A sampling-based circuit for optimal decision making
Authors: Camille Rullán Buxó, Cristina Savin
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here we propose a spiking network model that maps neural samples of a task-specific marginal distribution into an instantaneous representation of uncertainty via a procedure inspired by online kernel density estimation, so that its output can be readily used for decision making. Our model is consistent with experimental results at the level of single neurons and populations, and makes predictions for how neural responses and decisions could be modulated by uncertainty and prior biases. |
| Researcher Affiliation | Academia | Camille E. Rullán Buxó Center for Neural Science New York University New York, NY 10003 ch2880@nyu.edu Cristina Savin Center for Neural Science Center for Data Science New York University New York, NY 10003 csavin@nyu.edu |
| Pseudocode | No | The paper describes the model and its dynamics mathematically and verbally but does not provide pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper describes using synthetic examples ('linear Gaussian graphical model', 'two-component Gaussian mixture') but does not reference any publicly available or open datasets with access information (link, DOI, citation, etc.). |
| Dataset Splits | No | The paper does not specify training/test/validation dataset splits or reference predefined splits. It discusses 'simulated data' and 'test stimuli' but not in the context of standard dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper refers to 'simulation parameters' but does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings. |