The brain has a modular design. The advantages of modularity are well known to engineers: modules that can be replicated and cascaded, such as transistors and web servers, lie at the root of powerful technologies. The brain seems to apply this principle in two ways: with modular circuits and with modular computations. Anatomical evidence suggests the existence of canonical microcircuits that are replicated across brain areas, for example, across regions of the cerebral cortex 1,2 . Physiological and behavioural evidence suggests that canonical neural computations exist -standard computational modules that apply the same fundamental operations in a variety of contexts. A canonical neural computation can rely on diverse circuits and mechanisms, and different brain regions or different species may implement it with different available components. A third kind of computation has been seen to operate in various neural systems: divisive normalization. Normalization computes a ratio between the response of an individual neuron and the summed activity of a pool of neurons. Normalization was proposed in the early 1990s to explain non-linear properties of neurons in the primary visual cortex [17] [18] [19] . Similar computations 20 had been proposed previously to explain light adaptation in the retina [21] [22] [23] [24] , size invariance in the fly visual system 25 and associative memory in the hippocampus 26 . Evidence that has accumulated since then suggests that normalization plays a part in a wide variety of modalities, brain regions and species. Here, we review this evidence and suggest that normalization is a canonical neural computation in sensory systems and possibly also in other neural systems. We introduce normalization by describing results from the olfactory system of invertebrates. We then describe its operation in retina, in primary visual cortex, in higher visual cortical areas and in non-visual cortical areas, and discuss its role in sensory processing and in the modulatory effects of attention. Finally, we review the multiple mechanisms and circuits that may be associated with normalization, and the behavioural measurements that are captured well by normalization. Two independent sections define the basic elements of the normalization equation and the many roles that have been proposed for normalization in relation to optimizing the neural code. Abstract | There is increasing evidence that the brain relies on a set of canonical neural computations, repeating them across brain regions and modalities to apply similar operations to different problems. A promising candidate for such a computation is normalization, in which the responses of neurons are divided by a common factor that typically includes the summed activity of a pool of neurons. Normalization was developed to explain responses in the primary visual cortex and is now thought to operate throughout the visual system, and in many other sensory modalities and brain regions. Normalization may underlie operations such as the representation of odours, the modulatory effects of visual attention, the encoding of value and the integration of multisensory information. Its presence in such a diversity of neural systems in multiple species, from invertebrates to mammals, suggests that it serves as a canonical neural computation.
Adaptive normalization has been described as a canonical neural computation in neuroscience because biological systems employ it on essentially all sensory modalities REF .
Normalization as a canonical neural computation
Nature Reviews Neuroscience
Nature Reviews Neuroscience
Medicine Biology