Rava Azeredo da Silveira, Raphael Benichou, Damon A. Clark, Markus Meister, Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Harvard University [Cambridge], Laboratoire de Physique Statistique de l'ENS (LPS), Université Paris Diderot - Paris 7 (UPD7)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), École normale supérieure - Paris (ENS-PSL), Harvard University, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)
Adaptation is at the heart of sensation and nowhere is it more salient than in early visual processing. Light adaptation in photoreceptors is doubly dynamical: it depends upon the temporal structure of the input and it affects the temporal structure of the response. We introduce a non-linear dynamical adaptation model of photoreceptors. It is simple enough that it can be solved exactly and simulated with ease; analytical and numerical approaches combined provide both intuition on the behavior of dynamical adaptation and quantitative results to be compared with data. Yet the model is rich enough to capture intricate phenomenology. First, we show that it reproduces the known phenomenology of light response and short-term adaptation. Second, we present new recordings and demonstrate that the model reproduces cone response with great precision. Third, we derive a number of predictions on the response of photoreceptors to sophisticated stimuli such as periodic inputs, various forms of flickering inputs, and natural inputs. In particular, we demonstrate that photoreceptors undergo rapid adaptation of response gain and time scale, over ∼ 300 ms—i. e., over the time scale of the response itself—and we confirm this prediction with data. For natural inputs, this fast adaptation can modulate the response gain more than tenfold and is hence physiologically relevant., Author Summary Photoreceptors constitute the interface between the visual world and the cerebral world, as they convert light inputs into neural signals. This conversion is subject to continuous adaptation: response gain and time scale vary as a function of input history. This adaptation is ‘dynamical’ both because it depends upon the temporal structure of the stimulus and because it affects the kinetics of the response. Traditionally, theoretical studies of photoreceptors fall within one of two extreme approaches: either detailed modeling based upon the biophysics of phototransduction or functional modeling based upon phenomenological descriptions of photoreceptor response. While the former approach involves too many coupled equations and unknown parameters to allow for analytical treatments, building intuition, or predicting trends, instances of the latter approach, such as the simple linear-nonlinear model, fail to capture essential features of dynamical adaptation. Here, we develop understanding at an intermediate level. We define and discuss a phenomenological model which is simple enough to allow for full solutions and predictions, but embodies features of phototransduction well enough to capture a rich phenomenology. We demonstrate that our model reproduces data with high accuracy and can be used to make predictions on the response to sophisticated visual inputs such as natural stimuli.