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..
Self-Attention-Based Contextual Modulation Improves Neural System Identification
Authors: Isaac Lin, Tianye Wang, Shang Gao, Tang Shiming, Tai Lee
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we demonstrate that adding a simple self-attention layer to a CNN can improve neural response prediction of macaque V1 neurons in two performance metrics: overall tuning correlation and prediction of the tuning peaks. To understand the mechanism driving improvement, we assessed the three contextual modulation mechanisms convolutions, self-attention, and a fully connected readout layer. We obtained a dataset of neuronal responses measured using two-photon imaging with GCa MP5 from two awake behaving macaque monkeys... We compared the performance of the ff+sa-CNN model to the parameter-matched baseline ff-CNN model and found that incorporating self-attention significantly improved correlation and both peak tuning metrics (see first two rows of Table 1). |
| Researcher Affiliation | Academia | Isaac Lin1, , Tianye Wang2, Shang Gao1,3, Shiming Tang2, Tai Sing Lee1, 1Carnegie Mellon University, 2Peking University, 3Massachusetts Institute of Technology |
| Pseudocode | No | The paper describes methods in text and uses diagrams for model architectures (e.g., Figure 2) but does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | A.15 CODE FOR EXPERIMENTS The code is hosted at the github repository: https://github.com/lucanren/sacnn |
| Open Datasets | Yes | We obtained a dataset of neuronal responses measured using two-photon imaging with GCa MP5 from two awake behaving macaque monkeys... in response to 34k and 49k natural images extracted from the Image Net dataset. |
| Dataset Splits | Yes | The 30k-50k images in the training set were presented once, and the 1000 images in the validation set were tested once with 10 repeats. |
| Hardware Specification | Yes | Training and computations were performed on an in-house computing cluster with GPU (NVIDIA V100 or similar) nodes. |
| Software Dependencies | No | The paper mentions 'optimizer = Adam' and 'loss = MSE' but does not specify programming language versions or library versions (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We list key training hyperparameters here: (1) batch size = 50, (2) learning rate = 0.001, (3) optimizer = Adam, (4) loss = MSE, (5) epochs = 50. |