Self-Attention-Based Contextual Modulation Improves Neural System Identification
Authors: Isaac Lin, Tianye Wang, Shang Gao, Tang Shiming, Tai Lee
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | 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. |