Point process latent variable models of larval zebrafish behavior
Authors: Anuj Sharma, Robert Johnson, Florian Engert, Scott Linderman
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | With a dataset of over 120,000 swim bouts, we show that our models reveal interpretable discrete classes of swim bouts and continuous internal states like hunger that modulate their dynamics. These models are a major step toward understanding the natural behavioral program of the larval zebrafish and, ultimately, its neural underpinnings. Figure 1 illustrates our experimental setup for collecting behavioral data of freely swimming larval zebrafish [12]. Sections 6 and 7 present our results from applying our methods to synthetic and real data. |
| Researcher Affiliation | Academia | Anuj Sharma Columbia University Robert E. Johnson Harvard University Florian Engert Harvard University Scott W. Linderman Columbia University |
| Pseudocode | No | The paper describes the inference algorithm in text but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific statements about open-source code availability, nor does it include a link to a code repository or mention code in supplementary materials. |
| Open Datasets | No | Each trial consists of a sequence of up to 350 swim bouts (b.) and we recorded over 120,000 bouts from 130 fish over about 1000 trials. Figure 1 illustrates our experimental setup for collecting behavioral data of freely swimming larval zebrafish [12]. |
| Dataset Splits | Yes | We simulate a synthetic dataset consisting of S = 1000 sequences, each of which contains Ns = 300 events and shares the same global parameters θ. We use Str = 750 of these sequences for training and save 250 for evaluation. We use 105 fish for training and 25 for model comparison. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or library versions (e.g., Python, PyTorch, TensorFlow versions) that would be needed to replicate the experiment. |
| Experiment Setup | Yes | We fit the model with 50 epochs of stochastic gradient ascent. We simulate a synthetic dataset... using a subset of size Ns,u = 15 for the sparse GP approximation. |