We propose to study the precision of coding in the retina where correlated visual signals are processed before being passed to ganglion cells for transmission to the brain. Performance of retinal circuits is limited by noise because the visual signal has a large (10 log unit) dynamic range but is carried by discrete stochastic events such as vesicle release, channel opening, and spikes. Therefore the retina takes advantage of correlated features of the visual environment such as extended objects, velocity, or direction of motion to code these features with specific circuits, improving their signal/noise ratio. But exactly how retinal circuits accomplish this is unknown. One standard theory is that noise from synaptic release and voltage- gated channels is removed by integrating over an extended time. However, the presence of nonlinearities in retinal circuitry suggests that encoding is more complex. For example, the All amacrine cells and bipolar cells contain voltage-gated channels that may amplify and provide adaptation, and they also contain gap junctions that detect correlated signals and remove noise. The dendrites of many ganglion cells are active and may boost postsynaptic potentials nonlinearly to generate a reliable signal. We hypothesize that these neural elements are poised to specifically amplify fast spatially-correlated signals, creating a coincidence detector that imparts salience to visual signals. We propose to test this hypothesis by applying an ideal observer to the responses of real and model neurons. The ideal observer is a computer program that discriminates using the likelihood rule between the responses to a pair of stimuli to measure the precision with which a neuron signals e.g. motion or contrast. This analysis provides the number of gray levels, a fundamental measure of information capacity. We will record from live bipolar, amacrine, and ganglion cells, construct realistic computer models of these neurons and their circuits, and measure the precision of real neurons and model with the ideal observer. Tracking the precision of transient, sustained, and directional selective visual signals from one layer to the next, we will discover where in the visual pathway information is lost and preserved, and gain a better understanding of how information is coded. This work will help to understand how the eye functions, and this knowledge will help clinical researchers determine what has gone wrong in many types of eye disease such as night blindness and other retinal dystrophies.
| Borghuis, Bart G; Ratliff, Charles P; Smith, Robert G (2018) Impact of light-adaptive mechanisms on mammalian retinal visual encoding at high light levels. J Neurophysiol 119:1437-1449 |
| Howlett, Marcus H C; Smith, Robert G; Kamermans, Maarten (2017) A novel mechanism of cone photoreceptor adaptation. PLoS Biol 15:e2001210 |
| Ding, Huayu; Smith, Robert G; Poleg-Polsky, Alon et al. (2016) Species-specific wiring for direction selectivity in the mammalian retina. Nature 535:105-10 |
| Lipin, Mikhail Y; Taylor, W Rowland; Smith, Robert G (2015) Inhibitory input to the direction-selective ganglion cell is saturated at low contrast. J Neurophysiol 114:927-41 |
| Smith, Robert G; Delaney, Kerry R; Awatramani, Gautam B (2014) Post-receptor adaptation: lighting up the details. Curr Biol 24:R608-10 |
| Puthussery, Theresa; Venkataramani, Sowmya; Gayet-Primo, Jacqueline et al. (2013) NaV1.1 channels in axon initial segments of bipolar cells augment input to magnocellular visual pathways in the primate retina. J Neurosci 33:16045-59 |
| Abbas, Syed Y; Hamade, Khaldoun C; Yang, Ellen J et al. (2013) Directional summation in non-direction selective retinal ganglion cells. PLoS Comput Biol 9:e1002969 |
| Taylor, W R; Smith, R G (2012) The role of starburst amacrine cells in visual signal processing. Vis Neurosci 29:73-81 |
| Trenholm, Stuart; Johnson, Kyle; Li, Xiao et al. (2011) Parallel mechanisms encode direction in the retina. Neuron 71:683-94 |
| Taylor, W R; Smith, R G (2011) Trigger features and excitation in the retina. Curr Opin Neurobiol 21:672-8 |
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