Biology subjects

Toyama, K.

Publications and source records attributed to Toyama, K..

5 recordsLinked to original sources

bioRxiv2026

IF1 restrains excessive elevation of the mitochondrial membrane potential and safeguards the epithelial state of human induced pluripotent stem cells

Human pluripotent stem cells (hPSCs) rely on glycolysis and exhibit low mitochondrial respiration, conditions under which F1Fo ATP synthase tends to run in reverse, hydrolyzing ATP. ATP synthase inhibitory factor subunit 1 (IF1) inhibits F1Fo-mediated ATP hydrolysis, but its significance in glycolytic hPSCs remains unclear. Here, we show that stable IF1 knockdown (IF1-KD) in human induced pluripotent stem cells (hiPSCs) enhanced mitochondrial ATP hydrolysis and elevated the mitochondrial membrane potential ({Delta}{Psi}m). Markers of the undifferentiated state were largely maintained, whereas IF1-KD cells exhibited reduced epithelial characteristics and a partial epithelial-mesenchymal transition (EMT)-like state. IF1-KD cells also showed enhanced store-operated Ca2 entry (SOCE) and increased nuclear NFATc3. Pharmacological reduction of {Delta}{Psi}m attenuated SOCE, whereas NFATc3 overexpression reproduced key features of the EMT-like expression pattern. Endogenous IF1 therefore acts as a constitutive restraint on reverse-mode F1Fo activity, limiting excessive {Delta}{Psi}m and downstream SOCE-NFATc3 signaling and safeguarding the epithelial state of hPSCs.

Cell Biology↗

bioRxiv2023

Negative reward-prediction errors of climbing fiber inputs for cerebellar reinforcement learning algorithm

Although the cerebellum is typically linked to supervised learning algorithms, it also exhibits extensive involvement in reward processing. In this study, we investigated the cerebellums role in executing reinforcement learning algorithms, with a particular emphasis on essential reward-prediction errors. We employed the Q-learning model to accurately reproduce the licking responses of mice in a Go/No-go auditory-discrimination task. This method enabled the calculation of reinforcement learning variables, such as reward, predicted reward, and reward-prediction errors in each learning trial. By tensor component analysis of two-photon Ca2+ imaging data from more than 6,000 Purkinje cells, we found that climbing fiber inputs of the two distinct components, which were specifically activated during Go and No-go cues in the learning process, showed an inverse relationship with predictive reward-prediction errors. By assuming bidirectional parallel-fiber Purkinje-cell synaptic plasticity, we constructed a cerebellar neural-network model with 5,000 spiking neurons of granule cells, Purkinje cells, cerebellar nuclei neurons, and inferior olive neurons. The network model qualitatively reproduced distinct changes in licking behaviors, climbing-fiber firing rates and their synchronization during discrimination learning separately for Go/No-go conditions. We found that Purkinje cells in the two components could develop specific motor commands for their respective auditory cues, guided by the predictive reward-prediction errors from their climbing fiber inputs. These results indicate a possible role of context-specific actors in modular reinforcement learning, integrating with cerebellar supervised learning capabilities.

neuroscience↗

bioRxiv2022

Dynamic organization of cerebellar climbing fiber response and synchrony in multiple functional modules reduces dimensions for reinforcement learning

Daynamic functional organization by synchronization is theorized to be essential for dimension reduction of the cerebellar learning space. We analyzed a large amount of coordinate-localized, two-photon imaging data from cerebellar Crus II in mice undergoing "Go/No-go" reinforcement learning. Tensor component analysis revealed that a majority of climbing fiber inputs to Purkinje cells were reduced to only four functional components, corresponding to accurate timing control of motor initiation related to a Go cue, cognitive error-based learning, reward processing, and inhibition of erroneous behaviors after a No-go cue. Spatial distribution of these components coincided well with the boundaries of Aldolase-C/zebrin II expression in Purkinje cells, whereas several components are mixed in single neurons. Synchronization within individual components was bidirectionally regulated according to specific task contexts and learning stages. These findings suggest that the cerebellum, based on anatomical compartments, reduces dimensions by self-organization of components, a feature that may inspire new-generation AI designs.

neuroscience↗

bioRxiv2019

Precise hyperacuity estimation of spike timing from calcium imaging

Two-photon imaging is a major recording technique in neuroscience, but it suffers from several limitations, including a low sampling rate, the nonlinearity of calcium responses, the slow dynamics of calcium dyes and a low signal-to-noise ratio, all of which impose a severe limitation on the application of two-photon imaging in elucidating neuronal dynamics with high temporal resolution. Here, we developed a hyperacuity algorithm (HA_time) based on an approach combining a generative model and machine learning to improve spike detection and the precision of spike time inference. First, Bayesian inference estimates the calcium spike model by assuming the constancy of the spike shape and size. A support vector machine employs this information and detects spikes with higher temporal precision than the sampling rate. Compared with conventional thresholding, HA_time improved the precision of spike time estimation up to 20-fold for simulated calcium data. Furthermore, the benchmark analysis of experimental data from different brain regions and simulation of a broader range of experimental conditions showed that our algorithm was among the best in a class of hyperacuity algorithms. We encourage experimenters to use the proposed algorithm to precisely estimate hyperacuity spike times from two-photon imaging.

neuroscience↗

bioRxiv2019

Electrical coupling controls dimensionality and chaotic firing of inferior olive neurons

One of the main challenges facing online neural learning systems with numerous modifiable parameters (or "degrees-of-freedom") such as the cerebellum, is how to avoid "overfitting". We previously proposed that the cerebellum controls the degree-of-freedoms during learning by gradually modulating the electric coupling strength between inferior olive neurons. Here, we develop a modeling technique to estimate effective coupling strengths between inferior olive neurons from in vivo recordings of Purkinje cell complex spike activity in three different coupling conditions. We show that high coupling strengths induce synchronous firing and decrease the dimensionality of inferior olive firing dynamics. In contrast, intermediate coupling strengths induce chaotic firing and increase the dimensionality of firing dynamics. Our results thus support the hypothesis that effective coupling controls the dimensionality of inferior olive firing, which may allow the olivocerebellar system to learn effectively from a small training sample set despite the low firing frequency of inferior olive neurons.

neuroscience↗