An integrated deep learning‐based model of spatial cells that combines self‐motion with sensory information
| العنوان: | An integrated deep learning‐based model of spatial cells that combines self‐motion with sensory information |
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| المؤلفون: | Azra Aziz, Peesapati S. S. Sreeharsha, Rohan Natesh, Vaddadhi S. Chakravarthy |
| المصدر: | Hippocampus. 32:716-730 |
| بيانات النشر: | Wiley, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Deep Learning, Place Cells, Space Perception, Cognitive Neuroscience, Models, Neurological, Grid Cells |
| الوصف: | A special class of neurons in the hippocampal formation broadly known as the spatial cells, whose subcategories include place cells, grid cells, and head direction cells, are considered to be the building blocks of the brain's map of the spatial world. We present a general, deep learning-based modeling framework that describes the emergence of the spatial-cell responses and can also explain responses that involve a combination of path integration and vision. The first layer of the model consists of head direction (HD) cells that code for the preferred direction of the agent. The second layer is the path integration (PI) layer with oscillatory neurons: displacement of the agent in a given direction modulates the frequency of these oscillators. Principal component analysis (PCA) of the PI-cell responses showed the emergence of cells with grid-like spatial periodicity. We show that the Bessel functions could describe the response of these cells. The output of the PI layer is used to train a stack of autoencoders. Neurons of both the layers exhibit responses resembling grid cells and place cells. The paper concludes by suggesting the wider applicability of the proposed modeling framework beyond the two simulated studies. |
| تدمد: | 1098-1063 1050-9631 |
| DOI: | 10.1002/hipo.23461 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0f19d7781f136831a3fd13c71211083c https://doi.org/10.1002/hipo.23461 |
| Rights: | CLOSED |
| رقم الانضمام: | edsair.doi.dedup.....0f19d7781f136831a3fd13c71211083c |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 10981063 10509631 |
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| DOI: | 10.1002/hipo.23461 |