Tomoki Fukai

This study uses simulation-based inference to quantify compensatory mechanisms in a spiking neuronal network model under hyperexcitability conditions.

This paper shows that pre-stimulus brain states predict and reduce variability in stimulation responses by timing interventions to optimal neural states.

The paper constructs a neural network model of the prefrontal-thalamo-hippocampal circuit to study how modular division of labor supports spatial memory tasks.

The paper develops a microcircuit model of hippocampal area CA3 to explain how replay and sharp-wave ripples are initiated and terminated based on reward-associated locations.

The paper identifies criticality signatures in cortical networks during synchronization onset, showing both intra- and inter-hemispheric coordination patterns.

This paper develops a multiscale computational framework to study how brain criticality at local and global scales interacts through structural connectivity.

This study investigates how memory-specific excitatory-inhibitory balance enables diverse replay and reduces catastrophic forgetting in a recurrent spiking network.

The paper shows that a recurrent spiking neuron network using predictive learning rules generates spontaneous activity resembling probabilistic sensory experiences, replicating behavioral biases in perceptual decision making.

The paper shows that in mice, preconfigured engram cell ensembles during sleep correlate with learning, and postlearning synaptic changes prepare for new memories.

This study tests if recurrent inhibitory interneuron connectivity can generate synchrony in a biophysical dentate gyrus model and finds it insufficient.

The paper investigates how astrocytes and short-term plasticity influence cell assembly dynamics in a hippocampal model, showing they balance flexibility and robustness in memory formation.

The paper analyzes neuronal ensemble activity in rats during a decision-making task, identifying distinct spatiotemporal dynamics related to choice and outcome.

This paper introduces a neuron model with biologically-plausible learning rules that enable rapid pattern detection through somato-dendritic coupling and transient boosting.

This study links synaptic competition to pattern separation, showing that competition-based learning outperforms backpropagation in small sample sizes.

This paper introduces a neural representation model that unifies spatial and semantic computations by linking spatial navigation value functions to word embedding information measures.

This paper introduces a biologically plausible generative model of the hippocampal formation that uses theta-band oscillations to train a network for spatial and non-spatial memory tasks.

This study presents a cortical model of local-global inhibition balance to investigate how inhibitory neuron diversity affects memory retrieval in associative memory structures.

The paper introduces an improved CNMF-based algorithm for calcium imaging data analysis, enhancing cell detection accuracy and signal quality.

This study shows that intrinsic bursts, not isolated spikes, enhance learning of Levy flight trajectories in recurrent neural networks by synchronizing burst onsets across populations.

This study identifies astrocyte GluN2C NMDARs as regulators of basal synaptic strength distribution in hippocampal CA1 pyramidal neurons, specifically in stratum radiatum inputs.

This paper introduces a model called Laplacian associative memory to simulate graph-based hierarchical computation in the brain, showing it generates multiscale representations similar to graph Laplacian eigenvectors and exhibits hippocampal-like chunking.

The paper identifies synergistic information transfer between the medial frontal cortex and dorsal striatum in rats during decision-making tasks.

This paper introduces a recurrent gated neuronal network model with dendrites to solve context-dependent segmentation tasks using synthetic and calcium imaging data.

This study shows that neural trajectories in the medial frontal cortex and dorsal striatum of rats process choice and outcome information in parallel, with increased spike synchrony between these regions during task performance.

The paper develops a neural network model of the entorhinal-hippocampal circuit to study how spatial working memory is encoded, maintained, and recalled through cholinergic modulation and theta oscillations.

The paper introduces artificial dendritic neurons that use conditional entropy minimization to self-supervise temporal feature extraction from continuous information streams.

This study shows adult zebrafish use future state prediction errors to guide active avoidance behavior in a virtual reality setup.

The paper shows that individual differences in decision making under uncertainty are linked to variability in neural dynamics in the medial frontal cortex of rats.

This paper introduces a class of unsupervised neural networks that identify repeated patterns in sequence data through mutual prediction between reservoir computing modules.

The paper presents a computational model of dendritic processing in CA3 neurons that uses preplay sequences for one-shot learning, showing how dendritic computations enable stable memory formation.