Ideal observer analysis of signal quality in retinal circuits

Abstract

The function of the retina is crucial, for it must encode visual signals so the brain can detect objects in the visual world. However, the biological mechanisms of the retina add noise to the visual signal and therefore reduce its quality and capacity to inform about the world. Because an organism's survival depends on its ability to unambiguously detect visual stimuli in the presence of noise, its retinal circuits must have evolved to maximize signal quality, suggesting that each retinal circuit has a specific functional role. Here we explain how an ideal observer can measure signal quality to determine the functional roles of retinal circuits. In a visual discrimination task the ideal observer can measure from a neural response the increment threshold, the number of distinguishable response levels, and the neural code, which are fundamental measures of signal quality relevant to behavior. It can compare the signal quality in stimulus and response to determine the optimal stimulus, and can measure the specific loss of signal quality by a neuron's receptive field for non-optimal stimuli. Taking into account noise correlations, the ideal observer can track the signal to noise ratio available from one stage to the next, allowing one to determine each stage's role in preserving signal quality. A comparison between the ideal performance of the photon flux absorbed from the stimulus and actual performance of a retinal ganglion cell shows that in daylight a ganglion cell and its presynaptic circuit loses a factor of ~10-fold in contrast sensitivity, suggesting specific signal-processing roles for synaptic connections and other neural circuit elements. The ideal observer is a powerful tool for characterizing signal processing in single neurons and arrays along a neural pathway.

Temporal resolution of single photon responses in primate rod photoreceptors and limits imposed by cellular noise

“…A discrimination task to probe sensitivity Detection and temporal sensitivity were quantified using a two-alternative forced-choice (2AFC) discrimination task [22][23][24]. Measured rod outer segment currents were used to discriminate flashes delivered at two possible times.…”

Trigger features and excitation in the retina

“…see [42]). The stimulus dimension for which SNR is greatest represents a good candidate for the trigger feature of the GC, and the number of discriminable levels in the response, obtained as the ratio of the dynamic range and the SNR, may provide a useful metric for comparing the relevance of different stimulus dimensions (reviewed in [43]). The simplest ideal observer method measures performance for a 2-alternative forced-choice discrimination task, in which a pair of stimuli that differ by a small amount in one or more dimensions are repeated, and the responses filtered with a correlation template ideal for the discrimination (Figure 3, top).…”

“…A comparison between different cell types of the number of discriminable levels for a stimulus dimension, for example, contrast or direction of motion, shows how much information about the stimulus is available to each cell type, and a comparison to the ideal model shows how much was lost. Moreover, by comparing the relevance of stimuli for neurons at different stages in a pathway, inferences can be made about the connectivity, and the signal processing roles of the circuit elements [43]. …”

“…Bottom, repeat over all trials, tally correct responses, and plot as a neurometric function. From Smith and Dhingra (2009).…”

The dependence of visual noise perception on background color and luminance

“…A photon shot noise is an unavoidable physical noise in any photon-capturing devices (e.g. photo diode, 28th Picture Coding Symposium, PCS2010, December 8-10, 2010, Nagoya, Japan 978-1-4244-7135-5/10/$26.00 ©2010 IEEE cone/rod photoreceptors in the retina [15]). The LabLumNoise is additive Gaussian noise on the luminance L* axis in CIELAB.…”