Normalization Principles in Computational Neuroscience

Abstract

A core question in systems and computational neuroscience is how the brain represents information. Identifying principles of information coding in neural circuits is critical to understanding brain organization and function in sensory, motor, and cognitive neuroscience. This provides a conceptual bridge between the underlying biophysical mechanisms and the ultimate behavioral goals of the organism. Central to this framework is the question of computation: what are the relevant representations of input and output, and what algorithms govern the input-output transformation? Remarkably, evidence suggests that certain canonical computations exist across different circuits, brain regions, and species. Such computations are implemented by different biophysical and network mechanisms, indicating that the unifying target of conservation is the algorithmic form of information processing rather than the specific biological implementation. A prime candidate to serve as a canonical computation is divisive normalization, which scales the activity of a given neuron by the activity of a larger neuronal pool. This nonlinear transformation introduces an intrinsic contextual modulation into information coding, such that the selective response of a neuron to features of the input is scaled by other input characteristics. This contextual modulation allows the normalization model to capture a wide array of neural and behavioral phenomena not captured by simpler linear models of information processing. The generality and flexibility of the normalization model arises from the normalization pool, which allows different inputs to directly drive and suppress a given neuron, effectively separating information that drives excitation and contextual modulation. Originally proposed to describe responses in early visual cortex, normalization has been widely documented in different brain regions, hierarchical levels, and modalities of sensory processing; furthermore, recent work shows that the normalization extends to cognitive processes such as attention, multisensory integration, and decision making. This ubiquity reinforces the canonical nature of the normalization computation and highlights the importance of an algorithmic framework in linking biological mechanism and behavior.