Complex priors and flexible inference in recurrent circuits with dendritic nonlinearities
Authors: Benjamin S. H. Lyo, Cristina Savin
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | 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 blyo@nyu.edu Cristina Savin Center for Neural Science, Center for Data Science New York University csavin@nyu.edu |
| 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. |