Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Estimating Fluctuations in Neural Representations of Uncertain Environments
Authors: Sahand Farhoodi, Mark Plitt, Lisa Giocomo, Uri Eden
NeurIPS 2020 | Venue PDF | 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 EMAIL Mark H. Plitt Department of Neurobiology Stanford University, Stanford, CA, USA EMAIL Lisa Giocomo Department of Neurobiology Stanford University, Stanford, CA, USA EMAIL Uri T. Eden Department of Mathematics and Statistics Boston University, Boston, MA, USA EMAIL |
| 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. |