Here we review neural tuning and representational geometry with the goal of clarifying the relationship between them. The tuning induces the geometry, but different sets of tuned neurons can induce the same geometry. The geometry determines the Fisher information, the mutual information and the ...
Here, we review recent studies adopting a representational geometry approach, and argue that important advances in social perception can be gained by triangulating on the structure of representations via three levels of analysis: neuroimaging, behavioral measures, and computational modeling. Among other ...
Neural population dynamics during reaching are better explained by a dynamical system than representational tuning. PLoS Comput. Biol. 12, e1005175 (2016). Article ADS PubMed PubMed Central Google Scholar Sussillo, D. & Barak, O. Opening the black box: low-dimensional dynamics in high-...
A. A. Between-subject prediction reveals a shared representational geometry in the rodent hippocampus. Curr. Biol. 31, 4293–4304 (2021). Herrero-Vidal, P., Rinberg, D. & Savin, C. Across-animal odor decoding by probabilistic manifold alignment. Adv. Neural Inform. Process. Syst. 34, ...
smooth tuning properties. We will first develop and validate our method, and then apply it to analyze Macaque V1 data. We will show that our method helps reveal insights into the structure of the neural code for visual orientation, the information content, and the geometry of the ...
2022-NIPS-Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints 2022-NIPS-On Measuring Excess Capacity in Neural Networks 2022-NIPS-Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks ...
Determination of hyperparameters and fine-tuning the model is a big challenge. Retraining models to track changes in data distribution is challenging. • Scaling computations, reducing the overhead of optimizing parameters, avoiding expensive inference and sampling. • In the absence of labeled data...
Our study leads us to propose a new, lightweight gather-excite pair of operators which yields significant improvements across different architectures, datasets and tasks, with minimal tuning of hyperparameters. We also investigate the effect of the operators on distributed representation learned by ...
Deep neural networks (DNNs) excel at visual recognition tasks and are increasingly used as a modeling framework for neural computations in the primate brain. Just like individual brains, each DNN has a unique connectivity and representational profile. He
smooth tuning properties. We will first develop and validate our method, and then apply it to analyze Macaque V1 data. We will show that our method helps reveal insights into the structure of the neural code for visual orientation, the information content, and the geometry of the ...