Naoki Hiratani

Sex differences in exploration-exploitation strategies during home-cage decision making

In 136 mice on FED3 tasks, males showed slightly higher win-stay in all three choice tasks, with higher accuracy in a deterministic bandit but not a probabilistic one.

Age-related changes in behavioural and neural variability in a decision-making task

This study examines age-related changes in neural and behavioural variability during a decision-making task in mice, finding increased neural variability and reduced variability quenching in specific brain regions.

Local gated-Hebbian learning of deep cerebellar networks with quadratic classification capacity

The paper shows that a two-hidden-layer network with local Hebbian learning can achieve quadratic classification capacity, supporting fast and efficient learning in cerebellum-like circuits.

Ventral striatal astrocytes contribute to reinforcement learning

This study shows that ventral striatal astrocytes influence reinforcement learning by modulating decision noisiness and reward-guided choice through calcium dynamics.

A Brain-Wide Map of Neural Activity during Complex Behaviour

The paper presents a brain-wide neural activity map from 621,733 neurons across 139 mice performing a decision-making task, showing how neural activity encodes task variables across brain regions.

Cortical neural landscape captures mouse-to-mouse variability in anticipatory vs. inattentive decision making

The paper uses deep learning to analyze neural data from mice performing a decision-making task, revealing that cortical dynamics correlate with anticipatory vs. inattentive behavior.

Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools

Decision-Making Time Cells in Hippocampal Dorsal CA1

The paper identifies decision-making time cells in dorsal CA1 pyramidal neurons during associative learning in mice, showing sequential activity encodes temporal memory for decision-making.

Data architecture for a large-scale neuroscience collaboration

The paper describes a modular data architecture for a neuroscience collaboration, enabling data contribution, access, and analysis across multiple labs.

Evolution of neural activity in circuits bridging sensory and abstract knowledge

This study uses a circuit model to investigate how neural activity evolves during abstract knowledge acquisition and how it relates to experimental observations.

Developmental and evolutionary constraints on olfactory circuit selection

The paper predicts neural circuit structure across species by optimizing a three-layer neural network to achieve efficient learning, recovering mammalian 3/2 and invertebrate 7/2 scaling laws.

Rapid Bayesian learning in the mammalian olfactory system

This study formulates olfactory learning as a Bayesian optimization process and shows a computational model can identify odors from few observations and learn reward associations with a plausible neural architecture.

Chunking sequence information by mutually predicting recurrent neural networks

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

Redundancy in synaptic connections enables neurons to learn optimally

The paper shows that redundant synaptic connections allow neurons to learn optimally through synaptic plasticity and rewiring, approximating particle filtering.

Detailed dendritic excitatory/inhibitory balance through heterosynaptic spike-timing-dependent plasticity

The paper shows that heterosynaptic spike-timing-dependent plasticity achieves detailed excitatory/inhibitory balance on dendritic branches and explains inhibitory neuron maturation effects on excitatory neuron selectivity.

Hebbian Wiring Plasticity Generates Efficient Network Structures for Robust Inference with Synaptic Weight Plasticity

The paper shows that combining Hebbian synaptic weight plasticity with wiring plasticity leads to efficient and robust network structures for inference tasks.