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..
Deep Learning Models of the Retinal Response to Natural Scenes
Authors: Lane McIntosh, Niru Maheswaranathan, Aran Nayebi, Surya Ganguli, Stephen Baccus
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We find that deep neural network models markedly outperform previous models in predicting retinal responses both for white noise and natural scenes. |
| Researcher Affiliation | Academia | Lane T. Mc Intosh 1, Niru Maheswaranathan 1, Aran Nayebi1, Surya Ganguli2,3, Stephen A. Baccus3 1Neurosciences Ph D Program, 2Department of Applied Physics, 3Neurobiology Department Stanford University EMAIL |
| Pseudocode | No | The paper contains architectural diagrams (Figure 1, Figure 2.1) but no pseudocode or explicitly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The spiking activity of a population of tiger salamander retinal ganglion cells was recorded in response to both sequences of natural images jittered with the statistics of eye movements and high resolution spatiotemporal white noise. |
| Dataset Splits | Yes | More details on the stimuli, retinal recordings, experimental structure, and division of data for training, validation, and testing are given in the Supplemental Material. |
| Hardware Specification | Yes | LM: NSF, NVIDIA Titan X Award |
| Software Dependencies | No | Optimization was performed using ADAM [20] via the Keras and Theano software libraries [21]. This lists software libraries but does not provide specific version numbers. |
| Experiment Setup | Yes | Model parameters were optimized to minimize a loss function corresponding to the negative log-likelihood under Poisson spike generation. Optimization was performed using ADAM [20] via the Keras and Theano software libraries [21]. The networks were regularized with an β2 weight penalty at each layer and an β1 activity penalty at the ο¬nal layer, which helped maintain a baseline ο¬ring rate near 0 Hz. Models were trained over the course of 100 epochs, with early-stopping guided by a validation set. |