Biology subjects

Ohmae, S.

Publications and source records attributed to Ohmae, S..

6 recordsLinked to original sources

bioRxiv2026

AI-like parallelogram geometry of word relations in the human neocortex

The same semantic relation (e.g., queen-king and woman-man) is encoded as a shared vector direction in language AI, forming parallelogram geometry, whereas its presence in the human brain remains unknown. Our fMRI analysis revealed similar geometry in the neocortex, particularly in higher-order language regions. Our findings link the brain and AI through a common geometry, advancing understanding of how abstract relational knowledge is organized and used in human language processing.

neuroscience↗

bioRxiv2024

A deep-learning strategy to identify cell types across species from high-density extracellular recordings

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but dont reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifiers predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously-recorded cell types during behavior.

neuroscience↗

bioRxiv2022

Emergence of syntax and word prediction in an artificial neural circuit of the cerebellum

The cerebellum, interconnected with the cerebral neocortex, plays a vital role in human-characteristic cognition such as language processing, but knowledge about the underlying circuit computation of the cerebellum remains very limited. Here, to understand the computation underlying the cerebellar language processing, we developed a biologically-constrained cerebellar artificial neural network (cANN) model, which implemented the recently identified cerebello-cerebellar recurrent pathway. We found that while cANN acquires prediction of future words, another function of syntactic recognition emerges in the middle layer of the prediction circuit. The recurrent pathway of the cANN was essential for the two language functions, whereas cANN variants with further biological constraints preserved these functions. Considering the uniform structure of cerebellar circuitry across all functional domains, the single-circuit computation, which is the common basis of the two language functions, can be generalized to fundamental cerebellar functions of prediction and grammar-like rule extraction from sequences, that underpin a wide range of cerebellar motor and cognitive functions. This is a pioneering study to understand the circuit computation of human-characteristic cognition by biologically-constrained ANNs, which can lead new therapeutic interventions for human cognitive dysfunction.

neuroscience↗

bioRxiv2021

A recurrent circuit links antagonistic cerebellar modules during associative motor learning

The neural architecture of the cerebellum is thought to be specialized for performing supervised learning: specific error-related climbing fiber inputs are used to teach sensorimotor associations to small ensembles of Purkinje cells located in functionally distinct modules that operate independently of each other in a purely feedforward manner. Here, we test whether the basic operation of the cerebellum complies with this basic architecture in mice that learned a simple sensorimotor association during eyeblink conditioning. By recording Purkinje cells in different modules and testing whether their responses rely on recurrent circuits, our results reveal three operational principles about the functional organization of the cerebellum that stand in stark contrast to the conventional view: (1) Antagonistic organization, (2) Recurrent network dynamics, and (3) Intermodular communication. We propose that the neural architecture of the cerebellum implements these three operational principles to achieve optimal performance and solve a number of problems in motor control.

neuroscience↗

bioRxiv2021

P-sort: an open-source software for cerebellar neurophysiology

Analysis of electrophysiological data from Purkinje cells (P-cells) of the cerebellum presents challenges for spike detection. Complex spikes have waveforms that vary significantly from one event to the next, raising the problem of misidentification. Even when complex spikes are detected correctly, the simple spikes may belong to a different P-cell, raising the danger of misattribution. Here, we analyzed data from over 300 P-cells in marmosets, macaques, and mice, using an open-source, semi-automated software called P-sort that addresses the spike identification and attribution problems. Like other sorting software, P-sort relies on nonlinear dimensionality reduction to cluster spikes. However, it also uses the statistical relationship between simple and complex spikes to merge seemingly disparate clusters, or split a single cluster. In comparison with expert manual curation, occasionally P-sort identified significantly more complex spikes, as well as prevented misattribution of clusters. Three existing automatic sorters performed less well, particularly for identification of complex spikes. To improve development of analysis tools for the cerebellum, we provide labeled data for 313 recording sessions, as well as statistical characteristics of waveforms and firing patterns.

neuroscience↗

bioRxiv2020

Deleting Mecp2 from the entire cerebellum rather than its neuronal subtypes causes a delay in motor learning in mice

Rett syndrome is a devastating childhood neurological disorder caused by mutations in MECP2. Of the many symptoms, motor deterioration is a significant problem for patients. In mice, deleting Mecp2 from the cortex or basal ganglia causes motor dysfunction, hypoactivity, and tremor, which are abnormalities observed in patients. However, little is known about the consequences of deleting Mecp2 from the cerebellum, a brain region critical for motor function. Here we show that deleting Mecp2 from the entire cerebellum, but not from individual cerebellar cell types, causes a delay in motor learning that is overcome by additional training. We also observed irregular firing rates of Purkinje cells and transcriptional misregulation within the cerebellum of knockout mice. These findings demonstrate that the motor deficits present in Rett syndrome arise, in part, from cerebellar dysfunction. For Rett syndrome as well as other neurodevelopmental disorders, our results highlight the importance of understanding which brain regions contribute to disease phenotypes.

neuroscience↗