Mouse Cones Adapt Fast, Rods Slowly In Vivo

Abstract

PURPOSE. To study rod-and cone-driven adaptation dynamics separately, we used the silent substitution technique to selectively stimulate rods or cones in the Opn1lw LIAIS (LIAIS) mouse, in which the native M-cone pigment is replaced by a human L-cone pigment (L*). METHODS. ERG recordings were performed on anesthetized LIAIS mice. ERG stimuli were sinusoidally modulated. After 10 minutes of adaptation to 0.4 candela per square meter (cd/ m 2) ERGs were measured, followed by 11-minute adaptation to 8.8 cd/m 2 background and recordings directly after the luminance increase and every second minute. Finally, during adaptation to 0.4 cd/m 2 for 32 minutes, ERG responses were recorded directly after the change in background and every second minute. This protocol was repeated with rodisolating stimuli (8 Hz; 75% rod contrast), L*-cone-isolating stimuli (12 Hz; 55% cone contrast) and white light (8 Hz and 12 Hz; 100% Michelson contrast). RESULTS. At 8.8 cd/m 2 , responses directly displayed photopic response properties without further changes in either cone or white light responses. Rod-driven responses were very small. After the return to 0.4 cd/m 2 , both rod-driven and white light responses increased over a time course of about 30 minutes. Cone-driven responses were very small. Response phases changed directly after a change in background without further alterations. CONCLUSIONS. Rod-and cone-driven signal pathways display strongly different adaptation characteristics: adaptation of cone-driven responses to photopic conditions is very fast, whereas rod-driven responses change with a time course up to 30 minutes during scotopic conditions. Luminance responses are cone-driven at 8.8 cd/m 2 and rod-driven at 0.4 cd/m 2 .

Behavioral state tunes mouse vision to ethological features through pupil dilation

“…Our data indicates that a switch between the rod and cone system can also happen dynamically in behaving mice as a consequence of changes in pupil size across distinct behavioral states. Cone activation rates measured in mouse (Joachimsthaler & Kremers, 2019) and primate retina ( van Hateren & Lamb, 2006) support a fast switch from rod-to cone-driven visual responses for timescales below one second. As rod and cone photoreceptors differ with respect to light sensitivity, temporal resolution, and degree of non-linearity (discussed in Lamb, 2016), dynamically adjusting their relative activation will greatly in-fluence the sensory representation of the visual scene.…”

Unraveling the mystery of ocular retinoid turnover: Insights from albino mice and the role of STRA6

“…We also recorded the electrical responses in dark-adapted eyes and the responses in light-adapted eyes by electroretinography (ERG) ( 23 , 24 ) ( Fig. S5 ).…”

Encoding the Photoreceptors of the Human Eye

“…The hope is to use deep learning techniques to decode this correlation. This can be used to artificially generate the electrical signals from the photons and trigger the optic nerve for people who had normal vision earlier and still have the sensation intact in the optic nerves [20][21][22][23][24][25][26][27][28][29].…”

“…The simulation performed on normal human eyes show an a-wave (negative deflection) which is followed by a b-wave (positive deflection) [28]. Electroretinography performed on eyes adapted to dark will trigger the rod system whereas that performed on eyes adjusted to light will trigger the cone system [29]. Simulation of input light signals (electromagnetic radiation) and output electrical signal data using electroretinography with normal vision of several individuals needs to be performed [27].…”

“…Once we have millions of such records, this could be fed into a convolutional neural network that uses deep learning techniques to find the correlation between the electromagnetic light waves and electronic signals generated in response. This function helps adjust the weights and biases of the parameters to be able to analyze any new scenarios presented to the eye [20][21][22][23][24][25][26][27][28][29]. Figure 3 shows the graph for electroretinography.…”