RayBNN: A 3-D Biological Neural Network Transfer 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 transfer learning algorithms are extremely limited because the transferred models always have to adhere to the dimensions of the base model and can not easily change the neural architecture to solve new 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 novel neural network that is transferable to any other network architecture and can accommodate many datasets. Our novel approach uses raytracing to connect neurons in a three-dimensional space, allowing the network to grow into any shape or size. In the Alcala dataset, our transfer learning algorithm trains the fastest across changing environments and input sizes. In the future, this network may be considered for implementation on real biological neural networks to decrease power consumption.