Biology subjects

Shimono, M.

Publications and source records attributed to Shimono, M..

7 recordsLinked to original sources

bioRxiv2025

In vitro 2 In vivo : Bidirectional and High-Precision Generation of In Vitro and In Vivo Neuronal Spike Data

Spontaneous neural activity is often dismissed as noise, yet predicting its future evolution remains difficult, especially across recording preparations. We study transfer between in vitro and in vivo multineuronal spike trains with a Transformer that treats spikes as sparse point processes and does not require cell-to-cell correspondence across datasets. Because binned spike trains are extremely sparse and imbalanced, likelihood-based objectives can be dominated by the majority no-spike class under domain shift. Introducing a Dice objective that maximizes spike-event overlap stabilizes training and substantially improves performance, including in cross-domain prediction and generation. Training on one domain and evaluating on another yields a quantitative predictability score and a predictability matrix. We show that (i) a performance-based embedding organizes datasets by transferability; (ii) only specific in vitro and in vivo pairs support reliable cross-domain generation, whereas mismatched pairs degrade despite strong within-domain accuracy; (iii) attention and gradient-based importance analyses reveal how the model shifts emphasis across neurons and time; and (iv) the predictability matrix guides source-target selection for generating diverse activity patterns. This framework supports systematic tests of transferability and may help prioritize datasets for translational studies.

neuroscience↗

bioRxiv2023

Deep neural generation of neuronal spikes.

In the brain, many regions work in a network-like association, yet it is not known how durable these associations are in terms of activity and could survive without structural connections. To assess the association or similarity between brain regions with a new "generating" approach, this study evaluated the similarity of activities of neurons at the cellular level within each region after disconnecting between regions. To this end, a multi-layer LSTM (Long-Short Term Memory) model was used. Surprisingly, the results revealed that generation of activity from one region to other regions that had been disconnected was possible with similar reproduction accuracy as generation between the same regions in many cases. Notably, not only firing rates but also synchronization of firing between neuron pairs, which is often used as neuronal representations, could be reproduced with considerable precision. Additionally, their accuracies were associated with the relative distance between brain regions and the strength of the structural connections that initially connected them. This outcome not only enables us to look into principles in neuroscience based on the potential to generate new informative data, but also creates neural activity that has not been measured in adequate amounts and could potentially lead to reduced animal experiments.

neuroscience↗

bioRxiv2022

Whole brain evaluation of cortical micro-connectomes.

The brain is an organ that functions as a network of many elements connected in a non-uniform manner. Especially, the cortex is evolutionarily newest, and is thought to be primarily responsible for the high intelligence of mammals. In the mature mammalian brain, all cortical regions are expected to have some degree of homology, but have some variations of local circuits to achieve specific functions enrolled by individual regions. However, few cellular-level studies have examined how the networks within different cortical regions differ. This study aimed to find rules for systematic changes of connectivity (microconnectomes) across 16 different cortical region groups. We also observed unknown trends in basic parameters in vitro such as firing rate and layer thickness across brain regions. The results revealed that the frontal group shows unique characteristics such as dense active neurons, thick cortex and strong connections with deeper layers. This suggests the frontal side of the cortex is inherently capable of driving, even in isolation. This may suggest that deep layers of frontal node provide the driving force generating a global pattern of spontaneous synchronous activity, such as the Default Mode Network. This finding may explain why disruption in this region causes a large impact on mental health.

neuroscience↗

bioRxiv2020

Inhibitory neurons are a Central Controlling regulator in the effective cortical microconnectome.

The brain is a network system in which excitatory and inhibitory neurons keep the activity balanced in the highly non-uniform connectivity pattern of the microconnectome. It is well known that the relative percentage of inhibitory neurons is much smaller than excitatory neurons. So, in general, how the inhibitory neurons can keep the balance with the surrounding excitatory neurons is an important question. We observed effective networks, reflecting causal interactions, of ~1000 neurons in cortical acute slices. Surprisingly, we found that inhibitory neurons are not only located at more central positions than excitatory neurons but also have stronger controlling ability of other neurons than excitatory neurons. Besides, we found that the precedence in centrality and controlling ability of inhibitory neurons are well observed in deep cortical layers by comparing with distribution of neurons coloured by NeuN immunostaining data. Preceding the observation, we also found that inhibitory neurons show higher firing rate than excitatory neurons, and that their firing rate also closely obey a log-normal distribution as previously known about excitatory neurons. Additionally, their connectivity strengths also obeyed a log-normal distribution. In summary, within the network interaction of huge numbers of neurons, inhibitory neurons seem to produce a central controlling system that sustains the homeostatic behavior of the brain. A similar evaluation in different life stages and in disease states etc. will not only provide deeper understandings in the homeostasis of the brain, but also will provide a selective and effective way to stimulate individual neurons to modulate neuropsychiatry or neurodegeneration disease states.

neuroscience↗

bioRxiv2019

Light activates the translational regulatory GCN2 kinase via reactive oxygen species emanating from the chloroplast

Cytosolic mRNA translation is subject to global and mRNA-specific controls. Phosphorylation of translation initiation factor eIF2 anchors a reversible switch that represses translation globally. The stress-responsive GCN2 kinase is the only known kinase for eIF2 in Arabidopsis. Here we show that conditions that generate reactive oxygen species (ROS) in the chloroplast, such as dark-light transitions, high light, and the herbicide methyl viologen all rapidly activated the GCN2 kinase, whereas mitochondrial and ER stress did not. In addition, GCN2 activation was light dependent and mitigated by photosynthesis inhibitors and ROS quenchers. Accordingly, seedling growth of multiple gcn2 mutant alleles was retarded under conditions of excess light, implicating the GCN2-eIF2 pathway in responses to light and associated ROS. Once activated, the GCN2 kinase preferentially suppressed the ribosome loading of mRNAs for functions such as mitochondrial ATP synthesis, the chloroplast thylakoids, vesicle trafficking, and translation. The transcriptome of gcn2 mutants was sensitized to abiotic stress, including oxidative stress, as well as innate immune responses. Accordingly, gcn2 displayed defects in immune priming by the fungal elicitor, chitin. In conclusion, we provide evidence that reactive oxygen species produced by the photosynthetic apparatus help to activate the highly conserved GCN2 kinase, leading to eIF2 phosphorylation and thus affecting the status of the cytosolic protein synthesis apparatus.

plant biology↗

bioRxiv2017

Tracking Individual Differences in Perception by TMS-EEG Intrinsic Effective Connectivity

Non-invasive human electroencephalography (EEG) coupled with transcranial magnetic stimulation (TMS) is currently used to measure coarse stimulus-response relationships in brain physiology during behavior. However, with key modifications, the TMS-EEG technique holds even greater promise for monitoring fine-scale neural signatures of human behavior. Here, we demonstrate that a novel TMS-EEG co-registration technique can dynamically monitor individual human variation in perception based solely on EEG resting-state intrinsic effective connectivity probed by TMS-based phase resetting of ongoing activity. We used a bistable stimulus task, where the percept is perceived as either horizontal or vertical apparent motion, to record gamma band interhemispheric integration of information. Fine-grained inter-individual behavioral differences in horizontal motion bias could be measured by tracking resting-state gamma-band effective connectivity from right hMT+ to left hMT+. Thus, our method of triggering intrinsic resting-state effective connectivity in oscillatory dynamics can monitor individual differences in perception via the long-range integration of information. This technique will be useful for the manipulative dissection of individual-scale human cognition mediated by neural dynamics and may also expand neurofeedback approaches.

neuroscience↗

bioRxiv2017

Communicability Systematically Explains Transmission Speed In A Cortical Macro-Connectome

Global dynamics in the brain can be captured using fMRI, MEG, or electrocorticography (ECoG), but models are often restricted by anatomical constraints. Complementary single-/multi-unit recordings have described local fast temporal dynamics. However, because of anatomical constraints, global fast temporal dynamics remain incompletely understood. Therefore, we compared temporal aspects of cross-area propagations of single-unit recordings and ECoG, and investigated their anatomical bases. First, we demonstrated how both evoked and spontaneous ECoGs can accurately predict latencies of single-unit recordings. Next, we estimated the propagation velocity (1.0-1.5 m/s) from brain-wide data and found that it was fairly stable among different conscious levels. We also found that the anatomical topology strongly predicted the latencies. Finally, Communicability, a novel graph-theoretic measure, could systematically capture the balance between shorter or longer pathways. These results demonstrate that macro-connectomic perspective is essential for evaluating detailed temporal dynamics in the brain.\n\nAuthor SummaryThis study produced four main findings: First, we demonstrated that ECoG signals could predict the timing of evoked electrical spikes of neurons elicited by visual stimuli. Second, we showed that spontaneous ECoG recorded under a blindfold condition (without any stimuli) could also predict the timing of visually evoked neuronal spikes. We also clarified that performance predictions from blindfold data are essentially supported by the constraints of structural paths. Third, we quantified the propagation velocity (conductance velocity) as 1.0-1.5 m/s, and found that the velocity was stable among different conscious levels. Fourth, Communicability successfully characterized the relative contributions of shorter and longer paths. This study represents an important contribution to the theoretical understanding of the brain in terms of connectomics, dynamical propagations, and multi-scale architectures.

neuroscience↗