Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.02529 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909604746625024 |
|---|---|
| author | Wagenmaker, Andrew Mi, Lu Rozsa, Marton Bull, Matthew S. Svoboda, Karel Daie, Kayvon Golub, Matthew D. Jamieson, Kevin |
| author_facet | Wagenmaker, Andrew Mi, Lu Rozsa, Marton Bull, Matthew S. Svoboda, Karel Daie, Kayvon Golub, Matthew D. Jamieson, Kevin |
| contents | Recent advances in techniques for monitoring and perturbing neural populations have greatly enhanced our ability to study circuits in the brain. In particular, two-photon holographic optogenetics now enables precise photostimulation of experimenter-specified groups of individual neurons, while simultaneous two-photon calcium imaging enables the measurement of ongoing and induced activity across the neural population. Despite the enormous space of potential photostimulation patterns and the time-consuming nature of photostimulation experiments, very little algorithmic work has been done to determine the most effective photostimulation patterns for identifying the neural population dynamics. Here, we develop methods to efficiently select which neurons to stimulate such that the resulting neural responses will best inform a dynamical model of the neural population activity. Using neural population responses to photostimulation in mouse motor cortex, we demonstrate the efficacy of a low-rank linear dynamical systems model, and develop an active learning procedure which takes advantage of low-rank structure to determine informative photostimulation patterns. We demonstrate our approach on both real and synthetic data, obtaining in some cases as much as a two-fold reduction in the amount of data required to reach a given predictive power. Our active stimulation design method is based on a novel active learning procedure for low-rank regression, which may be of independent interest. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02529 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Active learning of neural population dynamics using two-photon holographic optogenetics Wagenmaker, Andrew Mi, Lu Rozsa, Marton Bull, Matthew S. Svoboda, Karel Daie, Kayvon Golub, Matthew D. Jamieson, Kevin Neurons and Cognition Machine Learning Recent advances in techniques for monitoring and perturbing neural populations have greatly enhanced our ability to study circuits in the brain. In particular, two-photon holographic optogenetics now enables precise photostimulation of experimenter-specified groups of individual neurons, while simultaneous two-photon calcium imaging enables the measurement of ongoing and induced activity across the neural population. Despite the enormous space of potential photostimulation patterns and the time-consuming nature of photostimulation experiments, very little algorithmic work has been done to determine the most effective photostimulation patterns for identifying the neural population dynamics. Here, we develop methods to efficiently select which neurons to stimulate such that the resulting neural responses will best inform a dynamical model of the neural population activity. Using neural population responses to photostimulation in mouse motor cortex, we demonstrate the efficacy of a low-rank linear dynamical systems model, and develop an active learning procedure which takes advantage of low-rank structure to determine informative photostimulation patterns. We demonstrate our approach on both real and synthetic data, obtaining in some cases as much as a two-fold reduction in the amount of data required to reach a given predictive power. Our active stimulation design method is based on a novel active learning procedure for low-rank regression, which may be of independent interest. |
| title | Active learning of neural population dynamics using two-photon holographic optogenetics |
| topic | Neurons and Cognition Machine Learning |
| url | https://arxiv.org/abs/2412.02529 |