Biology subjects

Funamizu, A.

Publications and source records attributed to Funamizu, A..

6 recordsLinked to original sources

bioRxiv2026

Dual reinforcement-learning network modules for modeling decision-making with multiple strategies

Animals and humans use multiple behavioral strategies to perform tasks. However, neural implementations of multiple strategies remain elusive, as some studies propose distinct pathways, while others observe overlapping brain regions associated with strategies. We propose a hybrid deep reinforcement learning (H-DRL) method, in which one network model implements model-free and inference-based behaviors through synaptic plasticity and recurrent activity. H-DRL uses a single updating rule and switches the strategy according to task demands without an explicit arbitrator. H-DRL reproduced mixed strategies of humans in a two-step task. In the mouse perceptual decision-making task, H-DRL adapted the recurrent dynamics with rich learning when the task condition required inference-based behavior, while adopting model-free behavior with lazy learning for a simple condition. The activity of H-DRL units showed condition-dependent maintenance of previous events, consistent with orbitofrontal cortical activity in mice. Our model provides a unified view that one cortical network automatically determines strategies in use depending on task conditions.

neuroscience↗

bioRxiv2025

Open-source modular FPGA system for two-photon mesoscope enabling multi-layer, multi-depth neural activity recording and lifetime imaging

Large field-of-view (FOV) two-photon microscopy makes it possible to record a large number of neural activities from multiple brain regions simultaneously. However, the larger the field of view, the longer it takes to scan the entire FOV. To increase imaging speed, we have developed open-source software to digitize analogue signals from a photomultiplier tube using a field-programmable gate array (FPGA) at a rate of 3.2 GS/s. By combining this with a newly developed a circular delay-path module for a custom two-photon mesoscope (Diesel2p), we succeeded in simultaneous recording of >10,000 neurons from the entire bilateral dorsal cortex at up to four depths. We also demonstrated large FOV lifetime imaging using the same system. Our modular, open-source FPGA system can be readily integrated into any type of two-photon microscope and will accelerate the biomedical application of multi-scale two-photon imaging in a wide range of pathophysiological investigations.

neuroscience↗

bioRxiv2024

Real-cyber hybrid neural network for predicting neural circuits in mouse decision-making

A major challenge in neuroscience is to elucidate how the brain processes sensory input to generate behavior, especially given the difficulty to measure neural activity across the whole brain. To address this limitation, previous studies have used artificial neural networks (ANNs) and modeled decision-making circuits in the brain. Here, we developed a real-cyber hybrid neural network (HNN) that directly input the activity of real neurons to an ANN, imposing biological constraints on the model. Using spike inputs from cortical or subcortical regions, the HNN predicted body movements of head-fixed mice during a task better than the ANN did. This improvement arose not only from the added mouse neuronal activity, but from the activity generated within the HNN. The generated activity potentially included unrecorded neuronal activity from mice, which was difficult for the ANN. We propose that HNNs develop brain-like activity to predict animal movements and compensate for unrecorded neural activity.

neuroscience↗

bioRxiv2024

Global neural encoding of model-free and inference-based strategies in mice

When a simple model-free strategy does not provide sufficient outcomes, an inference-based strategy estimating a hidden task structure becomes essential for optimizing choices. However, the neural circuitry involved in inference-based strategies is still unclear. We developed a tone frequency discrimination task in head-fixed mice in which the tone category of the current trial depended on the category of the previous trial. When the tone category was repeated every trial, the mice continued to use the default model-free strategy, as well as when tone was randomly presented, to bias the choices. In contrast, the default strategy gradually shifted to an inference-based strategy when the tone category was alternated in each trial. Brain-wide electrophysiological recording during the overtrained phase suggested that the neural activity of the frontal and sensory cortices, hippocampus, and striatum was correlated with the reward expectation of both the model-free and inference-based strategies. These results suggest the global encoding of multiple strategies in the brain.

neuroscience↗

bioRxiv2023

Localized and global computation for integrating prior value and sensory evidence in the mouse cerebral cortex

Adaptive behavior requires integrating prior knowledge of action outcomes and sensory evidence for making decisions while maintaining prior knowledge for future actions. As outcome- and sensory-based decisions are often tested separately, it is unclear how these processes are integrated in the brain. In a tone frequency discrimination task with two sound durations and asymmetric reward blocks, we found that neurons in the medial prefrontal cortex of male mice represented the additive combination of prior reward expectations and choices. The sensory inputs and choices were selectively decoded from the auditory cortex irrespective of reward priors and the secondary motor cortex, respectively, suggesting localized computations of task variables are required within single trials. In contrast, all the recorded regions represented prior values that needed to be maintained across trials. We propose localized and global computations of task variables in different time scales in the cerebral cortex.

neuroscience↗

bioRxiv2023

Stable sound decoding despite modulated sound representation in the auditory cortex

The activity of neurons in the auditory cortex is driven by both sounds and non-sensory context. To investigate the neuronal correlates of non-sensory context, we trained head-fixed mice to perform a two-alternative choice auditory task in which either reward or stimulus expectation (prior) was manipulated in blocks. Using two-photon calcium imaging to record populations of single neurons in auditory cortex, we found that both stimulus and reward expectation modulated the activity of these neurons. A linear decoder trained on this population activity could decode stimuli as well or better than predicted by the animals performance. Interestingly, the optimal decoder was stable even in the face of variable sensory representations. Neither the context nor the mouses choice could be reliably decoded from the recorded neural activity. Our findings suggest that in spite of modulation of auditory cortical activity by task priors, auditory cortex does not represent sufficient information about these priors to exploit them optimally and that decisions in this task require that rapidly changing sensory information be combined with more slowly varying task information extracted and represented in brain regions other than auditory cortex.

neuroscience↗