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..
Biologically plausible solutions for spiking networks with efficient coding
Authors: Veronika Koren, Stefano Panzeri
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we simulate the network with biologically plausible parameters and analyze the behavior of the network with respect to the complexity of E-E synaptic connections. |
| Researcher Affiliation | Academia | Veronika Koren Department of Excellence for Neural Information Processing Center for Molecular Neurobiology (ZMNH) University Medical Center Hamburg-Eppendorf (UKE) Falkenried 94, 20251 Hamburg, Germany EMAIL Stefano Panzeri Department of Excellence for Neural Information Processing Center for Molecular Neurobiology (ZMNH) University Medical Center Hamburg-Eppendorf (UKE) Falkenried 94, 20251 Hamburg, Germany Istituto Italiano di Tecnologia Genoa, Italy EMAIL |
| Pseudocode | No | The paper describes the model and its dynamics using mathematical equations and text, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] see Supplementary material |
| Open Datasets | No | The paper describes network simulations using internally defined parameters and generated inputs (e.g., 'step input in one of the input features'), rather than utilizing an external public dataset. |
| Dataset Splits | No | The paper describes theoretical derivations and simulations of a neural network model, rather than conducting experiments on a dataset that would require explicit training, validation, or test splits. |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] Supplementary material |
| Software Dependencies | No | The paper does not provide specific software details with version numbers (e.g., library names, programming languages, or solvers) used for the experiments. |
| Experiment Setup | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] see Figure Captions |