Whole brain evaluation of cortical micro-connectomes

Matsuda, Kouki; Nakajima, Ryouta; Shirakami, Arata; Akutsu, Tatsuya; Shimono, Masanori

doi:10.1101/2022.10.05.510240

Cited by 2 publications

(3 citation statements)

References 91 publications

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“…We performed spike sorting (Spyking Circus software) from the time series obtained in the main measurement and converted to time series binary data of the activity of the cell population. Refer these following references about the details of the experimental procedure [Ide et al, 2019, Kajiwara et al, 2021; Shirakami et al, 2021, Matsuda et al, 2022].…”

Section: Methodsmentioning

confidence: 99%

“…The test data is also sometimes referred to as target data because this data is the target of the generation process. The training and test data were respectively obtained from one of the 16 regions of the neocortex (fig1-b, Table 1) [Matsuda et al, 2022].…”

Section: -4 Topics Conducted In This Researchmentioning

confidence: 99%

“…We used neuronal spike data recorded and studied in detail in our past study. Here, we briefly explain the experimental procedure utilized in the past study [Kajiwara et al, 2021;Shirakami, 2021, Matsuda et al, 2022. The whole experimental processes are also now open in a video journal [Ide et al, 2019].…”

Section: -1 Data Acquisition Of Neuronal Activitiesmentioning

confidence: 99%

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Deep neural generation of neuronal spikes

Nakajima

Shirakami

Tsumura

et al. 2023

Preprint

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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.

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Section: Methodsmentioning

confidence: 99%

Section: -4 Topics Conducted In This Researchmentioning

confidence: 99%

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Deep neural generation of neuronal spikes

Nakajima

Shirakami

Tsumura

et al. 2023

Preprint

Self Cite

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Mutual generation in neuronal activity across the brain via deep neural approach, and its network interpretation

Nakajima,

Shirakami,

Tsumura

et al. 2023

Commun Biol

Self Cite

View full text Add to dashboard Cite

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 generating approach, this study evaluated the similarity of activities of neurons within each region after disconnecting between regions. The “generation” approach here refers to using a multi-layer LSTM (Long Short-Term Memory) model to learn the rules of activity generation in one region and then apply that knowledge to generate activity in other regions. Surprisingly, the results revealed that activity generation from one region to disconnected regions was possible with similar accuracy to generation between the same regions in many cases. Notably, firing rates and synchronization of firing between neuron pairs, often used as neuronal representations, could be reproduced with precision. Additionally, accuracies were associated with the relative angle between brain regions and the strength of the structural connections that initially connected them. This outcome enables us to look into trends governing non-uniformity of the cortex based on the potential to generate informative data and reduces the need for animal experiments.

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Whole brain evaluation of cortical micro-connectomes

Cited by 2 publications

References 91 publications

Deep neural generation of neuronal spikes

Deep neural generation of neuronal spikes

Mutual generation in neuronal activity across the brain via deep neural approach, and its network interpretation

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