Eye smarter than scientists believed: neural computations in circuits of the retina

Abstract

We rely on our visual system to cope with the vast barrage of incoming light patterns and to extract features from the scene that are relevant to our well-being. The necessary reduction of visual information already begins in the eye. In this review, we summarize recent progress in understanding the computations performed in the vertebrate retina and how they are implemented by the neural circuitry. A new picture emerges from these findings that helps resolve a vexing paradox between the retina's structure and function. Whereas the conventional wisdom treats the eye as a simple prefilter for visual images, it now appears that the retina solves a diverse set of specific tasks and provides the results explicitly to downstream brain areas.

Figure 1. Retinal circuitry

A: Diversity of retinal cell types. For all five classes of retinal neurons – photoreceptors (P), horizontal cells (H), bipolar cells (B), amacrine cells (A), and ganglion cells (G) – a number of types can be identified according to morphological characteristics and dendritic stratification patterns. The image shows the major cell types of a typical mammalian retina. (Reprinted with permission from Masland, 2001.) B: Specificity of retinal wiring. Double immunostaining for calbindin (red) and calretinin (green) in a vertical section of mouse retina (Haverkamp and Wässle, 2000) visualizes some of the structure and complexity of the retinal network. The staining labels horizontal cells, certain amacrine cells in the inner nuclear layer (INL), and some ganglion cells in the ganglion cell layer (GCL). The interposed outer and inner plexiform layers (OPL and IPL) are the sites of massive and often very specific synaptic contacts between the various cell types. For example, the labeled amacrine cells and ganglion cells extend their dendrites into three distinct thin strata of the IPL, which underscores the specificity of retinal microcircuits. (Reprinted with permission from Haverkamp and Wässle, 2000; see also Wässle, 2004.) C: Schematic drawing of connections between the basic cell classes. The neurons in the retina are connected through chemical synapses that are either sign-preserving (excitatory, closed circles) or sign-inverting (inhibitory, open circles). In addition, one finds a considerable amount of electrical coupling between cells via gap junctions within all cell classes (not shown) and across some types of cells (marked by resistor symbol). The input into the network is incident light, which hyperpolarizes the photoreceptors. The connections from photoreceptors to bipolar cells are of either sign, producing both OFF-type and ON-type bipolars. Horizontal cells provide negative feedback and lateral inhibition to photoreceptors and bipolar cells. Bipolar cells are reciprocally connected to amacrine cells with chemical synapses and, for some types, through electrical gap junctions. Ganglion cells represent the output layer of the retina; their axons form the optic nerve. They collect excitation from bipolar cells and mostly inhibition from amacrine cells. In addition, ganglion cells and amacrine cells can be electrically coupled. This general connectivity sets the framework for any specific retinal microcircuit.

Figure 2. Computations performed by the retina and their underlying microcircuits

A: Detection of dim light flashes in the rod-to-rod bipolar pathway. Left: Rod bipolar cells pool over many rod photoreceptors, which show distinct responses to single-photon activation embedded in noise. Bipolar cell potentials are not swamped by the accumulated noise in all rods, but instead show distinct activations from single photons, as shown by the voltage traces from a simple model simulation. Right: The important elements of the corresponding retinal microcircuitry. Each photoreceptor output is sent through a band-pass temporal filter followed by a thresholding operation before summation by the rod bipolar cell (Field and Rieke, 2002). Notation for this and all circuit diagrams: triangle = neuron; rectangle = temporal filter function; oval = instantaneous rectifier; closed/open circle = sign-preserving/inverting synapse. B: Sensitivity to texture motion. Left: Y-type ganglion cells show activation when a fine grating shifts in either direction over the receptive field (circle), even though the average illumination remains constant. Right: The underlying microcircuit. Each shift of the grating excites some bipolar cells and inhibits others. The bipolar cells have biphasic dynamics (see impulse response in inset) and thus respond transiently. Only the depolarized bipolar cells communicate to the ganglion cell, because of rectification in synaptic transmission. Thus the ganglion cell fires transiently on every shift (Hochstein and Shapley, 1976). C: Detection of differential motion. Left: An object-motion-sensitive ganglion cell remains silent under global motion of the entire image, but fires when the image patch in its receptive field moves differently from the background. Right: The circuitry behind this computation is based on similar elements as the Y-cell (panel B). Rectification of bipolar cell signals in the receptive field center creates sensitivity to motion. Polyaxonal amacrine cells in the periphery are excited by the same motion-sensitive circuit and send inhibitory inputs to the center. If motion in the periphery is synchronous with that in the center, the excitatory transients will coincide with the inhibitory ones, and firing is suppressed (Ölveczky et al., 2003; Baccus et al., 2008). D: Detection of approaching motion. Left: A certain type of retinal ganglion cell responds strongly to the visual pattern of an approaching dark object, as indicated by the schematic spike train below, but only weakly to lateral object motion. Right: The circuit that generates this approach sensitivity is composed of excitation from OFF bipolar cells and inhibition from amacrine cells that are activated by ON bipolar cells, at least partly via gap junction coupling. Importantly, these inputs are organized in subfields whose signals are nonlinearly rectified before integration by the ganglion cell (Münch et al., 2009). E: Rapid encoding of spatial structures with spike latencies. Left: Specific retinal ganglion cells encode the structure of a new image by their spike latencies. Cells with receptive fields (circles) in a dark region fire early, those in a bright region fire late. Cells whose receptive fields contain both dark and bright produce intermediate latencies and thus encode the boundary in their synchronous firing. Right: The responses result from a circuit that combines synaptic inputs from both ON and OFF bipolar cells whose signals are individually rectified. The timing differences in the responses follow from a delay (Δt) in the ON pathway (Gollisch and Meister, 2008a). F: Switching circuit. Left: A control signal selectively gates one of two potential input signals. Right: In the retina, such a control signal is driven by certain wide-field amacrine cells (A₁), which are activated during rapid image shifts in the periphery. Their activation leads to a suppression of OFF bipolar signals and, through a putative local amacrine cell (A₂), to disinhibition of ON bipolar signals (Geffen et al., 2007).

Figure 3. Pattern adaptation based on synaptic depression at bipolar cell terminals

A: Circuit model for pattern selectivity at bipolar terminals (top) with corresponding ganglion cell receptive fields (bottom). A ganglion cell receives input from multiple bipolar cells. Here bipolar cells B₁ and B₂ have the same receptive field, namely the center square of the lattice below. Their terminals, however, receive presynaptic inhibition from two amacrine cells A₁ and A₂ at different neighboring locations. This interaction leads to different transmitter release rates at the two terminals and makes the terminals preferentially respond to different visual patterns. The B₁ terminal, for example, receives co-occurring inhibition from A₁ for horizontal stripes and therefore responds more strongly to vertical stripes, while the B₂ terminal responds more strongly to horizontal stripes. B: During stimulation with flickering vertical stripes, bipolar cell B₁ and amacrine cell A₁ are activated out of phase, so there is no presynaptic suppression of release. The synaptic terminal of B₁ is therefore highly active (bold arrow), and over time, synaptic depression reduces the strength of this connection (small circle). Cells B₂ and A₂, on the other hand, are activated in phase. Thus release at the terminal of B₂ is suppressed (thin arrow), vesicles can be replenished, and the connection recovers its strength (large circle). If one subsequently tests the ganglion cell’s receptive field using a mixture of vertical and horizontal stripes (Hosoya et al., 2005), the sensitivity to horizontal stripes, mediated by the strong B₂ terminal, will exceed that to vertical stripes. C: During stimulation with horizontal stripes, the reverse changes take place. After synaptic depression has taken its course, the ganglion cell becomes more sensitive to vertical stripes.

Figure 4. Linear readout of a neural computation

A sensory neural network such as the retina responds to a stimulus, here the movement of an object in the visual field, by producing spike patterns in the output neurons. The network is postulated to solve a computational task, here compensating for the phototransduction delay and extrapolating a motion trajectory to the current object location. To claim that the network has solved this computation, we stipulate that the computational result should be obtainable as a linear combination of the output neurons’ activities. In the present case, this readout is provided by binning the spike trains in short observation windows and weighting each neuron’s spike count, n_i(t), by the position vector of its receptive field center, x⃗_i. Summation of these weighted responses over all neurons yields successive representations of the current object position, $\overrightarrow{x}(t)={\displaystyle \sum _i}{\overrightarrow{x}}_i·n_i(t)/{\displaystyle \sum _i}{n}_i(t)$.