Neural Network Architecture Choices Drive Fundamental Differences in Circuit Solutions and Cognitive Task Performance
A comprehensive study demonstrates that seemingly minor architectural choices in neural networks lead to fundamentally different circuit solutions for the same cognitive tasks. These differences significantly impact how networks handle unexpected inputs and generalize beyond their training data, with important implications for modeling biological intelligence.
Architectural Choices Shape Neural Circuit Solutions
According to research published in Nature Machine Intelligence, the selection of activation functions and connectivity constraints in recurrent neural networks (RNNs) leads to fundamentally different circuit mechanisms for solving identical cognitive tasks. The study analyzed six distinct RNN architectures using three common activation functions – ReLU, sigmoid, and tanh – with and without Dale’s law connectivity constraints, which restrict units to being exclusively excitatory or inhibitory like biological neurons.