Biology subjects

Fukai, T.

Publications and source records attributed to Fukai, T..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

A generative model of the hippocampal formation trained with theta driven local learning rules

Advances in generative models have recently revolutionised machine learning. Meanwhile, in neuroscience, generative models have long been thought fundamental to animal intelligence. Understanding the biological mechanisms that support these processes promises to shed light on the relationship between biological and artificial intelligence. In animals, the hippocampal formation is thought to learn and use a generative model to support its role in spatial and non-spatial memory. Here we introduce a biologically plausible model of the hippocampal formation tantamount to a Helmholtz machine that we apply to a temporal stream of inputs. A novel component of our model is that fast theta-band oscillations (5-10 Hz) gate the direction of information flow throughout the network, training it akin to a high-frequency wake-sleep algorithm. Our model accurately infers the latent state of high-dimensional sensory environments and generates realistic sensory predictions. Furthermore, it can learn to path integrate by developing a ring attractor connectivity structure matching previous theoretical proposals and flexibly transfer this structure between environments. Whereas many models trade-off biological plausibility with generality, our model captures a variety of hippocampal cognitive functions under one biologically plausible local learning rule.

neuroscience↗

bioRxiv2023

Myeloid Drp1 Deficiency Limits Revascularization in Ischemic Muscles via Inflammatory Macrophage Polarization and Metabolic Reprograming

In the preclinical model of peripheral arterial disease (PAD), M2-like anti-inflammatory macrophage polarization and angiogenesis are required for revascularization. The regulation of cell metabolism and inflammation in macrophages is tightly linked to mitochondrial dynamics. Drp1, a mitochondrial fission protein, has shown context-dependent macrophage phenotypes with both pro- and anti-inflammatory characteristics. However, the role of macrophage Drp1 in reparative neovascularization remains unexplored. Here we show that Drp1 expression was significantly increased in F4/80+ macrophages within ischemic muscle at day 3 after hindlimb ischemia (HLI), an animal model of PAD. Myeloid-specific Drp1-/- mice exhibited reduced limb perfusion recovery, angiogenesis and muscle regeneration after HLI. These effects were associated with an increase in pro-inflammatory M1-like macrophages, p-NFkB and TNF, and reduced anti-inflammatory M2-like macrophages and p-AMPK in ischemic muscle of myeloid Drp1-/- mice. In vitro, Drp1-/- macrophages under hypoxia serum starvation (HSS), an in vitro PAD model, demonstrated enhanced glycolysis via reducing p-AMPK as well as mitochondrial dysfunction and excessive mitochondrial ROS, resulting in increased M1-gene and reduced M2-gene expression. Conditioned media from HSS-treated Drp1-/- macrophages exhibited increased secretion of pro-inflammatory cytokines and suppressed angiogenic responses in cultured endothelial cells. Thus, Drp1 deficiency in macrophages under ischemia drives inflammatory metabolic reprogramming and macrophage polarization, thereby limiting revascularization in experimental PAD.

cell biology↗

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↗

bioRxiv2023

Learning rules for cortical-like spontaneous replay of an internal model

The brain is thought to construct an optimal internal model representing the probabilistic structure of the environment accurately. Evidence suggests that spontaneous brain activity gives such a model by cycling through activity patterns evoked by previous sensory experiences with the experienced probabilities. The brains spontaneous activity emerges from internally-driven neural population dynamics. However, how cortical neural networks encode internal models into spontaneous activity is poorly understood. Recent computational and experimental studies suggest that a cortical neuron can implement complex computations, including predictive responses, through soma-dendrite interactions. Here, we show that a recurrent network of spiking neurons subject to the same predictive learning principle provides a novel mechanism to learn the spontaneous replay of probabilistic sensory experiences. In this network, the learning rules minimize probability mismatches between stimulus-evoked and internally driven activities in all excitatory and inhibitory neurons. This learning paradigm generates stimulus-specific cell assemblies that internally remember their activation probabilities using within-assembly recurrent connections. Our model contrasts previous models that encode the statistical structure of sensory experiences into Markovian transition patterns among cell assemblies. We demonstrate that the spontaneous activity of our model well replicates the behavioral biases of monkeys performing perceptual decision making. Our results suggest that interactions between intracellular processes and recurrent network dynamics are more crucial for learning cognitive behaviors than previously thought.

neuroscience↗

bioRxiv2022

Synaptic Competition Enhances the Discrimination of Interfering Memories

Long-term plasticity is the primary mode of synaptic weight updating. However, other updating modes include modification of synaptic connections across neurons. The synaptic competition found during adult neurogenesis is an example. In this process, adult-born neurons are integrated into the existing neuronal pool by competing with mature neurons for synaptic connections from the entorhinal cortex. Evidence suggests that adult neurogenesis is critical for discriminating similar memories, i.e., memories with considerable interferences. This computational study aims to link synaptic competition with pattern separation. We show that synaptic competition and neuronal maturation are crucial for separating considerably overlapping memory patterns. Furthermore, we demonstrate that a feed-forward neural network trained by a competition-based learning rule can outperform a multi-layer perceptron trained by the backpropagation algorithm, especially when the sample size is small. These results unveil the functional implications and potential applications of synaptic competition in neural computation.

neuroscience↗

bioRxiv2022

HOTARU: Automatic sorting system for large scale calcium imaging data

Currently, calcium imaging allows long-term recording of large-scale neuronal activity in diverse states. However, it remains difficult to extract neuronal dynamics from recorded imaging data. In this study, we propose an improved constrained nonnegative matrix factorization (CNMF)-based algorithm and an effective method to extract cell shapes with fewer false positives and false negatives through image processing. We also show that the evaluation metrics obtained during image and signal processing can be combined and used for false-positive cell determination. For the CNMF algorithm, we combined cell-by-cell regularization and baseline shrinkage estimation, which greatly improved its stability and robustness. We applied these methods to real data and confirmed their effectiveness. Our method is simpler and faster, detects more cells with lower firing rates and signal-to-noise ratios, and enhances the quality of the extracted cell signals. These advances can improve the standard of downstream analysis and contribute to progress in neuroscience.

neuroscience↗

bioRxiv2021

Multiscale and extended retrieval of associative memory structures in a cortical model of local-global inhibition balance

Inhibitory neurons take on many forms and functions. How this diversity contributes to memory function is not completely known. Previous formal studies indicate inhibition differentiated by local and global connectivity in associative memory networks functions to rescale the level of retrieval of excitatory assemblies. However, such studies lack biological details such as a distinction between types of neurons (excitatory and inhibitory), unrealistic connection schemas, and non-sparse assemblies. In this study, we present a rate-based cortical model where neurons are distinguished (as excitatory, local inhibitory, or global inhibitory), connected more realistically, and where memory items correspond to sparse excitatory assemblies. We use this model to study how local-global inhibition balance can alter memory retrieval in associative memory structures, including naturalistic and artificial structures. Experimental studies have reported inhibitory neurons and their sub-types uniquely respond to specific stimuli and can form sophisticated, joint excitatory-inhibitory assemblies. Our model suggests such joint assemblies, as well as a distribution and rebalancing of overall inhibition between two inhibitory sub-populations - one connected to excitatory assemblies locally and the other connected globally - can quadruple the range of retrieval across related memories. We identify a possible functional role for local-global inhibitory balance to, in the context of choice or preference of relationships, permit and maintain a broader range of memory items when local inhibition is dominant and conversely consolidate and strengthen a smaller range of memory items when global inhibition is dominant. This model therefore highlights a biologically-plausible and behaviourally-useful function of inhibitory diversity in memory.

neuroscience↗

bioRxiv2021

Intrinsic bursts facilitate learning of Levy flight movements in recurrent neural network models

Isolated spikes and bursts of spikes are thought to provide the two major modes of information coding by neurons. Bursts are known to be crucial for fundamental processes between neuron pairs, such as neuronal communications and synaptic plasticity. Deficits in neuronal bursting can also impair higher cognitive functions and cause mental disorders. Despite these findings on the roles of bursts, whether and how bursts have an advantage over isolated spikes in the network-level computation remains elusive. Here, we demonstrate in a computational model that not isolated spikes but intrinsic bursts can greatly facilitate learning of Levy flight random walk trajectories by synchronizing burst onsets across neural population. Levy flight is a hallmark of optimal search strategies and appears in cognitive behaviors such as saccadic eye movements and memory retrieval. Our results suggest that bursting is a crucial component of sequence learning by recurrent neural networks in the brain.

neuroscience↗

bioRxiv2021

Synergistic information in the frontal cortex-striatal pathway

Across the cortico-basal ganglia circuit, the medial frontal cortex (MFC) communicates with the dorsal striatum (DS) during learning and planning. How these two brain regions communicate with each other is, however, not fully understood. Here we report the presence of synergistic information during information transfer across the frontal cortex-striatal pathway. Synergistic information emerges from the positive interaction of DS and MFC neurons and provides the DS with additional cortical information. This information is held latent in neuronal signals. To reveal it, we simultaneously record neuronal activities from the MFC and DS of rats trained on an outcome-based decision-making task and determined whether past neuronal activities of the DS positively influence communication rates. We detect a neuronal synergy that enables the MFC to boost its communication rate to the DS. Our results suggest that past neuronal activities of the DS help decode MFC signals. This ability is not attributed to the inherent autocorrelation of DS spiking activities.

neuroscience↗

bioRxiv2021

Astrocyte GluN2C NMDA receptors control basal synaptic strengths of hippocampal CA1 pyramidal neurons in the stratum radiatum

Experience-dependent plasticity is a key feature of brain synapses for which neuronal N-Methyl-D-Aspartate receptors (NMDARs) play a major role, from developmental circuit refinement to learning and memory. Astrocytes also express NMDARs although their exact function has remained controversial. Here we identify a circuit function for GluN2C NMDAR, a subtype highly expressed in astrocytes, in layer-specific tuning of synaptic strengths in mouse hippocampal CA1 pyramidal neurons. Interfering with astrocyte NMDAR or GluN2C NMDAR activity reduces the range of presynaptic strength distribution specifically in the stratum radiatum inputs without an appreciable change in the mean presynaptic strength. Mathematical modeling shows that narrowing of the width of presynaptic release probability distribution compromises the expression of long-term synaptic plasticity. Our findings suggest a novel feedback signaling system that uses astrocyte GluN2C NMDARs to adjust basal synaptic weight distribution of Schaffer collateral inputs, which in turn impacts computations performed by the CA1 pyramidal neuron.

neuroscience↗

bioRxiv2021

Neural mechanisms of context-dependent segmentation tested on large-scale recording data

The brain performs various cognitive functions by learning the spatiotemporal salient features of the environment. This learning likely requires unsupervised segmentation of hierarchically organized spike sequences, but the underlying neural mechanism is only poorly understood. Here, we show that a recurrent gated network of neurons with dendrites can context-dependently solve difficult segmentation tasks. Dendrites in this model learn to predict somatic responses in a self-supervising manner while recurrent connections learn a context-dependent gating of dendro-somatic current flows to minimize a prediction error. These connections select particular information suitable for the given context from input features redundantly learned by the dendrites. The model selectively learned salient segments in complex synthetic sequences. Furthermore, the model was also effective for detecting multiple cell assemblies repeating in large-scale calcium imaging data of more than 6,500 cortical neurons. Our results suggest that recurrent gating and dendrites are crucial for cortical learning of context-dependent segmentation tasks.

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

Concomitant processing of choice and outcome in frontal corticostriatal ensembles correlates with performance of rats

The frontal cortex-basal ganglia network plays a pivotal role in adaptive goal-directed behaviors. Medial frontal cortex (MFC) encodes information about choices and outcomes into sequential activation of neural population, or neural trajectory. While MFC projects to the dorsal striatum (DS), whether DS also displays temporally coordinated activity remains unknown. We studied this question by simultaneously recording neural ensembles in the MFC and DS of rodents performing an outcome-based alternative choice task. We found that the two regions exhibited highly parallel evolution of neural trajectories, transforming choice information into outcome-related information. When the two trajectories were highly correlated, spike synchrony was task-dependently modulated in some MFC-DS neuron pairs. Our results suggest that neural trajectories concomitantly process decision-relevant information in MFC and DS with increased spike synchrony between these regions.

neuroscience↗

bioRxiv2019

Oscillation-driven memory encoding, maintenance and recall in an entorhinal-hippocampal circuit model

During the execution of working memory tasks, task-relevant information is processed by local circuits across multiple brain regions. How this multi-area computation is conducted by the brain remains largely unknown. To explore such mechanisms in spatial working memory, we constructed a neural network model involving parvalbumin-positive, somatostatin-positive and vasoactive intestinal polypeptide-positive interneurons in the hippocampal CA1 and the superficial and deep layers of medial entorhinal cortex (MEC). Our model is based on a hypothesis that cholinergic modulations differently regulate information flows across CA1 and MEC at memory encoding, maintenance and recall during delayed nonmatching-to-place tasks. In the model, theta oscillation coordinates the proper timing of interactions between these regions. Furthermore, the model predicts that MEC is engaged in decoding as well as encoding spatial memory, which we confirmed by experimental data analysis. Thus, our model accounts for the neurobiological characteristics of the cross-area information routing underlying working memory tasks.

neuroscience↗

bioRxiv2019

Future state prediction errors guide active avoidance behavior by adult zebrafish.

Human predicts future. To ask if fish also has this capacity, we established the virtual reality training system with live imaging of the telencephalic neurons of adult zebrafish in the active avoidance and found that, at the onset of the trial, learned fish conceives two future conditions as the favorable status on its way to the safe goal, i.e. one with the backwardly moving landscape and the other with the color of the safe goal. And the two different neural ensembles monitor the discrepancy between these predictions and the perceived real external status. Once fish reaches the goal, another ensemble is set to work to monitor whether fish keeps staying in the safe goal. The manipulation to artificially enhance these prediction errors elevated the activities of these ensembles and induced fish to behave to correct errors, revealing that fish sets behavioral strategy to actively realize these predictions.

neuroscience↗

bioRxiv2019

Artificial Dendritic Neurons Enable Self-Supervised Temporal Feature Extraction

The brain identifies potentially salient features within continuous information streams to appropriately process external and internal temporal events. This requires the compression or abstraction of information streams, for which no effective information principles are known. Here, we propose conditional entropy minimization learning as the fundamental principle of such temporal processing. We show that this learning rule resembles Hebbian learning with backpropagating action potentials in dendritic neuron models. Moreover, networks of the dendritic neurons can perform a surprisingly wide variety of complex unsupervised learning tasks. Our model not only accounts for the mechanisms of chunking of temporal inputs in the human brain but also accomplishes blind source separation of correlated mixed signals, which cannot be solved by conventional machine learning methods, such as independent-component analysis.\n\nOne Sentence SummaryNeurons use soma-dendrite interactions to self-supervise the learning of characteristic features of various temporal inputs.

neuroscience↗

bioRxiv2018

Dynamic embedding of salience coding in hippocampal spatial maps

Hippocampal CA1 neurons participate in dynamic ensemble codes for space and memory. Prominent features of the environment are represented by an increased density of place cells, but cellular principles governing the formation and plasticity of such disproportionate maps are unknown. We thus imaged experience-dependent long-term changes in spatial representations at the cellular level in the CA1 deep sublayer in mice learning to navigate in a virtual-reality environment. The maps were highly dynamic but gradually stabilized as over-representations for motivational (reward) and environmental (landmark) salience emerged in different time courses by selective consolidation of relevant spatial representations. Relocation of the reward extensively reorganized pre-formed maps by a mechanism involving rapid recruitment of cells from the previous location followed by their re-stabilization, indicating that a subset of neurons encode reward-related information. The distinct properties of these CA1 cells may provide a substrate by which salient experience forms lasting and adaptable memory traces.

neuroscience↗

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↗