Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Learning a neural response metric for retinal prosthesis
Authors: Nishal P Shah, Sasidhar Madugula, EJ Chichilnisky, Yoram Singer, Jonathon Shlens
ICLR 2018 | Venue PDF | LLM Run Details | Input Tokens: 19,302 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,190 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using data from electrical stimulation experiments, we demonstrate that the learned metric could produce improvements in the performance of a retinal prosthesis.3.1 EXPERIMENTAL SETUP Spiking responses from hundreds of retinal ganglion cells (RGCs) in primate retina were recorded using a 512 electrode array system (Litke et al., 2004; Frechette et al., 2005). |
| Researcher Affiliation | Collaboration | 1Stanford University 2Google Brain 3Princeton University |
| Pseudocode | No | The paper describes the network architecture in Figure 6 and Table 1 but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code (no specific repository link, explicit code release statement, or code in supplementary materials). |
| Open Datasets | No | Spiking responses from hundreds of retinal ganglion cells (RGCs) in primate retina were recorded using a 512 electrode array system (Litke et al., 2004; Frechette et al., 2005). |
| Dataset Splits | No | The responses were partitioned into training (first 8 seconds) and testing (last 2 seconds) of each trial. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory) used for running its experiments. |
| Software Dependencies | No | Optimizer Adam (Kingma & Ba, 2014) (α = 0.01, β1 = 0.9, β2 = 0.999) Parameter updates 20,000 Batch size 100 Weight initialization Xavier initialization (Glorot & Bengio, 2010) (The paper mentions optimizers and initialization methods, but does not provide specific software dependencies with version numbers for reproducibility, such as Python or library versions.) |
| Experiment Setup | Yes | Optimizer Adam (Kingma & Ba, 2014) (α = 0.01, β1 = 0.9, β2 = 0.999) Parameter updates 20,000 Batch size 100 Weight initialization Xavier initialization (Glorot & Bengio, 2010) |