A 3D ray traced biological neural network learning model.

ABSTRACT: Training large neural networks on big datasets requires significant computational resources and time. Transfer learning reduces training time by pre-training a base model on one dataset and transferring the knowledge to a new model for another dataset. However, current choices of transfer learning algorithms are limited because the transferred models always have to adhere to the dimensions of the base model and can not easily modify the neural architecture to solve other datasets. On the other hand, biological neural networks (BNNs) are adept at rearranging themselves to tackle completely different problems using transfer learning. Taking advantage of BNNs, we design a dynamic neural network that is transferable to any other network architecture and can accommodate many datasets. Our appro

SUBMITTER: Yuen B 

PROVIDER: S-EPMC11525811 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature