scholarly journals Type-specific dendritic integration in mouse retinal ganglion cells

2019 ◽  
Author(s):  
Yanli Ran ◽  
Ziwei Huang ◽  
Tom Baden ◽  
Harald Baayen ◽  
Philipp Berens ◽  
...  
Keyword(s):  
Ion Channel ◽  
Retinal Ganglion Cells ◽  
Ganglion Cells ◽  
Receptive Fields ◽  
Temporal Correlation ◽  
Dendritic Morphology ◽  
Membrane Properties ◽  
Retinal Ganglion ◽  
Dendritic Integration ◽  
Dendritic Arbour

ABSTRACTNeural computation relies on the integration of synaptic inputs across a neuron’s dendritic arbour. However, the fundamental rules that govern dendritic integration are far from understood. In particular, it is still unclear how cell type-specific differences in dendritic integration arise from general features of neural morphology and membrane properties. Here, retinal ganglion cells (RGCs), which relay the visual system’s first computations to the brain, represent an exquisite model. They are functionally and morphologically diverse yet defined, and they allow studying dendritic integration in a functionally relevant context. Here, we show how four morphologically distinct types of mouse RGC with shared excitatory synaptic input (transient Off alpha, transient Off mini, sustained Off, and F-miniOff) exhibit distinct dendritic integration rules. Using two-photon imaging of dendritic calcium signals and biophysical modelling, we demonstrate that these RGC types strongly differ in their spatio-temporal dendritic integration: In transient Off alpha cells, dendritic receptive fields displayed little spatial overlap, indicative of a dendritic arbour that is partitioned in largely isolated regions. In contrast, dendritic receptive fields in the other three RGCs overlapped greatly and were offset to the soma, suggesting strong synchronization of dendritic signals likely due to backpropagation of somatic signals. Also temporal correlation of dendritic signals varied extensively among these types, with transient Off mini cells displaying the highest correlation across their dendritic arbour. Modelling suggests that morphology alone cannot explain these differences in dendritic integration, but instead specific combinations of dendritic morphology and ion channel densities are required. Together, our results reveal how neurons exhibit distinct dendritic integration profiles tuned towards their type-specific computations in their circuits, with the interplay between morphology and ion channel complement as a key contributor.

Mechanisms by Which Cell Geometry Controls Repetitive Impulse Firing in Retinal Ganglion Cells

1997 ◽  
Vol 78 (4) ◽  
pp. 1948-1964 ◽  
Author(s):  
J. F. Fohlmeister ◽  
R. F. Miller
Keyword(s):  
Ion Channels ◽  
Ion Channel ◽  
Retinal Ganglion Cells ◽  
Cell Morphology ◽  
Ganglion Cells ◽  
Dendritic Morphology ◽  
Retinal Ganglion ◽  
Cell Geometry ◽  
Impulse Frequency ◽  
Stimulation Of

Fohlmeister, J. F. and R. F. Miller. Mechanisms by which cell geometry controls repetitive impulse firing in retinal ganglion cells. J. Neurophysiol. 78: 1948–1964, 1997. Models for generating repetitive impulse activity were developed based on multicompartmental representations of ganglion cell morphology in the amphibian retina. Each model includes five nonlinear ion channels and one linear (leakage) channel. Compartmental distribution of ion channel type and density was designed to simulate whole cell recording experiments carried out in the intact retina-eyecup preparation. Correspondence between the model and physiology emphasized channel-specific details in the impulse waveform, based on phase plot analysis, frequency versus current (F/I) properties, and interspike trajectories for current injected into the soma, as well as the ability to conduct impulses in both orthodromic and antidromic directions. Two general types of model are developed, including equivalent cylinder representations and more realistic compartmentalizations of dendritic morphology. These multicompartmental models include representations for dendritic trees, soma, axon hillock, a thin axonal segment, and axon distal to thin segment. A large number of compartments (≤800) representing a single neuron were employed to ensure that maximum voltage differences between neighboring compartments during the steepest rates of change of membrane potential were acceptably small. Leakage conductance varied from 3 to 8 μS/cm2. The results establish that intercompartmental currents, due to inhomogeneous morphology, dominate membrane currents in the interspike intervals and thus play a major role in determining the impulse spacing and the information carried by impulse trains. Variations in input resistance are far less important than the degree to which ion channels are present in the dendritic compartments for the regulation of F/I properties. Cell geometry, including the thin axonal segment, places significant constraints on the location of ion channels required to support impulse initiation and propagation in both the ortho- and antidromic directions. The site of impulse initiation varies greatly and depends on the stimulus magnitude. Models that conform to physiological constraints also show irregular firing, particularly for near threshold stimulation of the soma, due to multiple sites of impulse initiation. Such behavior could represent an asset to the cells for conveying information under conditions of low contrast stimulation. Multiple spike initiation zones also can provide retinal ganglion cells with a variety of response characteristics, including spike doublets, depending on the level of cell activation. Increasing the diameter of the dendritic equivalent cylinder reduces the impulse frequency (F/I) response. Over a restricted range of ion channel densities in the dendritic tree, phase locking between dendritic membrane oscillations and somatic spiking can occur with dendritic stimulation, and mathematical chaos can be demonstrated when sufficiently thin dendritic processes are present. We conclude that cell morphology is the primary factor in determining firing patterns and the impulse frequency response of a given cell and that differences in channel density distribution across a population of cells plays, at most, a secondary role in this function. This conclusion applies to both synaptic activation and electrode stimulation of the soma.

scholarly journals Dependence of center radius on temporal frequency for the receptive fields of X retinal ganglion cells of cat.

1989 ◽  
Vol 94 (6) ◽  
pp. 987-995 ◽  
Author(s):  
J B Troy ◽  
C Enroth-Cugell
Keyword(s):  
Retinal Ganglion Cells ◽  
Ganglion Cells ◽  
Signal To Noise Ratio ◽  
Temporal Frequency ◽  
Receptive Fields ◽  
Retinal Ganglion ◽  
Spatial Expansion ◽  
Neuronal Membranes ◽  
Inductive Elements ◽  
X Cells

We examined the dependence of the center radius of X cells on temporal frequency and found that at temporal frequencies above 40 Hz the radius increases in a monotonic fashion, reaching a size approximately 30% larger at 70 Hz. This kind of spatial expansion has been predicted with cable models of receptive fields where inductive elements are included in modeling the neuronal membranes. Hence, the expansion of the center radius is clearly important for modeling X cell receptive fields. On the other hand, we feel that it might be of only minor functional significance, since the responsivity of X cells is attenuated at these high temporal frequencies and the signal-to-noise ratio is considerably worse than at low and midrange temporal frequencies.

Membrane Properties and Monosynaptic Retinal Excitation of Neurons in the Turtle Accessory Optic System

1997 ◽  
Vol 78 (2) ◽  
pp. 614-627 ◽  
Author(s):  
Naoki Kogo ◽  
Michael Ariel
Keyword(s):  
Receptive Field ◽  
Retinal Ganglion Cells ◽  
Ganglion Cells ◽  
Membrane Properties ◽  
Retinal Ganglion ◽  
Optic System ◽  
Accessory Optic System ◽  
Postsynaptic Potentials ◽  
Synaptic Inputs ◽  
Bon Cells

Kogo, Naoki and Michael Ariel. Membrane properties and monosynaptic retinal excitation of neurons in the turtle accessory optic system. J. Neurophysiol. 78: 614–627, 1997. Using an eye-attached isolated brain stem preparation of a turtle, Pseudemys scripta elegans, in conjunction with whole cell patch techniques, we recorded intracellular activity of accessory optic system neurons in the basal optic nucleus (BON). This technique offered long-lasting stable recordings of individual synaptic events. In the reduced preparation (most of the dorsal structures were removed), large spontaneous excitatory synaptic inputs [excitatory postsynaptic potentials (EPSPs)] were frequently recorded. Spontaneous inhibitory postsynaptic potentials were rarely observed except in few cases. Most EPSPs disappeared after injection of lidocaine into the retina. A few EPSPs of small size remained, suggesting that these EPSPs either were from intracranial sources or may have been miniature spontaneous synaptic potentials from retinal ganglion cell axon terminals. Population EPSPs were synchronously evoked by electrical stimulation of the contralateral optic nerve. Their constant onset latency and their ability to follow short-interval paired stimulation indicated that much of the population EPSP's response was monosynaptic. Visually evoked BON spikes and EPSP inputs to BON showed direction sensitivity when a moving pattern was projected onto the entire contralateral retina. With the use of smaller moving patterns, the receptive field of an individual BON cell was identified. A small spot of light, projected within the receptive field, guided the placement of a bipolar stimulation electrode to activate retinal ganglion cells that provided input to that BON cell. EPSPs evoked by this retinal microstimulation showed features of unitary EPSPs. Those EPSPs had distinct low current thresholds. Recruitment of other inputs was only evident when the stimulation level was increased substantially above threshold. The average size of evoked unitary EPSPs was 7.8 mV, confirming the large size of synaptic inputs of this system relative to nonsynaptic noise. EPSP shape was plotted (rise time vs. amplitude), with the use of either evoked unitary EPSPs or spontaneous EPSPs. Unlike samples of spontaneous EPSPs, data from many unitary EPSPs formed distinct clusters in these scatterplots, indicating that these EPSPs had a unique shape among the whole population of EPSPs. In most BON cells studied, hyperpolarization-activated channels caused a slow depolarization sag that reached a plateau within 0.5–1 s. This property suggests that BON cells may be more complicated than a simple site for convergence of direction-sensitive retinal ganglion cells to form a central retinal slip signal for control of oculomotor reflexes.

How Retinal Ganglion Cells Prevent Synaptic Noise From Reaching the Spike Output

2004 ◽  
Vol 92 (4) ◽  
pp. 2510-2519 ◽  
Author(s):  
Jonathan B. Demb ◽  
Peter Sterling ◽  
Michael A. Freed
Keyword(s):  
Retinal Ganglion Cells ◽  
Ganglion Cells ◽  
Spike Rate ◽  
Membrane Properties ◽  
Retinal Ganglion ◽  
Intracellular Recordings ◽  
Spike Response ◽  
Membrane Fluctuations ◽  
Fluctuation Amplitude ◽  
Synaptic Convergence

Synaptic vesicles are released stochastically, and therefore stimuli that increase a neuron's synaptic input might increase noise at its spike output. Indeed this appears true for neurons in primary visual cortex, where spike output variability increases with stimulus contrast. But in retinal ganglion cells, although intracellular recordings (with spikes blocked) showed that stronger stimuli increase membrane fluctuations, extracellular recordings showed that noise at the spike output is constant. Here we show that these seemingly paradoxical findings occur in the same cell and explain why. We made intracellular recordings from ganglion cells, in vitro, and presented periodic stimuli of various contrasts. For each stimulus cycle, we measured the response at the stimulus frequency (F1) for both membrane potential and spikes as well as the spike rate. The membrane and spike F1 response increased with contrast, but noise (SD) in the F1 responses and the spike rate was constant. We also measured membrane fluctuations (with spikes blocked) during the response depolarization and found that they did increase with contrast. However, increases in fluctuation amplitude were small relative to the depolarization (<10% at high contrast). A model based on estimated synaptic convergence, release rates, and membrane properties accounted for the relative magnitudes of fluctuations and depolarization. Furthermore, a cell's peak spike response preceded the peak depolarization, and therefore fluctuation amplitude peaked as the spike response declined. We conclude that two extremely general properties of a neuron, synaptic convergence and spike generation, combine to minimize the effects of membrane fluctuations on spiking.

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