Biology subjects

Shirakami, A.

Publications and source records attributed to Shirakami, A..

3 recordsLinked to original sources

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↗

bioRxiv2021

Neural network embedding of functional microconnectome.

Our brain works as a complex network system. Experiential knowledge seems to be coded into the organisms network architecture rather than retaining only properties of individual neurons. In order to be better able to consider the high complexity of this network architecture, extracting simple rules through both automated as well as interpretable analysis of topological patterns will be necessary in order to allow more useful observations of interrelationships within the complex neural architecture. By combining these two types of analysis methods, we could automatically compress and naturally interpret topological patterns of functional connectivities, which produced electrical activities from many neurons simultaneously from acute slices of mice brain for 2.5 hours [Kajiwara et al. 2021]. As the first type of analysis, this study trained an artificial neural network system called Neural Network Embedding (NNE), and automatically compressed the functional connectivities into only small (25%) dimensions. As the second type of analysis, we widely compared the compressed features with ~15 representative network metrics, having clear interpretations, including > 5 centrality-type metrics and newly developed network metrics, that quantify degrees or ratio of hubs distanced by several-nodes from initially focused hubs. As the result, although we could give interpretations for only 55-60% of the extracted features, these new metrics, together with the commonly utilized network metrics, enabled interpretations for 80-100% features, using automated analysis. The result demonstrates not only the fact that the NNE method surpasses limitations of commonly used human-made variables, but also the possibility that acknowledgement of our own limitations drives us to extend interpretable possibilities by developing new analytic methodologies.

neuroscience↗