Researchers at MIT Solve a Difference Equation Behind Interaction between Two Neurons through Synapses in order to Unlock a Fast and Efficient AI Algorithm
Machine learning systems that can perform representation learning in the context of spatial-temporal decision making include continuous-time neural networks. These models (DEs) are often represented by continuous differential equations. However, numerical DE solvers are limited in their expression when used on computers. This restriction has severely limited the scaling and understanding of natural physical processes like the dynamics in neural systems.
MIT researchers developed a \”liquid\” neural network model, a robust, fluid ML that can adapt and learn from changing situations. These methods could be used for safety-critical tasks like driving and flying.
As the number of synapses and neurons in the model increases, the mathematics become more complex and the cost to process the model increases.