, is not possible with abstract network implementations. By directly following the natural system's layout and circuitry, this type of implementation has the additional advantage that the results can be more easily compared to the experimental data, allowing a deeper and more direct understanding of the mechanisms underlying cognitive functions and dysfunctions and opening the way to a new generation of learning architectures.

Phase-amplitude coupling between low-frequency scalp EEG and high-frequency intracranial EEG during working memory task

Objective. Revealing the relationship between simultaneous scalp electroencephalography (EEG) and intracranial electroencephalography (iEEG) is of great importance for both neuroscientific research and translational applications. However, whether prominent iEEG features in the high-gamma band can be reflected by scalp EEG is largely unknown. To address this, we investigated the phase-amplitude coupling (PAC) phenomenon between the low-frequency band of scalp EEG and the high-gamma band of iEEG.Approach. We analyzed a simultaneous iEEG and scalp EEG dataset acquired under a verbal working memory paradigm from nine epilepsy subjects. The PAC values between pairs of scalp EEG channel and identified iEEG channel were explored. After identifying the frequency combinations and electrode locations that generated the most significant PAC values, we compared the PAC values of different task periods (encoding, maintenance, and retrieval) and memory loads.Main results. We demonstrated that the amplitude of high-gamma activities in the entorhinal cortex, hippocampus, and amygdala was correlated to the delta or theta phase at scalp locations such as Cz and Pz. In particular, the frequency bin that generated the maximum PAC value centered at 3.16-3.84 Hz for the phase and 50-85 Hz for the amplitude. Moreover, our results showed that PAC values for the retrieval period were significantly higher than those of the encoding and maintenance periods, and the PAC was also influenced by the memory load.Significance. This is the first human simultaneous iEEG and scalp EEG study demonstrating that the amplitude of iEEG high-gamma components is associated with the phase of low-frequency components in scalp EEG. These findings enhance our understanding of multiscale neural interactions during working memory, and meanwhile, provide a new perspective to estimate intracranial high-frequency features with non-invasive neural recordings.

Persistent neuronal firing in the medial temporal lobe supports performance and workload of visual working memory in humans

The involvement of the medial temporal lobe (MTL) in working memory is controversially discussed. Recent findings suggest that persistent neural firing in the hippocampus during maintenance in verbal working memory is associated with workload. Here, we recorded single neuron firing in 13 epilepsy patients (7 male) while they performed a visual working memory task. The number of coloured squares in the stimulus set determined the workload of the trial. Performance was almost perfect for low workload (1 and 2 squares) and dropped at high workload (4 and 6 squares), suggesting that high workload exceeded working memory capacity. We identified maintenance neurons in MTL neurons that showed persistent firing during the maintenance period. More maintenance neurons were found in the hippocampus for trials with correct compared to incorrect performance. Maintenance neurons increased and decreased firing in the hippocampus and increased firing in the entorhinal cortex for high compared to low workload. Population firing predicted workload particularly during the maintenance period. Prediction accuracy of workload based on single-trial activity during maintenance was strongest for neurons in the entorhinal cortex and hippocampus. The data suggest that persistent neural firing in the MTL reflects a domain-general process of maintenance supporting performance and workload of multiple items in working memory below and beyond working memory capacity. Persistent neural firing during maintenance in the entorhinal cortex may be associated with its preference to process visual-spatial arrays.

Effects of M currents on the persistent activity of pyramidal neurons in mouse primary auditory cortex

Neuronal persistent activity (PA) is a common phenomenon observed in many types of neurons. PA can be induced in neurons in the mouse auditory nucleus by activating cholinergic receptors with carbachol (CCh), a dual muscarinic and nicotinic receptor agonist. PA is presumed to be associated with learning-related auditory plasticity at the cellular level. However, the mechanism is not clearly understood. Many studies have reported that muscarinic cholinergic receptor agonists inhibit muscarinic-sensitive potassium channels (M channels). Potassium influx through M channels produces potassium currents, called M currents, which play an essential role in regulating neural excitability and synaptic plasticity. Further study is needed to determine whether M currents affect the PA of auditory central neurons and provide additional analysis of the variations in electrophysiological properties. We used in vitro whole-cell patch-clamp recordings in isolated mouse brain slices to investigate the effects of M currents on the PA in pyramidal neurons in layer V of the primary auditory cortex (AI-L5). We found that blocking M currents with XE991 depolarized the AI-L5 pyramidal neurons, which significantly increased the input resistance. The active threshold and threshold intensity were significantly reduced, indicating that the intrinsic excitability was enhanced. Our results also showed that blocking M currents with XE991 switched the neuronal firing patterns in the AI-L5 pyramidal neurons from regular-spiking to intrinsic-bursting. Blocking M currents facilitated PA by increasing the plateau potential and enhancing intrinsic excitability. Our results suggested that blocking M currents might facilitate the PA in AI-L5 pyramidal neurons, which underlies auditory plasticity.

Neurons as will and representation

Memory recollections and voluntary actions are often perceived as spontaneously generated irrespective of external stimuli. Although products of our neurons, they are only rarely accessible in humans at the neuronal level. Here I review insights gleaned from unique neurosurgical opportunities to record and stimulate single-neuron activity in people who can declare their thoughts, memories and wishes. I discuss evidence that the subjective experience of human recollection and that of voluntary action arise from the activity of two internal neuronal generators, the former from medial temporal lobe reactivation and the latter from frontoparietal preactivation. I characterize properties of these generators and their interaction, enabling flexible recruitment of memory-based choices for action as well as recruitment of action-based plans for the representation of conceptual knowledge in memories. Both internal generators operate on surprisingly explicit but different neuronal codes, which appear to arise with distinct single-neuron activity, often observed before participants' reports of conscious awareness. I discuss prediction of behaviour based on these codes, and the potential for their modulation. The prospects of editing human memories and volitions by enhancement, inception or deletion of specific, selected content raise therapeutic possibilities and ethical concerns.

Retrospective memory trace sustained by the human hippocampus during working memory task

Working memory is a subcategory of short-term memory that voluntarily maintains behaviourally relevant information to prepare for a subsequent action. An established theory is that working memory is supported by the prefrontal cortex (PFC) for executive control, while the hippocampus (HPC) is largely involved in long-term episodic memory. Recent studies suggest that the HPC is also involved in perception and short-term storage. However, it remains unclear whether the HPC supports active maintenance of short-term memory as working memory. To address this question, we devised a new delayed matching-to-sample task in which two visual items were presented at different locations one by one as samples. The sequential presentations of sample stimuli allowed us to dissociate the contents of working memory (i.e., identities and locations of two samples) from the constituent perceived information of single samples. By applying representational similarity analysis (RSA) to the blood-oxygen-level-dependent (BOLD) signals of human participants, we investigated the delay activity after the two sample presentations. The results of the RSA showed that the right HPC signalled only the second sample as a conjunctional representation of its item identity and location. In contrast, the right PFC, including both lateral and medial parts, represented the conjunctional information of both samples. These results suggested that the HPC may support short-term memory for retrospective coding to retain information of the last event rather than for prospective coding coupled with working memory.