Efficient estimation of neural tuning during naturalistic behavior
Authors: Edoardo Balzani, Kaushik Lakshminarasimhan, Dora Angelaki, Cristina Savin
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | When applied to neural recordings from monkeys performing a virtual reality spatial navigation task, P-GAM reveals mixed selectivity and preferential coupling between neurons with similar tuning. We demonstrate the efficiency of this procedure using artificial data, showing that P-GAM outperforms standard GLMs. |
| Researcher Affiliation | Academia | Edoardo Balzani Center for Neural Science New York University New York, NY, 10003 eb162@nyu.edu Kaushik Lakshminarasimhan Center for Theoretical Neuroscience Columbia University New York, NY, 10027 jl5649@columbia.edu Dora E. Angelaki Center for Neural Science New York University New York, NY, 10003 da93@nyu.edu Cristina Savin Center for Neural Science Center for Data Science New York University New York, NY, 10003 cs5360@nyu.edu |
| Pseudocode | No | The paper describes iterative procedures in numbered list format within the text, but does not include explicitly labeled "Pseudocode" or "Algorithm" blocks/figures. |
| Open Source Code | Yes | 1Code available at: https:/github.com/Balzani Edoardo/PGAM. |
| Open Datasets | Yes | When applied to neural recordings from monkeys performing a virtual reality spatial navigation task, P-GAM recovers known features of the neural code, in particular mixed selectivity[15] and structured noise correlations [16, 17, 18]. [3] Kaushik J. Lakshminarasimhan, Eric Avila, Erin Neyhart, Gregory C. De Angelis, Xaq Pitkow, and Dora E. Angelaki. Tracking the Mind s Eye: Primate Gaze Behavior during Virtual Visuomotor Navigation Reflects Belief Dynamics. Neuron, 106(4):662 674.e5, May 2020. |
| Dataset Splits | No | The paper mentions using cross-validation for hyperparameter optimization and model comparison ("optimized using a grid search, with the cross-validated pseudo-r2 as optimization objective"), but does not provide specific training/validation/test dataset split percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using the "statsmodels python library [29]" but does not provide a specific version number for it or any other software dependencies. |
| Experiment Setup | Yes | The order of the splines and the knots locations are model hyperparameters that could be optimized by cross-validation; in practice, we use cubic splines (m = 4) and manually choose knots that reasonably cover the input range. We calibrated the parameters of the ground truth model to a relatively realistic setting (30min of data, sampled in 6ms bins; 5Hz mean firing rates, see source code for details). A typical session lasts about 90min, with spike counts measured in 6ms time bins; the analysis presented here includes 30 sessions from one animal. |