Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Complex priors and flexible inference in recurrent circuits with dendritic nonlinearities
Authors: Benjamin S. H. Lyo, Cristina Savin
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In simulations, we demonstrate several scenarios of prior and posterior encoding, including nonlinear manifolds embedded in a higher dimensional ambient space as priors and several likelihoods corresponding to bottom-up and top-down evidence. We numerically tested the quality of the samples generated by our neural circuit in a toy example of a two-dimensional nonlinear manifold (shaped as a swiss-roll , see Fig. 1D inset) with linear dimensionality 3, embedded in an ambient feature space with dimensionality N = 10. While the quality of samples is harder to estimate, we also find good quality representations of a high dimensional prior trained on the MNIST dataset (Deng, 2012) (see Suppl. B.6). |
| Researcher Affiliation | Academia | Benjamin S. H. Lyo Center for Neural Science New York University EMAIL Cristina Savin Center for Neural Science, Center for Data Science New York University EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | A software implementation of the model is available at https://github.com/Savin-Lab-Code/Lyo Savin2023. |
| Open Datasets | Yes | While the quality of samples is harder to estimate, we also find good quality representations of a high dimensional prior trained on the MNIST dataset (Deng, 2012) (see Suppl. B.6). |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits, percentages, or explicit sample counts for reproduction. It mentions evaluating similarity using KL divergence but not in the context of formal 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 mentions tools like 'Adam optimizer' and 'torch autograd package' but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | The Adam optimizer with a learning rate of 3e-4 over 1.5e6 epochs. The Adam optimizer with a learning rate of 1e-4 over 5000 epochs. The Adam optimizer with a learning rate of 4e-3 for 5000 epochs. simulations use a depth of 7 and branching factor of 3, except in the most proximal section, which has a branching factor 4. Scalar γ is a hyperparameter that weighs the relative contribution of the log prior and log likelihood. This hyperparameter is set to 1. |