Sequential Memory Research Articles

Long sequence Hopfield memory*

Abstract Sequence memory is an essential attribute of natural and artificial intelligence that enables agents to encode, store, and retrieve complex sequences of stimuli and actions. Computational models of sequence memory have been proposed where recurrent Hopfield-like neural networks are trained with temporally asymmetric Hebbian rules. However, these networks suffer from limited sequence capacity (maximal length of the stored sequence) due to interference between the memories. Inspired by recent work on Dense Associative Memories, we expand the sequence capacity of these models by introducing a nonlinear interaction term, enhancing separation between the patterns. We derive novel scaling laws for sequence capacity with respect to network size, significantly outperforming existing scaling laws for models based on traditional Hopfield networks, and verify these theoretical results with numerical simulation. Moreover, we introduce a generalized pseudoinverse rule to recall sequences of highly correlated patterns. Finally, we extend this model to store sequences with variable timing between states’ transitions and describe a biologically-plausible implementation, with connections to motor neuroscience.

The Suppression Mechanisms of Passive Memory in Visual Working Memory: The Evidence from Electrophysiology.

Recent studies of visual working memory (VWM) underscore a structured hierarchy of storage states. Memories that are not immediately relevant to the task at hand but are essential for later use are transferred to a passive state, which operates independently of actively maintaining and manipulating current memories. Note that stimulating passive memory forcefully can reactivate it into an active state, resulting in a competition with active memory. Thus, to remain stable representations for both states within VWM, passive memory might involve sustained suppression during activity-silent maintenance to prevent reactivation from disrupting the current active storage. To investigate this, we analyzed lateralized electrophysiology signals while human participants (both women and men) were engaged in a sequential presentation memory task across two experiments. The results revealed positive contralateral delayed activity components and lateralized alpha enhancement for passive memory, neural indicative of suppression on passive storage. In addition, the suppression effect was independent of the memory load in both the active and the passive states. These findings support the notion of sustained suppression during activity-silent maintenance of passive memory, facilitating the stable maintenance of distinct storage states and advancing our understanding of the dynamic coding framework in VWM.

Distinct functions for beta and alpha bursts in gating of human working memory

Multiple neural mechanisms underlying gating to working memory have been proposed with divergent results obtained in human and animal studies. Previous findings from non-human primates suggest prefrontal beta frequency bursts as a correlate of transient inhibition during selective encoding. Human studies instead suggest a similar role for sensory alpha power fluctuations. To cast light on these discrepancies we employed a sequential working memory task with distractors for human participants. In particular, we examined their whole-brain electrophysiological activity in both alpha and beta bands with the same single-trial burst analysis earlier performed on non-human primates. Our results reconcile earlier findings by demonstrating that both alpha and beta bursts in humans correlate with the filtering and control of memory items, but with region and task-specific differences between the two rhythms. Occipital beta burst patterns were selectively modulated during the transition from sensory processing to memory retention whereas prefrontal and parietal beta bursts tracked sequence order and were proactively upregulated prior to upcoming target encoding. Occipital alpha bursts instead increased during the actual presentation of unwanted sensory stimuli. Source reconstruction additionally suggested the involvement of striatal and thalamic alpha and beta. Thus, specific whole-brain burst patterns correlate with different aspects of working memory control.

Analog Sequential Hippocampal Memory Model for Trajectory Learning and Recalling: A Robustness Analysis Overview

The rapid expansion of information systems in all areas of society demands more powerful, efficient, and low‐energy consumption computing systems. Neuromorphic engineering has emerged as a solution that attempts to mimic the brain to incorporate its capabilities to solve complex problems in a computationally and energy‐efficient way in real time. Within neuromorphic computing, building systems to efficiently store the information is still a challenge. Among all the brain regions, the hippocampus stands out as a short‐term memory capable of learning and recalling large amounts of information quickly and efficiently. Herein, a spike‐based bio‐inspired hippocampus sequential memory model is proposed that makes use of the benefits of analog computing and spiking neural networks (SNNs): noise robustness, improved real‐time operation, and energy efficiency. This model is applied to robotic navigation to learn and recall trajectories that lead to a goal position within a known grid environment. The model is implemented on the special‐purpose SNNs mixed‐signal DYNAP‐SE hardware platform. Through extensive experimentation together with an extensive analysis of the model's behavior in the presence of external noise sources, its correct functioning is demonstrated, proving the robustness and consistency of the proposed neuromorphic sequential memory system.

Local Second Order Mo̷ller-Plesset Theory with a Single Threshold Using Orthogonal Virtual Orbitals: A Distributed Memory Implementation.

In order to alleviate the computational burden associated with superlinear compute scalings with molecular size in electron correlation methods, researchers have developed local correlation methods that wisely treat relatively small contributions as zeros but still yield accurate energy approximation. Such local correlation techniques can also be combined with parallel computing resources to obtain further efficiency and scalability. This work focuses on the distributed memory parallel implementation of a local correlation method for second order Mo̷ller-Plesset (MP2) theory. This method also only has a single threshold to control the dropping of terms and accuracy of different computing kernels in the algorithm. The process partitioning strategy and distributed parallel implementation with the message passing interface (MPI) are discussed. In particular, the algorithm relies on a fixed sparsity pattern matrix multiplication and a corresponding distributed conjugate gradient solver, which exhibits almost linear scaling in both strong and weak scaling analyses. Numerical experiments on a range of molecules, including linear chains and molecules with 2 and 3-dimensional characters, are reported. For example, with only 32 MPI ranks, this MP2 implementation can calculate the correlation energy of vancomycin in def2-TZVP basis within 0.003% accuracy (10-6.5 threshold) in half an hour, where the same problem is unfeasible to solve with sequential or pure shared memory implementations.

White matter correlates of cognition: A diffusion magnetic resonance imaging study

BackgroundOur comprehension of the interplay of cognition and the brain remains constrained. While functional imaging studies have identified cognitive brain regions, structural correlates of cognitive functions remain underexplored. Advanced methods like Diffusion Magnetic Resonance Imaging (DMRI) facilitate the exploration of brain connectivity and White Matter (WM) tract microstructure. Therefore, we conducted connectometry method on DMRI data, to reveal WM tracts associated with cognition. Methods125 healthy participants from the National Institute of Mental Health Intramural Healthy Volunteer Dataset were recruited. Multiple regression analyses were conducted between DMRI-derived Quantitative Anisotropy (QA) values within WM tracts and scores of participants in Flanker Inhibitory Control and Attention Test (attention), Dimensional Change Card Sort (executive function), Picture Sequence Memory Test (episodic memory), and List Sorting Working Memory Test (working memory) tasks from National Institute of Health toolbox. The significance level was set at False Discovery Rate (FDR)<0.05. ResultsWe identified significant positive correlations between the QA of WM tracts within the left cerebellum and bilateral fornix with attention, executive functioning, and episodic memory (FDR=0.018, 0.0002, and 0.0002, respectively), and a negative correlation between QA of WM tracts within bilateral cerebellum with attention (FDR=0.028). Working memory demonstrated positive correlations with QA of left inferior longitudinal and left inferior fronto-occipital fasciculi (FDR=0.0009), while it showed a negative correlation with QA of right cerebellar tracts (FDR=0.0005). ConclusionOur results underscore the intricate link between cognitive performance and WM integrity in frontal, temporal, and cerebellar regions, offering insights into early detection and targeted interventions for cognitive disorders.

Statement of Retraction: The Impact of Phonological Awareness in Improving Sequential Memory among Students with Learning Disabilities

Statement of Retraction: The Impact of Phonological Awareness in Improving Sequential Memory among Students with Learning Disabilities

A neural basis for learning sequential memory in brain loop structures.

Behaviors often involve a sequence of events, and learning and reproducing it is essential for sequential memory. Brain loop structures refer to loop-shaped inter-regional connection structures in the brain such as cortico-basal ganglia-thalamic and cortico-cerebellar loops. They are thought to play a crucial role in supporting sequential memory, but it is unclear what properties of the loop structure are important and why. In this study, we investigated conditions necessary for the learning of sequential memory in brain loop structures via computational modeling. We assumed that sequential memory emerges due to delayed information transmission in loop structures and presented a basic neural activity model and validated our theoretical considerations with spiking neural network simulations. Based on this model, we described the factors for the learning of sequential memory: first, the information transmission delay should decrease as the size of the loop structure increases; and second, the likelihood of the learning of sequential memory increases as the size of the loop structure increases and soon saturates. Combining these factors, we showed that moderate-sized brain loop structures are advantageous for the learning of sequential memory due to the physiological restrictions of information transmission delay. Our results will help us better understand the relationship between sequential memory and brain loop structures.

Enhancing healthcare cloud security with intelligent shuffled frog leaping optimized sequential long short-term memory (ISFLO-SLSTM)

Although medical image processing consumes much memory, computing time and specialized platforms are needed. As a result, it is crucial to implement cost-effective solutions to replace outdated ones. Due to these factors, we choose to adopt cloud computing to satisfy our needs for large-scale data processing. Meanwhile, this strategy offers accessibility to services upon request with excellent reliability and versatility. Therefore, employing cloud services rather than internal applications will assist healthcare organizations in outsourcing calculations to a third party, thereby reducing operating costs. However, to avoid malicious data leaks, comprehensive protection of information across unauthorized users and untrustworthy clouds is necessary. Different frameworks are created to allow consumers to save and analyze their information utilizing cloud-based computing. They are developed utilizing distributed systems, cryptosystems, or an amalgam of the two. The primary issue with applying cryptography methods for the enormous analysis of information on the cloud is the computing cost of image processing operations. Preventing unauthorized usage of healthcare records and private health details is the main concern. To secure data processing in a cloud environment by classifying image pixels, we offer a revolutionary intelligent shuffled frog jumping optimized sequential long short-term memory (ISFLO-SLSTM) approach. We added an additional layer, the cloud security module, to lower the danger of future medical data leakage. The effectiveness of the suggested strategy is assessed using medical RGB images taken from the Kaggle database. The findings of this study show how the ISFLO-SLSTM system has the potential to revolutionize healthcare information analysis by protecting patient privacy and security that utilizes the full extent of the latest machine learning methods.

Ordered spatial configuration protects representations of dissimilar items and reduces the similarity effect in visual working memory.

Although similarity could improve visual working memory (VWM) performance, it remains unclear how the spatial configuration of visual information influences the similarity effect in VWM. We explored this question by manipulating the orderliness of spatial configuration (ordered vs. scrambled) in the simultaneous (Experiment 1) and sequential (Experiment 2) change detection tasks. The results showed that similarity improved VWM performance when memory items were presented simultaneously and sequentially. For the simultaneous memory array containing similar and dissimilar items, the performance of the ordered spatial configuration was better than that of the scrambled spatial configuration when probing dissimilar items, while no such difference was found when probing similar items. Further, the similarity effect value in the scrambled spatial configuration was higher than that in the ordered spatial configuration. For the sequential memory array containing similar and dissimilar items, spatial configuration did not affect the similarity effect in VWM. Taken together, these findings suggest that spatial configuration could modulate the similarity effect when memory items are presented simultaneously, in which the ordered spatial configuration protects representations of dissimilar items and reduces the similarity effect in VWM. Our study provides additional evidence for the role of spatial configuration in the similarity effect in VWM, and supports the hierarchical model.

Identification of amnestic mild cognitive impairment among Black and White community-dwelling older adults using NIH Toolbox Cognition tablet battery.

Identify which NIH Toolbox Cognition Battery (NIHTB-CB) subtest(s) best differentiate healthy controls (HC) from those with amnestic mild cognitive impairment (aMCI) and compare the discriminant accuracy between a model using a priori "Norm Adjusted" scores versus "Unadjusted" standard scores with age, sex, race/ethnicity, and education controlled for within the model. Racial differences were also examined. Participants were Black/African American (B/AA) and White consensus-confirmed (HC = 96; aMCI = 62) adults 60-85 years old that completed the NIHTB-CB for tablet. Discriminant function analysis (DFA) was used in the Total Sample and separately for B/AA (n = 80) and White participants (n = 78). Picture Sequence Memory (an episodic memory task) was the highest loading coefficient across all DFA models. When stratified by race, differences were noted in the pattern of the highest loading coefficients within the DFAs. However, the overall discriminant accuracy of the DFA models in identifying HCs and those with aMCI did not differ significantly by race (B/AA, White) or model/score type (Norm Adjusted versus Unadjusted). Racial differences were noted despite the use of normalized scores or demographic covariates-highlighting the importance of including underrepresented groups in research. While the models were fairly accurate at identifying consensus-confirmed HCs, the models proved less accurate at identifying White participants with an aMCI diagnosis. In clinical settings, further work is needed to optimize computerized batteries and the use of NIHTB-CB norm adjusted scores is recommended. In research settings, demographically corrected scores or within model correction is suggested.

The Neural Correlates of Central Auditory Dysfunction in Chronic Tinnitus: A Multimodal Approach.

It was aimed at assessing the connection between tinnitus and central auditory dysfunction using both central auditory tests (CATs) and diffusion tensor imaging (DTI) for brain regions that are crucial for central auditory processing. This prospective case-control study included 15 patients with persistent tinnitus and 20 healthy volunteers as controls. They underwent CATs for memory, attention, and DTI. The Tinnitus Handicap Inventory (THI) Questionnaire was applied as well. From several brain regions, the values of mean diffusivity (MD) and fractional anisotropy (FA) were determined. Comparing both groups, the tinnitus group showed statistically worse values as regards the CATs (memory for content, sequence memory, speech perception in noise (SPIN) at different signal-to-noise ratios, "SNRs") compared with the control group. As regards DTI, the tinnitus group showed decreased FA in several brain areas, including the cingulum, prefrontal-cortex (PFC), insula, and hippocampus. Furthermore, the tinnitus group showed significantly higher MD in the cingulum, BA-46, and amygdala compared with the control group. FA values of BA-46 were positively correlated with the SPIN-SNR-10 scores. Also, FA values of the middle cingulum were positively correlated with SPIN-SNRzero scores. MD values at BA-46 were negatively correlated with SPIN-SNR-10. THI scores were negatively correlated with FA at BA-46; however, they were positively correlated with MD at the amygdala. Central auditory dysfunction may be linked to the underlying neurophysiological changes in chronic tinnitus. Level 2 Laryngoscope, 2024.

Recall tempo of Hebbian sequences depends on the interplay of Hebbian kernel with tutor signal timing

Understanding how neural circuits generate sequential activity is a longstanding challenge. While foundational theoretical models have shown how sequences can be stored as memories in neural networks with Hebbian plasticity rules, these models considered only a narrow range of Hebbian rules. Here, we introduce a model for arbitrary Hebbian plasticity rules, capturing the diversity of spike-timing-dependent synaptic plasticity seen in experiments, and show how the choice of these rules and of neural activity patterns influences sequence memory formation and retrieval. In particular, we derive a general theory that predicts the tempo of sequence replay. This theory lays a foundation for explaining how cortical tutor signals might give rise to motor actions that eventually become "automatic." Our theory also captures the impact of changing the tempo of the tutor signal. Beyond shedding light on biological circuits, this theory has relevance in artificial intelligence by laying a foundation for frameworks whereby slow and computationally expensive deliberation can be stored as memories and eventually replaced by inexpensive recall.

Shared structure facilitates working memory of multiple sequences.

Daily experiences often involve the processing of multiple sequences, yet storing them challenges the limited capacity of working memory (WM). To achieve efficient memory storage, relational structures shared by sequences would be leveraged to reorganize and compress information. Here, participants memorized a sequence of items with different colors and spatial locations and later reproduced the full color and location sequences one after another. Crucially, we manipulated the consistency between location and color sequence trajectories. First, sequences with consistent trajectories demonstrate improved memory performance and a trajectory correlation between reproduced color and location sequences. Second, sequences with consistent trajectories show neural reactivation of common trajectories, and display spontaneous replay of color sequences when recalling locations. Finally, neural reactivation correlates with WM behavior. Our findings suggest that a shared common structure is leveraged for the storage of multiple sequences through compressed encoding and neural replay, together facilitating efficient information organization in WM.

Shared structure facilitates working memory of multiple sequences

Daily experiences often involve the processing of multiple sequences, yet storing them challenges the limited capacity of working memory (WM). To achieve efficient memory storage, relational structures shared by sequences would be leveraged to reorganize and compress information. Here, participants memorized a sequence of items with different colors and spatial locations and later reproduced the full color and location sequences one after another. Crucially, we manipulated the consistency between location and color sequence trajectories. First, sequences with consistent trajectories demonstrate improved memory performance and a trajectory correlation between reproduced color and location sequences. Second, sequences with consistent trajectories show neural reactivation of common trajectories, and display spontaneous replay of color sequences when recalling locations. Finally, neural reactivation correlates with WM behavior. Our findings suggest that a shared common structure is leveraged for the storage of multiple sequences through compressed encoding and neural replay, together facilitating efficient information organization in WM.

The synaptic correlates of serial position effects in sequential working memory.

Sequential working memory (SWM), referring to the temporary storage and manipulation of information in order, plays a fundamental role in brain cognitive functions. The serial position effect refers to the phenomena that recall accuracy of an item is associated to the order of the item being presented. The neural mechanism underpinning the serial position effect remains unclear. The synaptic mechanism of working memory proposes that information is stored as hidden states in the form of facilitated neuronal synapse connections. Here, we build a continuous attractor neural network with synaptic short-term plasticity (STP) to explore the neural mechanism of the serial position effect. Using a delay recall task, our model reproduces the the experimental finding that as the maintenance period extends, the serial position effect transitions from the primacy to the recency effect. Using both numerical simulation and theoretical analysis, we show that the transition moment is determined by the parameters of STP and the interval between presented stimulus items. Our results highlight the pivotal role of STP in processing the order information in SWM.

Do actions structure auditory memory? Action-based event segmentation effects on sensory responses, pupil dilation and sequential memory.

Our actions shape our everyday experience: what we experience, how we perceive, and remember it are deeply affected by how we interact with the world. Performing an action to deliver a stimulus engages neurophysiological processes which are reflected in the modulation of sensory and pupil responses. We hypothesized that these processes shape memory encoding, parsing the experience by grouping self- and externally generated stimuli into differentiated events. Participants encoded sound sequences, in which either the first or last few sounds were self-generated and the rest externally generated. We tested recall of the sequential order of sounds that had originated from the same (within event) or different sources (across events). Memory performance was not higher for within-event sounds, suggesting that actions did not structure the memory representation. However, during encoding, we observed the expected electrophysiological response attenuation for self-generated sounds, together with increased pupil dilation triggered by actions. Moreover, at the boundary between events, physiological responses to the first sound from the new source were influenced by the direction of the source switch. Our results suggest that introducing actions creates a stronger contextual shift than removing them, even though actions do not directly contribute to memory performance. This study contributes to our understanding of how interacting with sensory input shapes experiences by exploring the relationships between action effects on sensory responses, pupil dilation, and memory encoding. Importantly, it challenges the notion of a meaningful contribution from low-level neurophysiological mechanisms associated with action execution in the modulation of the self-generation effect.

Chronic activation of a negative engram induces behavioral and cellular abnormalities.

Negative memories engage a brain and body-wide stress response in humans that can alter cognition and behavior. Prolonged stress responses induce maladaptive cellular, circuit, and systems-level changes that can lead to pathological brain states and corresponding disorders in which mood and memory are affected. However, it is unclear if repeated activation of cells processing negative memories induces similar phenotypes in mice. In this study, we used an activity-dependent tagging method to access neuronal ensembles and assess their molecular characteristics. Sequencing memory engrams in mice revealed that positive (male-to-female exposure) and negative (foot shock) cells upregulated genes linked to anti- and pro-inflammatory responses, respectively. To investigate the impact of persistent activation of negative engrams, we chemogenetically activated them in the ventral hippocampus over 3 months and conducted anxiety and memory-related tests. Negative engram activation increased anxiety behaviors in both 6- and 14-month-old mice, reduced spatial working memory in older mice, impaired fear extinction in younger mice, and heightened fear generalization in both age groups. Immunohistochemistry revealed changes in microglial and astrocytic structure and number in the hippocampus. In summary, repeated activation of negative memories induces lasting cellular and behavioral abnormalities in mice, offering insights into the negative effects of chronic negative thinking-like behaviors on human health.

The Effect of Developmental Bottom-up Approach Through Perceptual-motor Interventions on Performance of Children With Mild Intellectual Disability

Background and Aims Intellectual disability manifests through defects in cognitive, motor, and perceptual skills as elements affecting learning and acquiring individual skills. Efforts to improve these skills are essential in these children. This study investigated the durable effects of perceptual-motor process-oriented interventions with the Ramon and Johnston protocol on the motor, cognitive, and perceptual performance of children with mild intellectual disability. Methods In this quasi-experimental research, 21 children with mild intellectual disability aged 7 to 12 years were selected using the available sampling method and according to the inclusion criteria. Then, they were assigned to an experimental group of 10 students (10 weeks of perceptual-motor intervention according to the Johnstone and Ramon protocol, two sessions per week) and a control group of 11 students (no intervention) using a simple random method. The participants were evaluated before and 2 months after the intervention with movement assessment battery for children 2nd Edition, Conners’ continuous performance test-II, the working memory test of Wechsler intelligence scale for children fourth edition, and the test of visual perception skills revised. Univariate covariance test was used for data analysis. Results The findings showed a significant difference between the intervention and control groups in general motor performance, aiming and catching, the correct answer in the attention test, visual sequential memory, and visual figure-ground in the delayed post-test stage (P&lt;0.05). Also, no significant difference was seen in other subtests (P&gt;0.05). Conclusion Bottom-up approach interventions through Ramon and Johnston’s comprehensive perceptual-motor training can significantly help improve general and ball motor skills, sustained attention, visual sequential memory, and visual figure-ground in children with mild intellectual disability.

Differences in the consolidation by spontaneous and evoked ripples in the presence of active dendrites.

Ripples are a typical form of neural activity in hippocampal neural networks associated with the replay of episodic memories during sleep as well as sleep-related plasticity and memory consolidation. The emergence of ripples has been observed both dependent as well as independent of input from other brain areas and often coincides with dendritic spikes. Yet, it is unclear how input-evoked and spontaneous ripples as well as dendritic excitability affect plasticity and consolidation. Here, we use mathematical modeling to compare these cases. We find that consolidation as well as the emergence of spontaneous ripples depends on a reliable propagation of activity in feed-forward structures which constitute memory representations. This propagation is facilitated by excitable dendrites, which entail that a few strong synapses are sufficient to trigger neuronal firing. In this situation, stimulation-evoked ripples lead to the potentiation of weak synapses within the feed-forward structure and, thus, to a consolidation of a more general sequence memory. However, spontaneous ripples that occur without stimulation, only consolidate a sparse backbone of the existing strong feed-forward structure. Based on this, we test a recently hypothesized scenario in which the excitability of dendrites is transiently enhanced after learning, and show that such a transient increase can strengthen, restructure and consolidate even weak hippocampal memories, which would be forgotten otherwise. Hence, a transient increase in dendritic excitability would indeed provide a mechanism for stabilizing memories.