Encoding of environmental illumination by primate melanopsin neurons.

Light regulates physiology, mood, and behavior through signals sent to the brain by intrinsically photosensitive retinal ganglion cells (ipRGCs). How primate ipRGCs sense light is unclear, as they are rare and challenging to target for electrophysiological recording. We developed a method of acute identification within the live, ex vivo retina. Using it, we found that ipRGCs of the macaque monkey are highly specialized to encode irradiance (the overall intensity of illumination) by blurring spatial, temporal, and chromatic features of the visual scene. We describe mechanisms at the molecular, cellular, and population scales that support irradiance encoding across orders-of-magnitude changes in light intensity. These mechanisms are conserved quantitatively across the ~70 million years of evolution that separate macaques from mice.

Optimized Signal Flow through Photoreceptors Supports the High-Acuity Vision of Primates.

The fovea is a neural specialization that endows humans and other primates with the sharpest vision among mammals. This performance originates in the foveal cones, which are extremely narrow and long to form a high-resolution pixel array. Puzzlingly, this form is predicted to impede electrical conduction to an extent that appears incompatible with vision. We observe the opposite: signal flow through even the longest cones (0.4-mm axons) is essentially lossless. Unlike in most neurons, amplification and impulse generation by voltage-gated channels are dispensable. Rather, sparse channel activity preserves intracellular current, which flows as if unobstructed by organelles. Despite these optimizations, signaling would degrade if cones were lengthier. Because cellular packing requires that cone elongation accompanies foveal expansion, this degradation helps explain why the fovea is a constant, miniscule size despite multiplicative changes in eye size through evolution. These observations reveal how biophysical mechanisms tailor form-function relationships for primate behavioral performance.

Normalization of input patterns in an associative network.

Numerous brain structures have a cerebellum-like architecture in which inputs diverge onto a large number of granule cells that converge onto principal cells. Plasticity at granule cell-to-principal cell synapses is thought to allow these structures to associate spatially distributed patterns of granule cell activity with appropriate principal cell responses. Storing large sets of associations requires the patterns involved to be normalized, i.e., to have similar total amounts of granule cell activity. Using a general model of associative learning, we describe two ways in which granule cells can be configured to promote normalization. First, we show how heterogeneity in firing thresholds across granule cells can restrict pattern-to-pattern variation in total activity while also limiting spatial overlap between patterns. These effects combine to allow fast and flexible learning. Second, we show that the perceptron learning rule selectively silences those synapses that contribute most to pattern-to-pattern variation in the total input to a principal cell. This provides a simple functional interpretation for the experimental observation that many granule cell-to-Purkinje cell synapses in the cerebellum are silent. Since our model is quite general, these principles may apply to a wide range of associative circuits.