The authors combined viral expression of the calcium indicator GCaMP6f with two-photon imaging to gauge the tuning properties of large populations of individual neurons in the primary visual cortex of awake mice. They used sinusoidal patterns with different orientations and spatial frequencies to measure stimulus tuning and light/dark small stimuli to map the cortical receptive fields and their dominant contrast polarity (ON-dominated: preference for lamps; OFF-dominated: preference for darks). When the authors compared the tuning similarity and receptive field overlap of pairs of neurons, they found a poor but significant positive correlation: as the receptive field overlap improved, the tuning similarity also improved (Fig. 1 em A /em ). In addition, they found that OFF-dominated neurons were more several than ON-dominated neurons (Fig. 1 em B /em ) and that ON receptive field subregions were more scattered in visual space than OFF receptive field subregions. Taken together with previous studies (Jin et al. 2008; Kremkow et al. 2016; Lee et al. 2016; Nauhaus et al. 2016; Yeh et al. 2009), these results demonstrate that the visual cortex of rodents, carnivores, and primates do not represent all mixtures of stimulus sizes equally and that dark stimuli dominate the cortical representation. Open in a separate window Fig. 1. Cortical biases in the combined representation of retinotopy with stimulus tuning and dark/light contrast polarity in mouse main visual cortex (Jimenez et al. 2018). em A /em : cartoon representing a poor but significant positive correlation between tuning similarity and receptive field overlap (the dotted ellipse represents data spread, and the solid series represents the info development). em B /em : cartoon representing the bias of cortical responses toward dark stimuli. Blue histogram represents the likelihood of selecting a cortical neuron dominated by the OFF pathway (responds more powerful to dark stimuli). Crimson histogram represents the likelihood of selecting a cortical neuron dominated by the ON pathway (responds more powerful to light stimuli). Dotted series symbolizes neurons with well balanced ON/OFF responses. The results of Jimenez et al. (2018) could also shed some light on the advancement of visible cortical maps for stimulus orientation in carnivores and primates and having less these maps in mice. Although the complete developmental mechanisms stay unknown, a fascinating possibility is normally that orientation maps emerge from the tiling of visible space by On / off ganglion cellular material in the retina (Paik and Ringach 2011; Soodak 1987; W?ssle et al. 1981). Relating to the model, the positioning of On / off retinal ganglion cellular material determines not merely the cortical retinotopy, but also the cortical choice for stimulus orientation. Closely spaced On / off retinal ganglion cellular material bias each cortical area toward a particular stimulus orientation. In the huge cat visible cortex, this bias qualified prospects to the advancement of orientation maps. In small mouse visible cortex, it qualified prospects to little clusters of cortical neurons with comparable orientation at confirmed retinotopic area. A prediction out of this model can be that stimulus tuning ought to be more similar among cortical neurons with overlapping receptive fields than those with distant receptive fields. The reasoning behind this prediction is that both stimulus tuning and receptive field geometry originate from the same mechanism: the ON and OFF receptive field positions inherited from the retina. The positive correlation between stimulus tuning and receptive field overlap that the authors demonstrate is certainly consistent with this prediction. However, providing support because of this model will demand testing additional predictions that even more directly eliminate alternative models. An essential check to the model is always to demonstrate that the business of On / off retinal ganglion cellular material may be used to predict the business of the cortical orientation map in the same pet. A main summary from Jimenez et al. (2018) can be that the principal visual cortex might not have to represent similarly all mixtures of retinotopy, orientation, and spatial rate of recurrence to extract visible information effectively. The authors give a useful analogy to describe this aspect. The photoreceptor array can feeling a limited group of wavelength mixtures at each spatial located area of the visible field, however the brain continues to be able to extract color information efficiently. Similarly, a biased set of stimulus-tuning combinations for orientation and spatial frequency in visual cortex can be also enough to extract shape information. The number of combined stimulus dimensions within a cortical map depends on many factors, including the size of the cortex, the size of the visual field, and the visual resolution of the eye. The cortex does not need to represent spatial frequencies that the eye cannot see or orientation differences that the eye cannot discriminate. Therefore, because visual acuity is more than one order of magnitude lower in mice than cats, mice need less cortical resources to process the visual picture. No matter brain size, all mammals with eyes need to have a systematic representation of stimulus location within a cortical retinotopic map. Nevertheless, the retinotopy gradient within this map (how fast retinotopy techniques with cortical range) varies across pets. For instance, in cats, a motion of 500 m within the visible cortical map just adjustments retinotopy by 25 % of a receptive field middle ( 0.3 in central vision). In contrast, the same movement in the mouse visual cortical map changes retinotopy by over a full receptive field center (Bonin et al. 2011), a displacement in visual space two orders of magnitude larger than in cats. Cats use 1 mm2 EPZ-6438 price of visual cortex to represent the same retinotopy, which allows accommodating multiple combinations of stimulus dimensions for the same location of visual space and even sorting the dimension combinations by eye input and light/dark polarity (Kremkow et al. 2016). In contrast, the cortical allocation is at least one order of magnitude smaller in the mouse. Since retinotopy changes so rapidly across mouse visual cortex, there is usually potentially much less space to support the multiple combos of stimulus measurements for every location of visible space. For that reason, the bias in the mixed representation of retinotopy and stimulus tuning that Jimenez et al. (2018) found may reflect either the figures of On / off retinal wiring or just a compromise to represent the most relevant stimulus combos in the offered cortical space (like the bias for central eyesight and OFF dominance in carnivores and primates). Whatever the reason why for the cortical biases are, the task of Jimenez et al. (2018) obviously indicates that the offered cortical space in the mouse will not represent all combos of retinotopy and stimulus tuning similarly. However, the results of the bias for visible function stay unclear. GRANTS We were supported by National Eyesight Institute Grants EY-027157 (to R. Mazade), EY-023190 (to C. M. Niell), and EY-05253 (to J. M. Alonso). DISCLOSURES No conflicts of curiosity, financial or elsewhere, are EPZ-6438 price declared by the authors. AUTHOR CONTRIBUTIONS R.M., C.M.N., and J.M.A. drafted manuscript; R.M., C.M.N., and J.M.A. edited and revised manuscript; R.M., C.M.N., and J.M.A. approved final version of manuscript. REFERENCES Bonin V, Histed MH, Yurgenson S, Reid RC. Local diversity and fine-scale organization of receptive fields in mouse visual cortex. J Neurosci 31: 18506C18521, 2011. doi:10.1523/JNEUROSCI.2974-11.2011. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Jimenez LO, Tring E, Trachtenberg JT, Ringach DL. Local tuning biases in mouse main visual cortex. J Neurophysiol. First published April 18, 2018. doi:10.1152/jn.00150.2018. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Jin JZ, Weng C, Yeh CI, Gordon JA, Ruthazer ES, Stryker MP, Swadlow HA, Alonso JM. On and off domains of geniculate afferents in cat main visual cortex. Nat Neurosci 11: EPZ-6438 price 88C94, 2008. doi:10.1038/nn2029. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Kremkow J, Jin J, Wang Y, Alonso JM. Principles underlying sensory map topography in main visual cortex. Nature 533: 52C57, 2016. doi:10.1038/nature17936. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Lee KS, Huang X, Fitzpatrick D. Topology of ON and OFF inputs in visual cortex enables an invariant columnar architecture. Nature 533: 90C94, 2016. doi:10.1038/nature17941. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Nauhaus I, Nielsen KJ, Callaway EM. Efficient receptive field tiling in primate V1. Neuron 91: 893C904, 2016. doi:10.1016/j.neuron.2016.07.015. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Niell CM, Stryker MP. Highly selective receptive fields in mouse visual cortex. J Neurosci 28: 7520C7536, 2008. doi:10.1523/JNEUROSCI.0623-08.2008. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Paik SB, Ringach DL. Retinal origin of orientation maps in visual cortex. Nat Neurosci 14: 919C925, 2011. doi:10.1038/nn.2824. [PMC free article] [PubMed] [CrossRef] [Google Scholar]Soodak RE. The retinal ganglion cell mosaic defines orientation columns in striate cortex. Proc Natl Acad Sci USA 84: 3936C3940, 1987. doi:10.1073/pnas.84.11.3936. [PMC free article] [PubMed] [CrossRef] [Google Scholar]W?ssle H, Boycott BB, Illing RB. Morphology and mosaic of on- EPZ-6438 price and off-beta cells in the cat retina and some functional considerations. Proc R Soc Lond B Biol Sci 212: 177C195, 1981. doi:10.1098/rspb.1981.0033. [PubMed] [CrossRef] [Google Scholar]Yeh CI, Xing D, Shapley RM. Black responses dominate macaque main visual cortex V1. J Neurosci 29: 11753C11760, 2009. doi:10.1523/JNEUROSCI.1991-09.2009. [PMC free article] [PubMed] [CrossRef] [Google Scholar]. Jimenez et al. (2018) in the demonstrates a weak but significant bias in the combined representation of retinotopy and stimulus tuning in mouse visual cortex as well as a cortical bias for dark stimuli similar to that found in larger brains (Jin et al. 2008; Kremkow et al. 2016; Lee et al. Rabbit Polyclonal to ETV6 2016; Yeh et al. 2009). The authors combined viral expression of the calcium indicator GCaMP6f with two-photon imaging to measure the tuning properties of large populations of individual neurons in the principal visible cortex of awake mice. They utilized sinusoidal patterns with different orientations and spatial frequencies to measure stimulus tuning and light/dark little stimuli to map the cortical receptive areas and their dominant comparison polarity (ON-dominated: choice for lighting; OFF-dominated: choice for darks). When the authors in comparison the tuning similarity and receptive field overlap of pairs of neurons, they discovered a fragile but significant positive correlation: as the receptive field overlap elevated, the tuning similarity also elevated (Fig. 1 em A /em ). Furthermore, they discovered that OFF-dominated neurons had been even more many than ON-dominated neurons (Fig. 1 em B /em ) and that ON receptive field subregions had been even more scattered in visible space than OFF receptive field subregions. Taken as well as previous research (Jin et al. 2008; Kremkow et al. 2016; Lee et al. 2016; Nauhaus et al. 2016; Yeh et al. 2009), these outcomes demonstrate that the visible cortex of rodents, carnivores, and primates usually do not represent all mixtures of stimulus sizes similarly and that dark stimuli dominate the cortical representation. Open up in another window Fig. 1. Cortical biases in the mixed representation of retinotopy with stimulus tuning and dark/light comparison polarity in mouse major visible cortex (Jimenez et al. EPZ-6438 price 2018). em A /em : cartoon representing a poor but significant positive correlation between tuning similarity and receptive field overlap (the dotted ellipse represents data pass on, and the solid range represents the info tendency). em B /em : cartoon representing the bias of cortical responses toward dark stimuli. Blue histogram represents the likelihood of locating a cortical neuron dominated by the OFF pathway (responds more powerful to dark stimuli). Crimson histogram represents the likelihood of locating a cortical neuron dominated by the ON pathway (responds more powerful to light stimuli). Dotted range signifies neurons with well balanced ON/OFF responses. The outcomes of Jimenez et al. (2018) could also shed some light on the advancement of visible cortical maps for stimulus orientation in carnivores and primates and having less these maps in mice. Although the precise developmental mechanisms remain unknown, an interesting possibility is that orientation maps emerge from the tiling of visual space by ON and OFF ganglion cells in the retina (Paik and Ringach 2011; Soodak 1987; W?ssle et al. 1981). According to this model, the position of On / off retinal ganglion cellular material determines not merely the cortical retinotopy, but also the cortical choice for stimulus orientation. Closely spaced On / off retinal ganglion cellular material bias each cortical area toward a particular stimulus orientation. In the huge cat visible cortex, this bias qualified prospects to the advancement of orientation maps. In small mouse visible cortex, it qualified prospects to little clusters of cortical neurons with comparable orientation at confirmed retinotopic area. A prediction out of this model can be that stimulus tuning should be more similar among cortical neurons with overlapping receptive fields than those with distant receptive fields. The reasoning behind this prediction is usually that both stimulus tuning and receptive field geometry originate from the same system: the On / off receptive field positions inherited from the retina. The positive correlation between stimulus tuning and receptive field overlap that the authors demonstrate is obviously in keeping with this prediction. Nevertheless, providing support because of this model will demand testing various other predictions that even more directly eliminate alternative models. An essential check to the model is always to demonstrate that the business of On / off retinal ganglion cellular material may be used to predict the business of the cortical orientation map in the same pet. A main bottom line from Jimenez et al. (2018) is certainly that the principal visual cortex might not have to represent similarly all combos of retinotopy, orientation, and spatial regularity.