Natural gradient enables fast sampling in spiking neural networks
Authors: Paul Masset, Jacob Zavatone-Veth, J. Patrick Connor, Venkatesh Murthy, Cengiz Pehlevan
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Figure 2 and in Supplemental Figures E.1-E.3, we show that the geometry-aware sampler is more robust to increasing the correlation of the parameters and the dimensionality, allowing inference at behavioral timescales. In Figures 3 and 4, and in Supplemental Figure E.4, we show that naïve choices of Γ lead to vanishing spike rates at strong correlations ρ and large parameter-space dimensionalities np. This results in dramatic underestimation of the mean and variance of the target distribution, which is resolved by choosing the geometry appropriately, again allowing inference at behavioral time-scales. |
| Researcher Affiliation | Academia | Paul Masset1,2 , Jacob A. Zavatone-Veth1,3 , J. Patrick Connor4, Venkatesh N. Murthy1,2 , Cengiz Pehlevan1,4 1Center for Brain Science, 2Department of Molecular and Cellular Biology, 3Department of Physics, 4John A. Paulson School of Engineering and Applied Sciences, Harvard University Cambridge, MA 02138 |
| Pseudocode | No | The paper describes its methods using mathematical equations and prose but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about making source code publicly available or provide any links to a code repository. |
| Open Datasets | No | The paper describes generating synthetic data for simulations (e.g., 'equicorrelated multivariate distributions', 'Gaussian distribution N(µ, Σ)') but does not provide concrete access information or citations for a publicly available or open dataset. |
| Dataset Splits | No | The paper discusses 'simulations' and 'realizations' for statistical analysis, but it does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers. |
| Experiment Setup | No | The paper mentions some parameters related to the model (e.g., 'τm = 20 ms', 'np = 20-dimensional', 'nn = 200 neurons', 'correlation ρ = 0.75') and simulation conditions, but it lacks a comprehensive description of the experimental setup, including specific hyperparameter values, optimizer settings, or detailed training configurations. |