Biology subjects

Sugase-Miyamoto, Y.

Publications and source records attributed to Sugase-Miyamoto, Y..

3 recordsLinked to original sources

bioRxiv2024

Age-related alterations in functional and structural networks in the brain in macaque monkeys

Resting-state networks (RSNs) have been used as biomarkers of brain diseases and cognitive performance. However, age-related changes in the RSNs of macaques, a representative animal model, are still not fully understood. In this study, we measured the RSNs in macaques aged 3-20 years and investigated the age-related changes from both functional and structural perspectives. The proportion of structural connectivity in the RSNs significantly decreased, whereas functional connectivity showed an increasing trend with age. Additionally, the amplitude of low-frequency fluctuations tended to increase with age, indicating that resting-state neural activity may be more active in the RSNs may increase with age. These results indicate that structural and functional alterations in typical RSNs are age-dependent and can be a marker of aging in the brain.

neuroscience↗

bioRxiv2023

Neural correlates of category learning in monkey inferior temporal cortex

We trained two monkeys implanted with multi-electrode arrays to categorize natural images of cats and dogs, in order to observe changes in neural activity related to category learning. We recorded neural activity from area TE, which is required for normal learning of visual categories based on perceptual similarity. Neural activity during a passive viewing task was compared pre- and post-training. After the category training, the accuracy of abstract category decoding improved. Specifically, the proportion of single units with category selectivity increased, and units sustained their category-specific responses for longer. Visual category learning thus appears to enhance category separability in area TE by driving changes in the stimulus selectivity of individual neurons and by recruiting more units to the active network.

neuroscience↗

bioRxiv2020

CoNNECT: Convolutional Neural Network for Estimating synaptic Connectivity from spike Trains

The recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms. Although the algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another method using a Convolutional Neural Network for Estimating synaptic Connectivity from spike trains (CoNNECT). After adaptation to huge amounts of simulated data, this method robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new method, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys.

neuroscience↗