Convolutional method for data assimilation An improved method on neuronal electrophysiological data
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909647511748608 |
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| author | Li, Dawei Abarbanel, Henry D. I. |
| author_facet | Li, Dawei Abarbanel, Henry D. I. |
| contents | We present a convolution-based data assimilation method tailored to neuronal electrophysiology, addressing the limitations of traditional value-based synchronization approaches. While conventional methods rely on nudging terms and pointwise deviation metrics, they often fail to account for spike timing precision, a key feature in neural signals. Our approach applies a Gaussian convolution to both measured data and model estimates, enabling a cost function that evaluates both amplitude and timing alignment via spike overlap. This formulation remains compatible with gradient-based optimization. Through twin experiments and real hippocampal neuron recordings, we demonstrate improved parameter estimation and prediction quality, particularly in capturing sharp, time-sensitive dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11365 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Convolutional method for data assimilation An improved method on neuronal electrophysiological data Li, Dawei Abarbanel, Henry D. I. Neurons and Cognition We present a convolution-based data assimilation method tailored to neuronal electrophysiology, addressing the limitations of traditional value-based synchronization approaches. While conventional methods rely on nudging terms and pointwise deviation metrics, they often fail to account for spike timing precision, a key feature in neural signals. Our approach applies a Gaussian convolution to both measured data and model estimates, enabling a cost function that evaluates both amplitude and timing alignment via spike overlap. This formulation remains compatible with gradient-based optimization. Through twin experiments and real hippocampal neuron recordings, we demonstrate improved parameter estimation and prediction quality, particularly in capturing sharp, time-sensitive dynamics. |
| title | Convolutional method for data assimilation An improved method on neuronal electrophysiological data |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2506.11365 |