Responses of neurons that integrate multiple sensory inputs are traditionally characterized in terms of a set of empirical principles. However, a simple computational framework that accounts for these ...

Abstract: Normalization techniques are essential for accelerating the training and improving the generalization of deep neural networks (DNNs), and have successfully been used in various applications.

Data normalization is vital to single-cell sequencing, addressing limitations presented by low input material and various forms of bias or noise present in the sequencing process. Several such ...

Reinforcement learning research in humans and other species indicates that rewards are represented in a context-dependent manner. More specifically, reward representations seem to be normalized as a ...

Various data structures are expected to have consistent constraints and or normalization applied to their keys and/or values when being used after creation. In order to facilitate normalization be ...