C-terminal phosphorylation regulates the kinetics of a subset of melanopsin-mediated behaviors in mice.

Intrinsically photosensitive retinal ganglion cells (ipRGCs) express the photopigment melanopsin and mediate several non-image-forming visual functions, including circadian photoentrainment and the pupillary light reflex (PLR). ipRGCs act as autonomous photoreceptors via the intrinsic melanopsin-based phototransduction pathway and as a relay for rod/cone input via synaptically driven responses. Under low light intensities, where only synaptically driven rod/cone input activates ipRGCs, the duration of the ipRGC response will be determined by the termination kinetics of the rod/cone circuits. Little is known, however, about the termination kinetics of the intrinsic melanopsin-based phototransduction pathway and its contribution to several melanopsin-mediated behaviors. Here, we show that C-terminal phosphorylation of melanopsin determines the recovery kinetics of the intrinsic melanopsin-based photoresponse in ipRGCs, the duration of the PLR, and the speed of reentrainment. In contrast, circadian phase alignment and direct effects of light on activity (masking) are not influenced by C-terminal phosphorylation of melanopsin. Electrophysiological measurements demonstrate that expression of a virally encoded melanopsin lacking all C-terminal phosphorylation sites (C terminus phosphonull) leads to a prolonged intrinsic light response. In addition, mice expressing the C terminus phosphonull in ipRGCs reentrain faster to a delayed light/dark cycle compared with mice expressing virally encoded WT melanopsin; however, the phase angle of entrainment and masking were indistinguishable. Importantly, a sustained PLR in the phosphonull animals is only observed at brighter light intensities that activate melanopsin phototransduction, but not at dimmer light intensities that activate only the rod/cone pathway. Taken together, our results highlight how the kinetics of the melanopsin photoresponse differentially regulate distinct light-mediated behaviors.

Identification of Retinal Ganglion Cell Firing Patterns Using Clustering Analysis Supplied with Failure Diagnosis.

An important goal in visual neuroscience is to understand how neuronal population coding in vertebrate retina mediates the broad range of visual functions. Microelectrode arrays interface on isolated retina registers a collective measure of the spiking dynamics of retinal ganglion cells (RGCs) by probing them simultaneously and in large numbers. The recorded data stream is then processed to identify spike trains of individual RGCs by efficient and scalable spike detection and sorting routines. Most spike sorting software packages, available either commercially or as freeware, combine automated steps with judgment calls by the investigator to verify the quality of sorted spikes. This work focused on sorting spikes of RGCs into clusters using an integrated analytical platform for the data recorded during visual stimulation of wild-type mice retinas with whole field stimuli. After spike train detection, we projected each spike onto two feature spaces: a parametric space and a principal components space. We then applied clustering algorithms to sort spikes into separate clusters. To eliminate the need for human intervention, the initial clustering results were submitted to diagnostic tests that evaluated the results to detect the sources of failure in cluster assignment. This failure diagnosis formed a decision logic for diagnosable electrodes to enhance the clustering quality iteratively through rerunning the clustering algorithms. The new clustering results showed that the spike sorting accuracy was improved. Subsequently, the number of active RGCs during each whole field stimulation was found, and the light responsiveness of each RGC was identified. Our approach led to error-resilient spike sorting in both feature extraction methods; however, using parametric features led to less erroneous spike sorting compared to principal components, particularly for low signal-to-noise ratios. As our approach is reliable for retinal signal processing in response to simple visual stimuli, it could be applied to the evaluation of disrupted physiological signaling in retinal neurodegenerative diseases.

Dynamic Characteristics of a New Three-Dimensional Linear Homeomorphic Saccade Model.

A linear homeomorphic eye movement model that produces 3D saccadic eye movements consistent with anatomical and physiological evidence is introduced in this second part of a two-paper sequence. Central to the model is the implementation of a time-optimal neural control strategy involving six linear muscle models that faithfully represent the dynamic characteristics of 3D saccades. The muscle is modeled as a parallel combination of viscosity [Formula: see text] and series elasticity [Formula: see text], connected to the parallel combination of active-state tension generator [Formula: see text], viscosity element [Formula: see text], and length tension elastic element [Formula: see text]. The neural input for each muscle is separately maintained while the effective pulling direction is modulated by its respective pulley. The results demonstrate that a time-optimal, 2D commutative neural controller, together with the pulley system, actively functions to implement Listing's law during both static and dynamic simulations and provide an excellent match with the experimental data. The parameters and neural input to the muscles are estimated using a time domain system identification technique from saccade data, with an excellent match between the model estimates and the data. A total of 20 horizontal, 5 vertical and 62 oblique saccades are analyzed.

Static Characteristics of a New Three-Dimensional Linear Homeomorphic Saccade Model.

A linear homeomorphic saccade model that produces 3D saccadic eye movements consistent with physiological and anatomical evidence is introduced. Central to the model is the implementation of a time-optimal controller with six linear muscles and pulleys that represent the saccade oculomotor plant. Each muscle is modeled as a parallel combination of viscosity [Formula: see text] and series elasticity [Formula: see text] connected to the parallel combination of active-state tension generator [Formula: see text], viscosity element [Formula: see text], and length tension elastic element [Formula: see text]. Additionally, passive tissues involving the eyeball include a viscosity element [Formula: see text], elastic element [Formula: see text], and moment of inertia [Formula: see text]. The neural input for each muscle is separately maintained, whereas the effective pulling direction is modulated by its respective mid-orbital constraint from the pulleys. Initial parameter values for the oculomotor plant are based on anatomical and physiological evidence. The oculomotor plant uses a time-optimal, 2D commutative neural controller, together with the pulley system that actively functions to implement Listing's law during both static and dynamic conditions. In a companion paper, the dynamic characteristics of the saccade model is analyzed using a time domain system identification technique to estimate the final parameter values and neural inputs from saccade data. An excellent match between the model estimates and the data is observed, whereby a total of 20 horizontal, 5 vertical, and 64 oblique saccades are analyzed.

Robust spike sorting of retinal ganglion cells tuned to spot stimuli.

We propose an automatic spike sorting approach for the data recorded from a microelectrode array during visual stimulation of wild type retinas with tiled spot stimuli. The approach first detects individual spikes per electrode by their signature local minima. With the mixture probability distribution of the local minima estimated afterwards, it applies a minimum-squared-error clustering algorithm to sort the spikes into different clusters. A template waveform for each cluster per electrode is defined, and a number of reliability tests are performed on it and its corresponding spikes. Finally, a divisive hierarchical clustering algorithm is used to deal with the correlated templates per cluster type across all the electrodes. According to the measures of performance of the spike sorting approach, it is robust even in the cases of recordings with low signal-to-noise ratio.

A neuron-based time-optimal controller of horizontal saccadic eye movements.

A neural network model of biophysical neurons in the midbrain for controlling oculomotor muscles during horizontal human saccades is presented. Neural circuitry that includes omnipause neuron, premotor excitatory and inhibitory burst neurons, long lead burst neuron, tonic neuron, interneuron, abducens nucleus and oculomotor nucleus is developed to investigate saccade dynamics. The final motoneuronal signals drive a time-optimal controller that stimulates a linear homeomorphic model of the oculomotor plant. To our knowledge, this is the first report on modeling the neural circuits at both premotor and motor stages of neural activity in saccadic systems.

A physiological neural controller of a muscle fiber oculomotor plant in horizontal monkey saccades.

A neural network model of biophysical neurons in the midbrain is presented to drive a muscle fiber oculomotor plant during horizontal monkey saccades. Neural circuitry, including omnipause neuron, premotor excitatory and inhibitory burst neurons, long lead burst neuron, tonic neuron, interneuron, abducens nucleus, and oculomotor nucleus, is developed to examine saccade dynamics. The time-optimal control strategy by realization of agonist and antagonist controller models is investigated. In consequence, each agonist muscle fiber is stimulated by an agonist neuron, while an antagonist muscle fiber is unstimulated by a pause and step from the antagonist neuron. It is concluded that the neural network is constrained by a minimum duration of the agonist pulse and that the most dominant factor in determining the saccade magnitude is the number of active neurons for the small saccades. For the large saccades, however, the duration of agonist burst firing significantly affects the control of saccades. The proposed saccadic circuitry establishes a complete model of saccade generation since it not only includes the neural circuits at both the premotor and motor stages of the saccade generator, but also uses a time-optimal controller to yield the desired saccade magnitude.