Stephen A. Baccus

Fast and Slow Contrast Adaptation in Retinal Circuitry

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.

Segregation of object and background motion in the retina

An important task in vision is to detect objects moving within a stationary scene. During normal viewing this is complicated by the presence of eye movements that continually scan the image across the retina, even during fixation. To detect moving objects, the brain must distinguish local motion within the scene from the global retinal image drift due to fixational eye movements. We have found that this process begins in the retina: a subset of retinal ganglion cells responds to motion in the receptive field centre, but only if the wider surround moves with a different trajectory. This selectivity for differential motion is independent of direction, and can be explained by a model of retinal circuitry that invokes pooling over nonlinear interneurons. The suppression by global image motion is probably mediated by polyaxonal, wide-field amacrine cells with transient responses. We show how a population of ganglion cells selective for differential motion can rapidly flag moving objects, and even segregate multiple moving objects.

Architecture and Activity-Mediated Refinement of Axonal Projections from a Mosaic of Genetically Identified Retinal Ganglion Cells

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.

A Retinal Circuit That Computes Object Motion

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 observers head or eye movements. We investigated the circuit basis for this object motion sensitive (OMS) response by recording intracellularly from all classes of retinal interneurons while simultaneously recording the spiking output of many ganglion cells. Fast, transient bipolar cells respond linearly to motion in the receptive field center. The synaptic output from their terminals is rectified and then pooled by the OMS ganglion cell. A type of polyaxonal amacrine cell is driven by motion in the surround, again via pooling of rectified inputs, but from a different set of bipolar cell terminals. By direct intracellular current injection, we found that these polyaxonal amacrine cells selectively suppress the synaptic input of OMS ganglion cells. A quantitative model of these circuit elements and their interactions explains how an important visual computation is accomplished by retinal neurons and synapses.

Precise Neural Stimulation in the Retina Using Focused Ultrasound

Focused ultrasound is a promising noninvasive technology for neural stimulation. Here we use the isolated salamander retina to characterize the effect of ultrasound on an intact neural circuit and compared these effects with those of visual stimulation of the same retinal ganglion cells. Ultrasound stimuli at an acoustic frequency of 43 MHz and a focal spot diameter of 90 μm delivered from a piezoelectric transducer evoked stable responses with a temporal precision equal to strong visual responses but with shorter latency. By presenting ultrasound and visual stimulation together, we found that ultrasonic stimulation rapidly modulated visual sensitivity but did not change visual temporal filtering. By combining pharmacology with ultrasound stimulation, we found that ultrasound did not directly activate retinal ganglion cells but did in part activate interneurons beyond photoreceptors. These results suggest that, under conditions of strong localized stimulation, timing variability is largely influenced by cells beyond photoreceptors. We conclude that ultrasonic stimulation is an effective and spatiotemporally precise method to activate the retina. Because the retina is the most accessible part of the CNS in vivo, ultrasonic stimulation may have diagnostic potential to probe remaining retinal function in cases of photoreceptor degeneration, and therapeutic potential for use in a retinal prosthesis. In addition, because of its noninvasive properties and spatiotemporal resolution, ultrasound neurostimulation promises to be a useful tool to understand dynamic activity in pharmacologically defined neural pathways in the retina.

Linking the computational structure of variance adaptation to biophysical mechanisms.

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 model parameters 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.

Retinal Adaptation to Object Motion

Due to fixational eye movements, the image on the retina is always in motion, even when one views a stationary scene. When an object moves within the scene, the corresponding patch of retina experiences a different motion trajectory than the surrounding region. Certain retinal ganglion cells respond selectively to this condition, when the motion in the cells receptive field center is different from that in the surround. Here we show that this response is strongest at the very onset of differential motion, followed by gradual adaptation with a time course of several seconds. Different subregions of a ganglion cells receptive field can adapt independently. The circuitry responsible for differential motion adaptation lies in the inner retina. Several candidate mechanisms were tested, and the adaptation most likely results from synaptic depression at the synapse from bipolar to ganglion cell. Similar circuit mechanisms may act more generally to emphasize novel features of a visual stimulus.

Coordinated dynamic encoding in the retina using opposing forms of plasticity.

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 newly observed 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.

Retina versus Cortex: Contrast Adaptation in Parallel Visual Pathways

Human vision adapts to the contrast of patterns by changing its sensitivity, but the origins of this perceptual adaptation have been disputed. In this issue of Neuron, Solomon et al. show that contrast adaptation in the primate arises mostly in the retina for the magnocellular pathway and mostly in the cortex for the parvocellular pathway. It appears that adaptation arises most strongly at sites that pool over many inputs.

Image Processing for a High-Resolution Optoelectronic Retinal Prosthesis

In an effort to restore visual perception in retinal diseases such as age-related macular degeneration or retinitis pigmentosa, a design was recently presented for a high-resolution optoelectronic retinal prosthesis having thousands of electrodes. This system requires real-time image processing fast enough to convert a video stream of images into electrical stimulus patterns that can be properly interpreted by the brain. Here, we present image-processing and tracking algorithms for a subretinal implant designed to stimulate the second neuron in the visual pathway, bypassing the degenerated first synaptic layer. For this task, we have developed and implemented: 1) A tracking algorithm that determines the implants position in each frame. 2) Image cropping outside of the implant boundaries. 3) A geometrical transformation that distorts the image appropriate to the geometry of the fovea. 4) Spatio-temporal image filtering to reproduce the visual processing normally occurring in photoceptors and at the photoreceptor-bipolar cell synapse. 5) Conversion of the filtered visual information into a pattern of electrical current. Methods to accelerate real-time transformations include the exploitation of data redundancy in the time domain, and the use of precomputed lookup tables that are adjustable to retinal physiology and allow flexible control of stimulation parameters. A software implementation of these algorithms processes natural visual scenes with sufficient speed for real-time operation. This computationally efficient algorithm resembles, in some aspects, biological strategies of efficient coding in the retina and could provide a refresh rate higher than fifty frames per second on our system