Bat Eyes Have Ultraviolet-Sensitive Cone Photoreceptors

Mammalian retinae have rod photoreceptors for night vision and cone photoreceptors for daylight and colour vision. For colour discrimination, most mammals possess two cone populations with two visual pigments (opsins) that have absorption maxima at short wavelengths (blue or ultraviolet light) and long wavelengths (green or red light). Microchiropteran bats, which use echolocation to navigate and forage in complete darkness, have long been considered to have pure rod retinae. Here we use opsin immunohistochemistry to show that two phyllostomid microbats, Glossophaga soricina and Carollia perspicillata, possess a significant population of cones and express two cone opsins, a shortwave-sensitive (S) opsin and a longwave-sensitive (L) opsin. A substantial population of cones expresses S opsin exclusively, whereas the other cones mostly coexpress L and S opsin. S opsin gene analysis suggests ultraviolet (UV, wavelengths <400 nm) sensitivity, and corneal electroretinogram recordings reveal an elevated sensitivity to UV light which is mediated by an S cone visual pigment. Therefore bats have retained the ancestral UV tuning of the S cone pigment. We conclude that bats have the prerequisite for daylight vision, dichromatic colour vision, and UV vision. For bats, the UV-sensitive cones may be advantageous for visual orientation at twilight, predator avoidance, and detection of UV-reflecting flowers for those that feed on nectar

High speed coding for velocity by archerfish retinal ganglion cells

<p>Abstract</p> <p>Background</p> <p>Archerfish show very short behavioural latencies in response to falling prey. This raises the question, which response parameters of retinal ganglion cells to moving stimuli are best suited for fast coding of stimulus speed and direction.</p> <p>Results</p> <p>We compared stimulus reconstruction quality based on the ganglion cell response parameters latency, first interspike interval, and rate. For stimulus reconstruction of moving stimuli using latency was superior to using the other stimulus parameters. This was true for absolute latency, with respect to stimulus onset, as well as for relative latency, with respect to population response onset. Iteratively increasing the number of cells used for reconstruction decreased the calculated error close to zero.</p> <p>Conclusions</p> <p>Latency is the fastest response parameter available to the brain. Therefore, latency coding is best suited for high speed coding of moving objects. The quantitative data of this study are in good accordance with previously published behavioural response latencies.</p

Synaptic inputs to identified color-coded amacrine and ganglion cells in the turtle retina

Previous studies have proposed models of the specific synaptic circuitry responsible for color processing in the turtle retina. To determine the accuracy of these models of the circuits underlying color opponency in the inner retina of the turtle (Pseudemys scripta), we have studied the physiology, morphology, and synaptic connectivity of identified amacrine and ganglion cells. These cells were first characterized electrophysiologically and were then stained with horseradish peroxidase. Postembedding electron immunocytochemistry for γ-aminobutyric acid (GABA) and glycine was used to reveal the neurochemical identity of their synaptic inputs. The red-ON/green, blue-OFF small-field ganglion cell, classified as G24, branched primarily in strata S1, S4, and S5 of the inner plexiform layer (IPL). Ganglion cell G24 showed a complex receptive field organized into a red-ON center surrounded by an inhibitory region, which, in turn, was surrounded by a second excitatory region. Only the center responses were color opponent. The red-OFF/green, blue-ON large-field, stellate amacrine cell, classified as A23b, stratified exclusively in stratum S2, near the S2/S3 border. The color-coded center was surrounded by a luminosity, red-sensitive surround. Synaptic input to G24 and A23b was dominated by amacrine cells (89% and 87%, respectively). G24 received significant input from amacrine cell profiles with GABA (13% of total) as well as glycine (11% of total) immunoreactivity, mostly in the proximal stratum S5 of the IPL (64% and 67% of the total GABA- and glycine- immunoreactive input, respectively). Bipolar cell synaptic input was also found predominantly in S4 and S5 (89%). In contrast, we found no glycine- immunoreactive input to A23b, and the density of the GABA-immunoreactive amacrine cell synaptic input revealed a central (15%) to peripheral (3%) gradient within the dendritic tree. The results of the present study support the previous models of the synaptic circuitry responsible for color-opponent signal processing in the inner retina of the turtle

Complexity and scaling properties of amacrine, ganglion, horizontal, and bipolar cells in the turtle retina

In the present studies we have evaluated the complexity and scaling properties of the morphology of retinal neurons using fractal dimension as a quantitative parameter. We examined a large number of cells from Pseudemys scripta and Mauremys caspica turtles that had been labeled using Golgi-impregnation techniques, intracellular injection of Lucifer Yellow followed by photooxidation, intracellular injection of rhodamine conjugated horseradish peroxidase, or intracellular injection of Lucifer Yellow or horseradish peroxidase alone. The fractal dimensions of two dimensional projections of the cells were calculated using a box counting method. Discriminant analysis revealed fractal dimension to be a significant classification parameter among several other parameters typically used for placing turtle retinal neurons in different cell classes. The fractal dimension of amacrine cells was significantly correlated with dendritic field diameters, while the fractal dimensions of ganglion cells did not vary with dendritic field span. There were no significant differences between the same cell types in two different turtle species, or between the same types of neurons in the same species after labeling with different techniques. The application of fractal dimension, as a quantitative measure of complexity and scaling properties and as a classification criterion of neuronal types, appears to be useful and may have wide applicability to other parts of the central nervous system.Peer reviewe