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  • Review Article
  • Published:

Normalization as a canonical neural computation

An Erratum to this article was published on 18 January 2013

This article has been updated

Key Points

  • Normalization computes a ratio between the response of an individual neuron and the summed activity of a pool of neurons.

  • The normalization model was developed to explain responses in the primary visual cortex (V1), and has been seen to operate in a variety of other regions of the visual system: light adaptation in the retina, contrast normalization in the retina and lateral geniculate nucleus, and visual processing in higher visual cortical areas beyond V1.

  • Normalization has also been proposed to be at the root of the modulatory effects of visual attention on neural responses in the visual cortex.

  • Normalization is seen in multiple species and brain regions. These include olfactory processing and representation in the fruitfly antennal lobe, the encoding of value in the posterior parietal cortex, multisensory integration of visual motion and vestibular signals, and auditory processing in the primary auditory cortex.

  • Different (feedforward and feedback) neural circuits and mechanisms might perform normalization, including presynaptic inhibition, shunting inhibition, synaptic depression, changes in the amplitude of ongoing activity and balanced amplification.

  • The effects of normalization can be measured behaviourally.

  • The computational benefits of normalization include maximizing sensitivity, providing invariance with respect to some stimulus dimensions at the expense of others, facilitating the decoding of a distributed neural representation, facilitating the discrimination among representations of different stimuli, providing max-pooling (winner-take-all competition) and reducing redundancy.

  • Understanding canonical neural computations such as normalization may shed light on psychiatric, neurological and developmental disorders.

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.

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Figure 1: Normalization in the olfactory system of the fruitfly.
Figure 2: Normalization in the retina.
Figure 3: Normalization in the primary visual cortex.
Figure 4: Attentional modulation of responses in the visual cortex and predictions of the normalization model of attention.
Figure 5: Some networks and mechanisms that have been proposed for normalization.

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Change history

  • 18 January 2013

    On page 52 of this article, in the legend for figure 1, the text "lower concentrations are shown by darker colours" should have read "lower concentrations are shown by darker colours". This has been corrected in the online version.

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Acknowledgements

We thank K. Harris, E. Simoncelli, J. Linden, R. Wilson and F. Rieke for helpful comments on the manuscript. his work was supported by awards from the Medical Research Council and by an Advanced Investigator award from the European Research Council (to M.C.) and by US National Institutes of Health grants R01-EY016752 and R01-EY019693 (to D.J.H.). M.C. holds the GlaxoSmithKline/Fight for Sight Chair in Visual Neuroscience.

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Glossary

Attention

The cognitive process of selecting one of many possible stimuli or events. In this Review, we focus on visuospatial attention. Rigorous methods have been developed for quantifying the effects of attention on performance.

Local contrast

(Also known as Weber contrast). An image that is obtained by subtracting the intensity at each location by the mean intensity averaged over a nearby region and dividing the result by that mean intensity.

Summation field

A region of sensory space that provides drive to a neuron. In many sensory systems, neurons derive their stimulus selectivity from a weighted sum of sensory inputs. The summation field comprises the weights in this sum.

Suppressive field

A region of sensory space providing suppression. In the normalization model, responses are suppressed by a weighted sum of activity of a population of neurons. The suppressive field comprises the weights in this weighted sum.

Grating contrast

(Also known as Michelson contrast). The contrast of a grating is given by twice the mean intensity minus the lowest intensity divided by the highest intensity. This is often expressed as a percentage. A 100% contrast grating is one in which the black bars have zero intensity.

Response saturation

Neural responses that increase with the strength of the input but progressively level off with very strong inputs. Normalization controls the strength at which responses saturate.

Normalization factor

A weighed sum of activity of a population of neurons, as determined by the suppressive field.

Winner-take-all

A neural computation in which the response depends on the maximum of the inputs.

MT

(Middle temporal area). Primate cortical area in which most neurons are selective for speed and direction of visual motion.

V4

Primate visual cortical area in which neurons respond selectively to combinations of visual features. The modulatory effects of attention on neural activity have been extensively studied in V4.

IT

(Inferotemporal cortex). A region of primate cortex in which neurons respond selectively to pictures of objects, faces and complex combinations of visual features.

Spectrotemporal receptive field

The receptive field of auditory neurons, which is typically defined in terms of sound frequency and time.

MST

(medial superior temporal area). Primate cortical area in which neurons combine information about visual motion, head movements and eye movements.

Heading

Trajectory of movement through the environment.

Lateral intraparietal area

Primate cortical area in which neural activity depends on visual input, eye movements, attention to visual input, intention to make an eye movement and factors that affect when and where to move the eyes (including the expected probability and magnitude of a reward).

Rational choice theory

Economic model of decision making.

Attentional gain factors

A key component of the normalization model of attention. Each attentional gain factor corresponds to a particular spatial location and sensory feature, and has a value that is larger than one when that location and/or feature is attended.

Gain control

Change in gain that multiplies or divides the amplitude of the response to an input.

Ongoing activity

Fluctuations in neural activity in the absence of any change in sensory inputs or task demands.

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Carandini, M., Heeger, D. Normalization as a canonical neural computation. Nat Rev Neurosci 13, 51–62 (2012). https://doi.org/10.1038/nrn3136

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