Ionic Currents Underlying Difference in Light Response Between Type A and Type B Photoreceptors

Regional expression of neuropeptides in the retina of the terrestrial slug Limax valentianus (Gastropoda, Stylommatophora, Limacidae)

Gastropods use lens-bearing eyes to detect ambient light. The retina contains photoreceptors that directly project to the brain. Here we identified the neurotransmitters that the retinal cells use for projection to the brain in the terrestrial slug Limax. We identified 12 genes encoding neuropeptides as well as a novel vesicular glutamate transporter, a marker of glutamatergic neuron, expressed in the retinal cells. Spatial expression profiles of the neuropeptide genes were determined by in situ hybridization. WWamide/MIP1/Pedal peptide2 were co-expressed in the neurons of the accessory retina. In the main retina, prohormone-4 was expressed in the ventro-lateral region. Clionin was expressed in the ventro-medial region. Pedal peptide was expressed in the anterior region of the main retina and in the accessory retina. Enterin was expressed in many neurons, including the accessory retina, but not in the dorsal region. FxRIamide1 and 2 were co-expressed in the posterior region. Prohormone-4 variant was uniformly expressed in many neurons but scarcely in the accessory retina. MIP2 was widely expressed throughout the dorso-ventral axis in the posterio-lateral region of the main retina. Myo1 was expressed in many neurons of the main retina but predominantly in the dorsal region. These expression patterns were confirmed by immunohistochemistry with specific antibodies against the neuropeptides. Projections of these peptidergic retinal neurons were confirmed by immunostaining of the optic nerve. Our present study revealed regional differentiation of the retina with respect to the neurotransmitters that the retinal cells use. neuropeptides, retina, neurotransmitter, gastropod, Lehmannia This article is protected by copyright. All rights reserved.

Relative spike timing in stochastic oscillator networks of the Hermissenda eye

The role of relative spike timing on sensory coding and stochastic dynamics of small pulse-coupled oscillator networks is investigated physiologically and mathematically, based on the small biological eye network of the marine invertebrate Hermissenda. Without network interactions, the five inhibitory photoreceptors of the eye network exhibit quasi-regular rhythmic spiking; in contrast, within the active network, they display more irregular spiking but collective network rhythmicity. We investigate the source of this emergent network behavior first analyzing the role of relative input to spike-timing relationships in individual cells. We use a stochastic phase oscillator equation to model photoreceptor spike sequences in response to sequences of inhibitory current pulses. Although spike sequences can be complex and irregular in response to inputs, we show that spike timing is better predicted if relative timing of spikes to inputs is accounted for in the model. Further, we establish that greater noise levels in the model serve to destroy network phase-locked states that induce non-monotonic stimulus rate-coding, as predicted in Butson and Clark (J Neurophysiol 99:146-154, 2008a; J Neurophysiol 99:155-165, 2008b). Hence, rate-coding can function better in noisy spiking cells relative to non-noisy cells. We then study how relative input to spike-timing dynamics of single oscillators contribute to network-level dynamics. Relative timing interactions in the network sharpen the stimulus window that can trigger a spike, affecting stimulus encoding. Also, we derive analytical inter-spike interval distributions of cells in the model network, revealing that irregular Poisson-like spike emission and collective network rhythmicity are emergent properties of network dynamics, consistent with experimental observations. Our theoretical results generate experimental predictions about the nature of spike patterns in the Hermissenda eye.

Random Noise Paradoxically Improves Light-Intensity Encoding in Hermissenda Photoreceptor Network

Neurons are notoriously noisy devices. Although the traditional view posits that noise degrades system performance, recent evidence suggests that noise may instead enhance neural information processing under certain conditions. Here we report that random channel and synaptic noise improve the ability of a biologically realistic computational model of the Hermissenda eye to encode light intensity. The model was created in GENESIS and is based on a previous model used to examine effects of changes in type B photoreceptor excitability, synaptic strength, and network architecture. The network consists of two type A and three type B multicompartmental photoreceptors. Each compartment contains a population of Hodgkin–Huxley-type ion channels and each cell is stimulated via artificial light currents. We found that the addition of random channel and synaptic noise yielded a significant improvement in the accuracy of the network's encoding of light intensity across eight light levels spanning 3.5 log units ( P < 0.001, modified Levene test). The benefits of noise remained after controlling for several consequences of randomness in the model. Additionally, improvements were not confined to perithreshold stimulus intensities. Finally, the effects of noise are not present in individual neurons, but rather are an emergent property of the synaptically connected network that is independent of stochastic resonance. These results suggest that noise plays a constructive role in neural information processing, a concept that could have important implications for understanding neural information processing or designing neural interface devices.