Analysis of Brain States from Multi-Region LFP Time-Series
Authors: Kyle R Ulrich, David E Carlson, Wenzhao Lian, Jana S Borg, Kafui Dzirasa, Lawrence Carin
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We examine brain states under different levels of arousal by recording LFPs simultaneously in multiple regions of the mouse brain, first, as mice pass through different stages of sleep, and second, as mice are moved from a familiar environment to a novel environment to induce interest and exploration. Figure 2 shows results on the toy data. The model correctly recovers exactly 3 states and 5 clusters, and, as seen in the figure, the state assignments and spectral densities of each cluster component are recovered almost perfectly. |
| Researcher Affiliation | Academia | Kyle Ulrich 1, David E. Carlson 1, Wenzhao Lian 1, Jana Schaich Borg 2, Kafui Dzirasa 2 and Lawrence Carin 1 1 Department of Electrical and Computer Engineering 2 Department of Psychiatry and Behavioral Sciences Duke University, Durham, NC 27708 |
| Pseudocode | No | The paper describes algorithmic steps and inference procedures in text, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about open-source code release or links to a code repository for the described methodology. |
| Open Datasets | No | Toy data: Data is generated for a single animal according to the proposed model in Section 2. Sleep data: Twelve hours of LFP data from sixteen different brain regions were recorded from three mice naturally transitioning through different levels of sleep arousal. Novel environment data: Thirty minutes of LFP data from five brain regions was recorded from five mice who were moved from their home cage to a novel environment approximately nine minutes into the recording. Data acquisition methods for the latter two datasets are discussed in [24]. |
| Dataset Splits | Yes | For further model verification, ten-fold cross-validation was used to compute predictive probabilities for held-out data (reported in Table 1), where we compare to two simpler versions of our model: 1) the HDP-HMM on brain states in (1) is replaced with an HDP, and 2) a single brain state. The data consists of W time-series windows for R regions of A animals; at random, 10% of these time-series windows were held-out, and the predictive distribution was used to determine their likelihood. |
| Hardware Specification | Yes | each iteration took on the order of 20 seconds using Matlab code on a PC with a 2.30GHz quad-core CPU and 8GB RAM. |
| Software Dependencies | No | The paper mentions "Matlab code" but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | Yes | For all results, we set Q = 10, H = 15, L = 25, stop the burn-in period after iteration 6, and start the subsequent computation period after iteration 25. Hyperparameters were set to γ0 = γ1 = .01, α0 = α1 = 1, µmin = 0, µmax = 50, νmax = 10, and e0 = f0 = 10−6. |