Power-law efficient neural codes provide general link between perceptual bias and discriminability
Authors: Michael Morais, Jonathan W. Pillow
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In simulations, we explore a range of SNRs and power-law efficient codes to verify these results, and examine a variety of decoders including posterior mode, median, and mean estimators (Section 5), demonstrating the universality of the bias-discriminability relationship across a broad space of models. We used simulated data to test our derived nonlinear and linear relationships between bias and discriminability (eqs. 12 & 13). |
| Researcher Affiliation | Academia | Michael J. Morais & Jonathan W. Pillow Princeton Neuroscience Institute & Department of Psychology Princeton University mjmorais, pillow@princeton.edu |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about releasing source code for the methodology described. |
| Open Datasets | No | The paper describes generating 'random smooth priors' for simulations ('As such, we draw random priors as exponentiated draws from Gaussian processes on [−π, π], according to Z exp(f), where f ∼ GP(0, K)') rather than using a pre-existing publicly available dataset, and does not provide access information for this generated data. |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset split information. It discusses using 'simulated data' and 'random priors' for its analysis without specifying formal data partitions for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In all simulations, we propagate each stimulus x ∼ p(x) on a finely tiled grid through a Bayesian observer model numerically, computing a posterior p(x|y) / p(x)N(y; x, kp(x) q) for a power-law efficient code under many powers q and SNRs k, and for each computed the Bayesian estimators associated with various loss functions of interest. [...] As such, we draw random priors as exponentiated draws from Gaussian processes on [−π, π], according to Z exp(f), where f ∼ GP(0, K) for Z as a normalizing constant, and K the radial basis function kernel wherein K(xi, xj) = 2 exp(− 1 2σ2kxixjk2) with magnitude = 1 and lengthscale = 0.75, selected such that a typical prior was roughly bimodal. |