Estimating Fluctuations in Neural Representations of Uncertain Environments
Authors: Sahand Farhoodi, Mark Plitt, Lisa Giocomo, Uri Eden
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the application of this approach to the analysis of population activity in the CA1 region of hippocampus of a mouse moving through ambiguous virtual environments. Our analyses demonstrate that many hippocampal cells express significant trial-to-trial variability in their representations and that the population representation can fluctuate rapidly between environments within a single trial when spatial cues are most ambiguous. |
| Researcher Affiliation | Academia | Sahand Farhoodi Department of Mathematics and Statistics Boston University, Boston, MA, USA sahand@bu.edu Mark H. Plitt Department of Neurobiology Stanford University, Stanford, CA, USA mplitt@stanford.edu Lisa Giocomo Department of Neurobiology Stanford University, Stanford, CA, USA giocomo@stanford.edu Uri T. Eden Department of Mathematics and Statistics Boston University, Boston, MA, USA tzvi@bu.edu |
| Pseudocode | No | The paper describes algorithms using mathematical equations, but no structured pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not provide any statement about making its source code publicly available or provide a link to a code repository. |
| Open Datasets | No | The paper describes using 'two-photon imaging recordings of CA1 pyramidal cells' collected under 'two different training conditions'. However, no concrete access information (link, DOI, repository, or citation to a public dataset) is provided for this data. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits (e.g., percentages or sample counts) needed to reproduce the experiment. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) used for running experiments are mentioned. |
| Software Dependencies | No | The paper mentions methods like 'Gamma-distributed generalized linear model' and 'K-means clustering algorithm' but does not provide specific software or library names with version numbers required for reproducibility. |
| Experiment Setup | No | The paper describes experimental conditions (e.g., morph levels, training conditions) and model parameters (e.g., Kj=2), but does not provide specific hyperparameters (like learning rate, batch size, epochs) or detailed system-level training settings for the model itself. |