Across-animal odor decoding by probabilistic manifold alignment
Authors: Pedro Herrero-Vidal, Dmitry Rinberg, Cristina Savin
NeurIPS 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | When applied to recordings from the mouse olfactory bulb, our approach reveals low-dimensional population dynamics that are odor specific and have consistent structure across animals. |
| Researcher Affiliation | Academia | Pedro Herrero-Vidal Center for Neural Science Neuroscience Institute New York University EMAIL Dmitry Rinberg Neuroscience Institute Center for Neural Science NYU Langone Health EMAIL Cristina Savin Center for Neural Science Center for Data Science New York University EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code available: github.com/pedroherrerovidal/am LDS. |
| Open Datasets | Yes | We tested our model on neural recordings from a 64-site grid-electrode stereotaxically implanted over the dorsal part of the olfactory bulb in five mice [13]. |
| Dataset Splits | Yes | We used Bayesian model comparison to determine the dimensionality of the latent space from data (evaluated on a separate validation set). |
| Hardware Specification | No | The paper mentions 'on a 2.9GHz CPU' but does not specify a particular CPU model or other detailed hardware components for reproducibility. |
| Software Dependencies | No | The paper mentions 'scikit-learn' but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | More specifically, we defined a shared low dimensional manifold (d = 3) and embedded K = 50 latent trajectories evolving over T = 41 time steps (Fig.2A). The stimulus-dependent inputs bk were constructed using a common template with stimulus-specific amplitude (individual dimensions scaled by a value drawn from N(1; 0.0004)) and rotation (evenly spaced over 170 degrees). The other latent dynamics parameters were set randomly: matrices Ak have diagonal entries drawn from N(0.4; 0.01) and off-diagonal drawn from N(0; 0.04) and the noise covariances Qk and Q0 are diagonal with variances drawn from N(0.55; 0.0025). We used d = 7 for all subsequent analyses. |