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. 2011 Sep;34(9):464-73.
doi: 10.1016/j.tins.2011.07.002. Epub 2011 Aug 15.

What Can Mice Tell Us About How Vision Works?

Free PMC article

What Can Mice Tell Us About How Vision Works?

Andrew D Huberman et al. Trends Neurosci. .
Free PMC article


Understanding the neural basis of visual perception is a long-standing fundamental goal of neuroscience. Historically, most vision studies were carried out on humans, macaques and cats. Over the past 5 years, however, a growing number of researchers have begun using mice to parse the mechanisms underlying visual processing; the rationale is that, despite having relatively poor acuity, mice are unmatched in terms of the variety and sophistication of tools available to label, monitor and manipulate specific cell types and circuits. In this review, we discuss recent advances in understanding the mouse visual system at the anatomical, receptive field and perceptual level, focusing on the opportunities and constraints those features provide toward the goal of understanding how vision works.


Box 1 Figure I
Box 1 Figure I. Cellular and physiological characterization of genetically identified RGC subtypes in mice
Figure 1
Figure 1. Basic architecture of mouse retinal ganglion cells (RGCs) and visual pathways
(Left panel) Mouse RGCs include 22 anatomically distinct subtypes, anatomically termed “G1-G22” [30]. Representative examples of reconstructions of RGCs in each category (based on dye-injections, are shown. Dark lines depict the somas and dendritic arbors of each cell. Lighter gray lines indicate dendritic arbors in deeper layers of the inner retina. Each patch of retina includes some, or all, of these 22 subtypes and all of these extend axons into the brain. Reproduced, with permission, from [30] (Right panel) Schematic diagram of the mouse visual pathways described here, showing direct retinal projections (solid arrows) to the dorsal lateral geniculate nucleus (dLGN) and to the superior colliculus (SC) as well as geniculo-cortical pathways from dLGN to visual cortex (dashed arrows). Note: for simplicity, most of the 20 plus subcortical visual targets are not shown here. Shaded portions of the retinas indicate the location of RGCs whose axons do not cross at the optic chiasm and instead project ipsilaterally. Ovals in the dLGN correspond to the termination zones of the ipsilateral projecting RGC axons. The binocular (“B”) and monocular (“M”) fields in the V1 area of the cortex are shown. The lighter lines in the dLGN and SC represent the approximate boundaries where axons of different functional categories of RGCs terminate [31, 33, 36].
Figure 2
Figure 2. Cortical response properties are similar across species despite differences in spatial resolution
A) The degree of orientation selectivity is comparable across species (left), despite the fact that their behavioral acuity - the smallest feature they can detect - varies by nearly two orders of magnitude (right). Mouse data reproduced from [50, 92], others from [51]. B) Three example simple cell receptive fields in V1, from mouse (upper row) and monkey (lower row), showing a similar range of spatial structure. Note that the scale bar for mouse is 20 degrees, while that for monkey is approximately 1 degree. Red and blue correspond to On and Off subregions, respectively. The similarity in structures demonstrates that cortical neurons in both species respond to similar visual features, but of different size. Mouse data modified, with permission, from [50]; monkey data reproduced, with permission, from [93]. In both studies, receptive fields were measured by reverse correlation methods in anesthetized animals.
Figure 3
Figure 3. Behavioral paradigms for assessing mouse visual function
A) Optomotor response as a measure for visual thresholds [82]. The mouse reflexively tracks moving gratings that are presented on computer screens surrounding the enclosure. The spatial frequency of the gratings is varied to determine the smallest features that the mouse will track. B) Running task for the measurement of photoreceptor thresholds [84],. A filtered light-emitting diode (LED) is used to provide a defined luminance source. When the luminance changes, the mouse must stop running on the wheel in order to receive a reward (eg. water, food). The animal’s motion is automatically recorded by an infrared (IR) emitter-detector. The defined geometry allows precise measurements of photon fluxes at the eye. C) Two-alternative forced choice swimming task [92]. The mouse is placed in a water-filled Y-maze, with a hidden platform on one end. A computer-controlled stimulus is presented on the side with the platform, and the mouse must detect the stimulus and swim toward the correct side in order to find the platform and be released from the water. D) Go/no-go licking task, combined with two-photon imaging[90]. Head-fixed mice are trained to lick only when a grating of the correct orientation is presented on a monitor in front of them. Incorrect licking is punished by a “time-out”. Concurrent two-photon imaging of calcium signals in the cortex allows for the measurement of neural correlates, and a camera detects eye movement. Reproduced, with permission, from (A) [82] , (B) [84], (C) [92], (D) [90].

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