Impulse Encoding Across the Dendritic Morphologies of Retinal Ganglion Cells

Abstract

Nerve impulse entrainment and other excitation and passive phenomena are analyzed for a morphologically diverse and exhaustive data set (n = 57) of realistic (3-dimensional computer traced) soma-dendritic tree structures of ganglion cells in the tiger salamander (Ambystoma tigrinum) retina. The neurons, including axon and an anatomically specialized thin axonal segment that is observed in every ganglion cell, were supplied with five voltage- or ligand-gated ion channels (plus leakage), which were distributed in accordance with those found in a recent study that employed an equivalent dendritic cylinder. A wide variety of impulse-entrainment responses was observed, including regular low-frequency firing, impulse doublets, and more complex patterns involving impulse propagation failures (or aborted spikes) within the encoder region, all of which have been observed experimentally. The impulse-frequency response curves of the cells fell into three groups called FAST, MEDIUM, and SLOW in approximate proportion as seen experimentally. In addition to these, a new group was found among the traced cells that exhibited an impulse-frequency response twice that of the FAST category. The total amount of soma-dendritic surface area exhibited by a given cell is decisive in determining its electrophysiological classification. On the other hand, we found only a weak correlation between the electrophysiological group and the morphological classification of a given cell, which is based on the complexity of dendritic branching and the physical reach or "receptive field" area of the cell. Dendritic morphology determines discharge patterns to dendritic (synaptic) stimulation. Orthodromic impulses can be initiated on the axon hillock, the thin axonal segment, the soma, or even the proximal axon beyond the thin segment, depending on stimulus magnitude, soma-dendritic membrane area, channel distribution, and state within the repetitive impulse cycle. Although a sufficiently high dendritic Na-channel density can lead to dendritic impulse initiation, this does not occur with our "standard" channel densities and is not seen experimentally. Even so, impulses initiated elsewhere do invade all except very thin dendritic processes. Impulse-encoding irregularities increase when channel conductances are reduced in the encoder region, and the F/I properties of the cells are a strong function of the calcium- and Ca-activated K-channel densities. Use of equivalent dendritic cylinders requires more soma-dendritic surface area than real dendritic trees, and the source of the discrepancy is discussed.

Ionic mechanisms underlying tonic and phasic firing behaviors in retinal ganglion cells

“…Simulation results shown in Figure 1 illustrate that the modified model can reproduce the general repetitive spiking activity similar to that observed in previous experiments and model results, 5,[24][25][26] when the 2 slow variables s 1 and s 2 are not included. The larger the stimulus intensity, more spikes the neuron would elicit.…”

Retinal ganglion cells in the eastern newt Notophthalmus viridescens: Topography, morphology, and diversity

“…Potential solutions to this problem include more adequate data scaling, data dimensionality reduction (e.g., factor transformation), and variable weighting prior to clustering (Gordon, 1999 Together with other factors, dendrite branching profusion determines the area of cell surface available for synapse formation . The pattern of dendrite course and arborization is related to many aspects of cell physiology, e.g., electrotonic properties (Rall, 1962), local synaptic circuits (Euler, 2002;Amirikian, 2005), and impulse entrainment (Sheasby and Fohlmeister, 1999). It is noteworthy that in the present case, the final selection of classification parameters was based not on their a priori functional significance but on the objective quality of the respective partitionings.…”

Effects of Noise on the Spike Timing Precision of Retinal Ganglion Cells

“…The model was based on a previous multicompartment model in salamander (Fohlmeister and Miller 1997b;Sheasby and Fohlmeister 1999) and single-compartment model in cat (Benison et al 2001) but was matched to our own physiological data. With the model thus calibrated, we determined the effect of synaptic and channel noise on spike timing during sustained spiking.…”