A power law describes the magnitude of adaptation in neural populations of primary visual cortex

Contrast gain control is a reparameterization of a population response curve

Neurons in primary visual cortex (area V1) adapt in varying degrees to the average contrast of the environment, suggesting that the representation of visual stimuli may interact with the state of cortical gain control in complex ways. To investigate this possibility, we measured and analyzed the responses of neural populations in mouse V1 to visual stimuli as a function of contrast in different environments, each characterized by a unique distribution of contrast values. Our findings reveal that, for a fixed stimulus, the population response can be described by a vector function r(gec), where the gain ge is a decreasing function of the mean contrast of the environment. Thus, gain control can be viewed as a reparameterization of a population response curve, which is invariant across environments. Different stimuli are mapped to distinct curves, all originating from a common origin, corresponding to a zero-contrast response. Altogether, our findings provide a straightforward, geometric interpretation of contrast gain control at the population level and show that changes in gain are well matched among members of a population.NEW & NOTEWORTHY The authors study the responses of neural populations in mouse primary visual cortex as a function of stimulus contrast. Measurements are performed in different "environments," each characterized by a different distribution of contrast values. They find that responses across environments can be viewed as a reparameterization of a single contrast-response curve, offering a simple, geometric account of contrast gain control in neural populations.

Contrast gain control is a reparameterization of a population response curve

Neurons in primary visual cortex (area V1) adapt in different degrees to the average contrast of the environment, suggesting that the representation of visual stimuli may interact with the state of cortical gain control in complex ways. To investigate this possibility, we measured and analyzed the responses of neural populations to visual stimuli as a function of contrast in different environments, each characterized by a unique distribution of contrast. Our findings reveal that, for a given stimulus, the population response can be described by a vector functionr ( g e c ) , where the gaing e is a decreasing function of the mean contrast of the environment. Thus, gain control can be viewed as a reparameterization of a population response curve, which is invariant across environments. Different stimuli are mapped to distinct curves, all originating from a common origin, corresponding to a zero-contrast response. Altogether, our findings provide a straightforward, geometric interpretation of contrast gain control at the population level and show that changes in gain are well coordinated among members of a neural population.

Adaptation-induced sharpening of orientation tuning curves in the mouse visual cortex

OBJECTIVE

Orientation selectivity is an emergent property of visual neurons across species with columnar and noncolumnar organization of the visual cortex. The emergence of orientation selectivity is more established in columnar cortical areas than in noncolumnar ones. Thus, how does orientation selectivity emerge in noncolumnar cortical areas after an adaptation protocol? Adaptation refers to the constant presentation of a nonoptimal stimulus (adapter) to a neuron under observation for a specific time. Previously, it had been shown that adaptation has varying effects on the tuning properties of neurons, such as orientation, spatial frequency, motion and so on.

BASIC METHODS

We recorded the mouse primary visual neurons (V1) at different orientations in the control (preadaptation) condition. This was followed by adapting neurons uninterruptedly for 12 min and then recording the same neurons postadaptation. An orientation selectivity index (OSI) for neurons was computed to compare them pre- and post-adaptation.

MAIN RESULTS

We show that 12-min adaptation increases the OSI of visual neurons ( n  = 113), that is, sharpens their tuning. Moreover, the OSI postadaptation increases linearly as a function of the OSI preadaptation.

CONCLUSION

The increased OSI postadaptation may result from a specific dendritic neural mechanism, potentially facilitating the rapid learning of novel features.

On the contrast response function of adapted neural populations

The magnitude of neural responses in sensory cortex depends on the intensity of a stimulus and its probability of being observed within the environment. How these two variables combine to influence the overall response of cortical populations remains unknown. Here we show that, in primary visual cortex, the vector magnitude of the population response is described by a separable power law that factors the intensity of a stimulus and its probability. Moreover, the discriminability between two contrast levels in a cortical population is proportional to the logarithm of the contrast ratio.NEW & NOTEWORTHY The magnitude of neural responses in sensory cortex depends on the intensity of a stimulus and its probability of being observed within the environment. The authors show that, in primary visual cortex, the vector magnitude of the population response is described by a separable power law that factors the intensity of a stimulus and its probability.