Biology subjects

Haga, T.

Publications and source records attributed to Haga, T..

9 recordsLinked to original sources

bioRxiv2026

Efficient memory sampling by hippocampal attractor dynamics with intrinsic oscillation

Hippocampus is known to replay activity patterns to recall and process memories, which is often related to Hopfield-type attractor dynamics. Another line of theoretical studies suggests that hippocampal replay prioritizes replay of experiences to accelerate value learning for efficient decision making. It is unknown how hippocampal attractor dynamics perform prioritized memory sampling, and more broadly, how we can consistently relate dynamical (bottom-up) and functional (top-down) theories of hippocampal replay. In this paper, we propose an extended Hopfield-type attractor network model with momentum, kinetic energy, and conservation of the total energy, which is called momentum Hopfield model. We show that our model can be interpreted as CA3-CA1 network model with intrinsic oscillation, and such network model reproduces hippocampal replay in 1-D and 2-D spatial structures. We also prove that our model functionally works as Markov-chain Monte Carlo sampling in which recall frequencies of memory patterns can be arbitrarily biased. Using this property, we implemented prioritized experience replay using our model, which actually accelerated reinforcement learning for spatial navigation. Our model explains how dynamics of hippocampal circuits realize efficient memory sampling, providing a theoretical link between dynamics and functions of hippocampal replay.

neuroscience↗

bioRxiv2025

Chronotype-specific impacts of sleep-wake schedule on behavioral, physiological and psychological parameters

Diurnal variations in human performance are known to be associated with chronotype, which reflects individual differences in preferred sleep-wake cycles. Early chronotypes (ECs) exhibit superior motor and cognitive functions in the morning, whereas late chronotypes (LCs) tend to perform better in the evening. However, previous studies have examined these patterns under socially imposed nighttime sleep schedules, without considering individuals preferred sleep timing. Thus, it remains unclear whether these chronotype-dependent performance peaks persist when individuals follow their preferred sleep schedules. To address this question, we measured subjective states, sleep pressure (using electroencephalography), and motor and cognitive functions at 1.5 and 12.5 hours after awakening under two conditions: the Clock-Time condition (10:30 PM-6:30 AM sleep aligned with social rhythms) and the Self-Time condition (8 hours of sleep aligned with individual preferences). The results revealed distinct chronotype-specific patterns. ECs exhibited increases in fatigue and sleepiness dependent on awake duration, showing typical sleep pressure accumulation. LCs were more affected by sleep timing and showed atypical patterns of sleep pressure accumulation compared with ECs. Motor ability tended to improve in the afternoon in both chronotypes, irrespective of sleep-wake schedule, whereas cognitive performance peaked at different stages of wakefulness - earlier for ECs and later for LCs - independent of the time of day. These findings suggest that chronotype-aligned sleep-wake schedules could help optimize fatigue management, cognitive performance, and motor control. Diurnal variations in performance cannot be attributed to clock time alone but emerge from the combined effects of chronotype, awake duration, and sleep-wake regulation. Statement of SignificanceThis study provides new insights into the ways in which chronotype and sleep-wake schedules jointly shape behavioral, physiological, and psychological dynamics across the day. Whereas previous research has largely emphasized circadian rhythms, our findings underscore the critical role of actual sleep timing in modulating daily function ing. This integrative perspective extends current understanding of the ways in which individual differences in biological timing and social constraints interact to influence human health and performance. Importantly, the results highlight the need for future studies to disentangle circadian and sleep-wake schedule effects, which could inform personalized strategies for optimizing well-being, productivity, and clinical interventions in disorders related to sleep and circadian misalignment.

neuroscience↗

bioRxiv2024

Dual roles of idling moments in past and future memories

Every day, we experience new daily episodes and store new memories. Although memories are stored in corresponding engram cells, how different sets of engram cells are selected for current and next episodes, and how they create their memories, remains unclear. We report that in mice, hippocampal CA1 neurons show an organized synchronous activity in prelearning home cage sleep that correlates with the learning ensembles only in engram cells, termed preconfigured ensembles. Moreover, after learning, a subset of nonengram cells develops population activity, which is constructed during postlearning offline periods through synaptic depression and scaling, and then emerges to represent engram cells for new learning. Together, our findings indicate that during offline periods there are two parallel processes occurring: conserving of past memories through reactivation, and preparation for upcoming ones through offline synaptic plasticity mechanisms.

neuroscience↗

bioRxiv2023

Unified neural representation model for physical space and linguistic concepts

Hippocampus and entorhinal cortex encode spaces by spatially local and hexagonal grid activity patterns (place cells and grid cells), respectively. In addition, the same brain regions also implicate neural representations for non-spatial, semantic concepts (concept cells). These observations suggest that neurocomputational mechanisms for spatial knowledge and semantic concepts are related in the brain. However, the exact relationship remains to be understood. Here we show a mathematical correspondence between a value function for goal-directed spatial navigation and an information measure for word embedding models in natural language processing. Based on this relationship, we integrate spatial and semantic computations into a neural representation model called as "disentangled successor information" (DSI). DSI generates biologically plausible neural representations: spatial representations like place cells and grid cells, and concept-specific word representations which resemble concept cells. Furthermore, with DSI representations, we can perform inferences of spatial contexts and words by a common computational framework based on simple arithmetic operations. This computation can be biologically interpreted by partial modulations of cell assemblies of non-grid cells and concept cells. Our model offers a theoretical connection of spatial and semantic computations and suggests possible computational roles of hippocampal and entorhinal neural representations.

neuroscience↗

bioRxiv2020

Associative memory networks for graph-based abstraction

Our cognition relies on the ability of the brain to segment hierarchically structured events on multiple scales. Recent evidence suggests that the brain performs this event segmentation based on the structure of state-transition graphs behind sequential experiences. However, the underlying circuit mechanisms are only poorly understood. In this paper, we propose an extended attractor network model for the graph-based hierarchical computation, called as Laplacian associative memory. This model generates multiscale representations for communities (clusters) of associative links between memory items, and the scale is regulated by heterogenous modulation of inhibitory circuits. We analytically and numerically show that these representations correspond to graph Laplacian eigenvectors, a popular method for graph segmentation and dimensionality reduction. Finally, we demonstrate that our model with asymmetricity exhibits chunking resembling to hippocampal theta sequences. Our model connects graph theory and the attractor dynamics to provide a biologically plausible mechanism for abstraction in the brain.

neuroscience↗

bioRxiv2020

Establishing defined daily doses (DDDs) for antimicrobial agents used for pigs, cattle and poultry in Japan and comparing them with European DDD values

Monitoring of antimicrobial use is essential to manage the development and selection of antimicrobial resistance. A variety of indicators has become available to monitor antimicrobial use in human and animal medicine. One of them is an indicator based on defined daily dose (DDD). By using the number of DDDs used and normalizing it by the population at risk of being treated in a defined period, one can estimate the number of treatment days with antimicrobial agents in a population. For veterinary medicine, the European Medicines Agency (EMA) has published the European values of DDD (DDDvet) for food-producing animals. In this study, we defined Japanese defined daily doses for antimicrobial agents (DDDjp) using DDD values that we previously assigned for antimicrobial products approved for use in pigs, cattle and poultry in Japan and compared them with DDDvet values. For the comparison, the quotient of Japanese and European values (QDDD) was calculated and the effect of the administration route and the number of active substances contained in the preparation was investigated. A total of 59, 51 and 27 DDDjp values were defined for 43, 32 and 25 antimicrobial agents using the data of 269, 195 and 131 products approved for use in pigs, cattle and poultry respectively. A comparison was possible for 44, 27 and 17 pairs of DDDjp and DDDvet values for antimicrobial agents used for pigs, cattle and poultry respectively. The comparison showed median QDDD value of 0.66 and 0.63 for antimicrobial agents used for pigs and cattle respectively (P<0.01), indicating that the Japanese daily doses are significantly lower than the corresponding EMA values in these species. For the antimicrobial agents used for poultry, no siginificant difference was observed between DDDjp and DDDvet values with median QDDD value of 1.17. The difference between DDDvet and DDDjp values and absence of DDDvet values for some antimicrobial agents marketed in Japan indicate that DDDjp rather than DDDvet should be used as the basis for the calculation of antimicrobial use monitoring in farm animals in Japan.

bioinformatics↗

bioRxiv2017

Recurrent network model for learning goal-directed sequences through reverse replay

Reverse replay of hippocampal place cells occurs frequently at rewarded locations, suggesting its contribution to goal-directed pathway learning. Symmetric spike-timing dependent plasticity (STDP) in CA3 likely potentiates recurrent synapses for both forward (start to goal) and reverse (goal to start) replays during sequential activation of place cells. However, how reverse replay selectively strengthens forward pathway is unclear. Here, we show computationally that firing sequences bias synaptic transmissions to the opposite direction of propagation under symmetric, but not asymmetric, STDP in the co-presence of short-term synaptic plasticity. We demonstrate that a significant bias can be created in biologically realistic simulation settings, and that this bias enables reverse replay to enhance goal-directed spatial memory on a T-maze. Our model for the first time provides the mechanistic account for the way reverse replay contributes to hippocampal sequence learning for reward-seeking spatial navigation.

neuroscience↗

bioRxiv2017

Unsupervised detection of cell-assembly sequences with edit similarity score

Cell assembly is a hypothetical functional unit of information processing in the brain. While technologies for recording large-scale neural activity have been advanced, mathematical methods to analyze sequential activity patterns of cell-assembly are severely limited. Here, we propose a method to extract cell-assembly sequences repeated at multiple time scales and various precisions from irregular neural population activity. The key technology is to combine \"edit similarity\" in computer science with machine-learning clustering algorithms, where the former defines a \"distance\" between two strings as the minimal number of operations required to transform one string to the other. Our method requires no external references for pattern detection, and is tolerant of spike timing jitters and length irregularity in assembly sequences. These virtues enabled simultaneous automatic detections of hippocampal place-cell sequences during locomotion and their time-compressed replays during resting states. Furthermore, our method revealed previously undetected cell-assembly structure in the rat prefrontal cortex during goal-directed behavior. Thus, our method expands the horizon of cell-assembly analysis.

neuroscience↗

bioRxiv2017

Dendritic processing of spontaneous neuronal sequences for one-shot learning

Spontaneous firing sequences colloquially called \"preplay\" are fundamental features of hippocampal network physiology. Preplay sequences have been hypothesized to participate in hippocampal learning and memory, but such functional roles and their potential cellular mechanisms remain unexplored. Here, we report a computational model based on the functional propagation of preplay sequences in the CA3 neuronal network. The model instantiates two synaptic pathways in CA3 neurons, one for proximal dendrite-somatic interactions to generate intrinsic preplay sequences and the other for distal dendritic processing of extrinsic sensory signals. The core dendritic computation is the maximization of matching between patterned activities in the two compartments through nonlinear spike generation. The model performs robust one-shot learning with long-term stability and independence that are modulated by the plasticity of dendrite-targeted inhibition. This model demonstrates that learning models combined with dendritic computations can enable preplay sequences to act as templates for rapid and stable memory formation.

neuroscience↗