The visual system adapts to the magnitude of intensity fluctuations, and this process begins in the retina. Following the switch from a low-contrast environment to one of high contrast, ganglion cell sensitivity declines in two distinct phases: a fast change occurs in <0.1 s, and a slow decrease over approximately 10 s. To examine where these modulations arise, we recorded intracellularly from every major cell type in the salamander retina. Certain bipolar and amacrine cells, and all ganglion cells, adapted to contrast. Generally, these neurons showed both fast and slow adaptation. Fast effects of a contrast increase included accelerated kinetics, decreased sensitivity, and a depolarization of the baseline membrane potential. Slow adaptation did not affect kinetics, but produced a gradual hyperpolarization. This hyperpolarization can account for slow adaptation in the spiking output of ganglion cells.

Retinal ganglion cells convey the visual image from the eye to the brain. They generally encode local differences in space and changes in time rather than the raw image intensity. This can be seen as a strategy of predictive coding, adapted through evolution to the average image statistics of the natural environment. Yet animals encounter many environments with visual statistics different from the average scene. Here we show that when this happens, the retina adjusts its processing dynamically. The spatio-temporal receptive fields of retinal ganglion cells change after a few seconds in a new environment. The changes are adaptive, in that the new receptive field improves predictive coding under the new image statistics. We show that a network model with plastic synapses can account for the large variety of observed adaptations.

Figure S1. Left: Vertical projection of the confocal image of the amacrine cell in Fig. 4c, superimposed on its receptive field as measured by reverse correlation (see Methods). Top Right: Vertical projection of the region of the soma, showing dendritic tuft. Bottom Right: Horizontal projection. Poly-axonal branches arborized in a narrow layer, distinct from the dendritic tuft. INL, inner nuclear layer; IPL, inner plexiform layer; GCL, ganglion cell layer.

Our understanding of how mammalian sensory circuits are organized and develop has long been hindered by the lack of genetic markers of neurons with discrete functions. Here, we report a transgenic mouse selectively expressing GFP in a complete mosaic of transient OFF-alpha retinal ganglion cells (tOFF-alphaRGCs). This enabled us to relate the mosaic spacing, dendritic anatomy, and electrophysiology of these RGCs to their complete map of projections in the brain. We find that tOFF-alphaRGCs project exclusively to the superior colliculus (SC) and dorsal lateral geniculate nucleus and are restricted to a specific laminar depth within each of these targets. The axons of tOFF-alphaRGC are also organized into columns in the SC. Both laminar and columnar specificity develop through axon refinement. Disruption of cholinergic retinal waves prevents the emergence of columnar- but not laminar-specific tOFF-alphaRGC connections. Our findings reveal that in a genetically identified sensory map, spontaneous activity promotes synaptic specificity by segregating axons arising from RGCs of the same subtype.

It has been demonstrated that electrical stimulation of the retina can produce visual percepts in blind patients suffering from macular degeneration and retinitis pigmentosa. However, current retinal implants provide very low resolution (just a few electrodes), whereas at least several thousand pixels would be required for functional restoration of sight. This paper presents the design of an optoelectronic retinal prosthetic system with a stimulating pixel density of up to 2500 pix mm(-2) (corresponding geometrically to a maximum visual acuity of 20/80). Requirements on proximity of neural cells to the stimulation electrodes are described as a function of the desired resolution. Two basic geometries of sub-retinal implants providing required proximity are presented: perforated membranes and protruding electrode arrays. To provide for natural eye scanning of the scene, rather than scanning with a head-mounted camera, the system operates similar to 'virtual reality' devices. An image from a video camera is projected by a goggle-mounted collimated infrared LED-LCD display onto the retina, activating an array of powered photodiodes in the retinal implant. The goggles are transparent to visible light, thus allowing for the simultaneous use of remaining natural vision along with prosthetic stimulation. Optical delivery of visual information to the implant allows for real-time image processing adjustable to retinal architecture, as well as flexible control of image processing algorithms and stimulation parameters.

Certain ganglion cells in the retina respond sensitively to differential motion between the receptive field center and surround, as produced by an object moving over the background, but are strongly suppressed by global image motion, as produced by the observer's head or eye movements. We investigated the circuit basis for this object motion sensitive (

Summary In multiple sensory systems, adaptation to the variance of a sensory input changes the sensitivity, kinetics and average response over timescales ranging from < 100 ms to tens of seconds. Here we present a simple biophysically relevant model of retinal contrast adaptation that accurately captures both the membrane potential response and all adaptive properties. The adaptive component of this model is a first-order kinetic process of the type used to describe ion channel gating and synaptic transmission. From the model, we conclude that all adaptive dynamics can be accounted for by depletion of a signaling mechanism, and that variance adaptation can be explained as adaptation to the mean of a rectified signal. The parameters of the model show strong similarity to known properties of bipolar cell synaptic vesicle pools. Diverse types of adaptive properties that implement theoretical principles of efficient coding can be generated by a single type of molecule or synapse with just a few microscopic states.

The range of natural inputs encoded by a neuron often exceeds its dynamic range. To overcome this limitation, neural populations divide their inputs among different cell classes, as with rod and cone photoreceptors, and adapt by shifting their dynamic range. We report that the dynamic behavior of retinal ganglion cells in salamanders, mice, and rabbits is divided into two opposing forms of short-term plasticity in different cell classes. One population of cells exhibited sensitization—a persistent elevated sensitivity following a strong stimulus. This novel dynamic behavior compensates for the information loss caused by the known process of adaptation occurring in a separate cell population. The two populations divide the dynamic range of inputs, with sensitizing cells encoding weak signals, and adapting cells encoding strong signals. In the two populations, the linear, threshold and adaptive properties are linked to preserve responsiveness when stimulus statistics change, with one population maintaining the ability to respond when the other fails.