A probabilistic population code based on neural samples
Authors: Sabyasachi Shivkumar, Richard Lange, Ankani Chattoraj, Ralf Haefner
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The top row of Figure 2 shows a numerical approximation to the posterior over s for the finite sample case and illustrates its convergence for t for the example model described in the previous section. As expected, posteriors for small numbers of samples are both wide and variable, and they get sharper and less variable as the number of samples increases (three runs are shown for each condition). Since the mean samples ( x) only depends on the marginals over x, we can approximate it using the mean field solution for our image model. The bottom row of Figure 2 shows the corresponding population responses: spike count for each neurons on the y axis sorted by the preferred stimulus of each neuron on the x axis. |
| Researcher Affiliation | Academia | Sabyasachi Shivkumar , Richard D. Lange , Ankani Chattoraj , Ralf M. Haefner Brain and Cognitive Sciences, University of Rochester {sshivkum, rlange, achattor, rhaefne2}@ur.rochester.edu |
| Pseudocode | No | The paper contains mathematical derivations and descriptions, but no explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing open-source code or links to a code repository. |
| Open Datasets | No | The paper mentions models being 'trained on natural images [14, 6]' in a general sense, referring to past work. However, it does not specify a particular public dataset used for the 'numerical approximation' and 'simulations' presented in this paper, nor does it provide concrete access information (link, DOI, specific citation) for a dataset used in their own work. |
| Dataset Splits | No | The paper does not provide information on training, validation, or test splits for any dataset used in its simulations or numerical approximations. |
| Hardware Specification | No | The paper does not specify any hardware (e.g., GPU, CPU models, or computational infrastructure) used for its simulations or numerical approximations. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers needed to replicate the work. |
| Experiment Setup | No | The paper describes mathematical models and derivations, along with numerical approximations and simulations. However, it does not provide specific experimental setup details such as hyperparameter values, optimization settings, or other configuration parameters commonly found in empirical machine learning papers. |