On the relations of LFPs & Neural Spike Trains
Authors: David E Carlson, Jana Schaich Borg, Kafui Dzirasa, Lawrence Carin
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results are based on data sets in which spike and LFP data are recorded simultaneously from up to 16 brain regions in a mouse. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering 2Department of Psychiatry and Behavioral Sciences Duke University Duham, NC 27701 |
| Pseudocode | No | The paper describes the model and inference steps mathematically and textually but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any link to source code or explicitly state that the code for the methodology is open-source or publicly available. |
| Open Datasets | No | The paper describes using two datasets from mouse brains, referencing a previous publication ([7]) for the first dataset's description. However, it does not provide any direct link, DOI, repository, or explicit statement of public availability for either dataset. |
| Dataset Splits | Yes | Cross-validation was performed using leave-one-out analysis over time bins, using the error metric of reduction in fractional error (RFE), 1 ||xb ˆxb||2 2/||xb||2 2. |
| Hardware Specification | No | The paper mentions that "Neuronal activity was recorded using a Multi-Neuron Acquisition Processor (Plexon)" but does not specify the computing hardware (e.g., GPU, CPU models, memory) used for running the computational experiments. |
| Software Dependencies | No | The paper mentions that "Spike sorting is performed on the spiking data by a VB implementation of [6]", which refers to a method, but does not list any specific software libraries or tools with version numbers. |
| Experiment Setup | Yes | For all experiments we choose L such that the dictionary element covered 0.5 seconds before and after each spike event. In the experiments, in one example the bins are chosen to be 30 seconds wide (novel-environment data) and in the other 1 minute (sleep-cycle data), and these are principally chosen for computational convenience (the second data set is nine times longer). Similar results were found with windows as narrow as 10 second, or as wide as 2 minutes. |