Biologically plausible solutions for spiking networks with efficient coding

Authors: Veronika Koren, Stefano Panzeri

NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 v.koren@uke.de 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 s.panzeri@uke.de
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