Neural system identification for large populations separating “what” and “where”
Authors: David Klindt, Alexander S. Ecker, Thomas Euler, Matthias Bethge
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate this architecture on ground-truth data to explore the challenges and limitations of CNN-based system identification. Moreover, we show that our network model outperforms current state-of-the art system identification models of mouse primary visual cortex.Our CNN with factorized readout outperformed all four baselines on all three scans (Table 1). |
| Researcher Affiliation | Academia | 1 Centre for Integrative Neuroscience, University of Tübingen, Germany 2 Bernstein Center for Computational Neuroscience, University of Tübingen, Germany 3 Institute for Ophthalmic Research, University of Tübingen, Germany 4 Institute for Theoretical Physics, University of Tübingen, Germany 5 Max Planck Institute for Biological Cybernetics, Tübingen, Germany 6 Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code to fit the models and reproduce the figures is available online at: https://github.com/david-klindt/NIPS2017 |
| Open Datasets | Yes | Moreover, we show that our model outperforms the current state-of-the-art on a publicly available dataset of mouse V1 responses to natural images [19]. |
| Dataset Splits | Yes | We use an initial learning rate of 0.001 and early stopping based on a separate validation set consisting of 20% of the training set. We fit all models using 80% of the training dataset for training and the remaining 20% for validation. |
| Hardware Specification | No | On this large dataset with 60.000 recordings from 8.000 neurons we were still able to fit the model on a single GPU and perform at 90% FEV (data not shown). |
| Software Dependencies | No | The paper mentions using the Adam optimizer and VGG-19 network, but does not provide specific version numbers for software dependencies like programming languages or deep learning frameworks. |
| Experiment Setup | Yes | We use an initial learning rate of 0.001 and early stopping based on a separate validation set consisting of 20% of the training set. When the validation error has not improved for 300 consecutive steps, we go back to the best parameter set and decrease the learning rate once by a factor of ten. After the second time we end the training. We find the optimal regularization weights λm and λw via grid search. We trained the model using Adam with a batch-size of 64. |