Flexible Context-Driven Sensory Processing in Dynamical Vision Models
Authors: Lakshmi Narasimhan Govindarajan, Abhiram Iyer, Valmiki Kothare, Ila Fiete
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study this Dynamical Cortical network (DCnet) in a visual cue-delay-search task and show that the model uses its own cue representations to adaptively modulate its perceptual responses to solve the task, outperforming state-of-the-art DNN vision and LLM models. |
| Researcher Affiliation | Academia | Lakshmi Narasimhan Govindarajan 1, 3, 4, Abhiram Iyer 2, 3, 4, Valmiki Kothare1, and Ila Fiete1, 3, 4 1Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 2Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 3Mc Govern Institute for Brain Research, MIT, Cambridge, MA 4K. Lisa Yang Integrative Computational Neuroscience (ICo N), MIT, Cambridge, MA |
| Pseudocode | No | The paper provides mathematical equations describing the model's dynamics in Appendix A.1 but does not present them in a structured pseudocode or algorithm block format. |
| Open Source Code | Yes | Code and datasets can be found here: Project repository. |
| Open Datasets | Yes | We draw inspiration from Clevr [48], a synthetic dataset for language-mediated visual reasoning and construct vis-count, a parametric visually-cued, delayed search task. |
| Dataset Splits | Yes | In total, our training (validation) dataset comprised of 384K (38K) trials. |
| Hardware Specification | Yes | All models were trained on A100 GPUs for 100 epochs each. |
| Software Dependencies | No | The paper mentions using "Adam W optimizer" and a "one-cycle learning rate scheduler," but it does not specify software dependencies like Python, PyTorch/TensorFlow, or other libraries with their specific version numbers. |
| Experiment Setup | Yes | We used an Adam W optimizer (momentum=0.9, β1 = 0.9, β2 = 0.999), a one-cycle learning rate scheduler with a warm-up period of 30 epochs and a maximum learning rate of 4e 4. DCnet was 4 layers deep ( 1.8M learnable parameters) and was trained with batches of 256 samples. |