Abstract:Retinal ganglion cells (RGCs) display differences in their morphology and intrinsic electrophysiology. The goal of this study is to characterize the ionic currents that explain the behavior of ON and OFF RGCs and to explore if all morphological types of RGCs exhibit the phenomena described in electrophysiological data. We extend our previous single compartment cell models of ON and OFF RGCs to more biophysically realistic multicompartment cell models and investigate the effect of cell morphology on intrinsic e… Show more
“…However, our simulation results suggest that the dendritic integration properties of tOff alpha and tOff 388 mini RGCs could not be explained by dendritic morphology alone but require dendritic ion channels in 389 agreement with earlier simulation studies (Maturana et al, 2014). One possible reason might be that 390 for most RGCs, action potentials generated in the soma can back propagate to the dendritic arbour 391 (Velte and Masland, 1999), which needs dendritic ion channels to enable the efficient backpropagation 392 (van Rossum et al, 2003;Velte and Masland, 1999).…”
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.
“…However, our simulation results suggest that the dendritic integration properties of tOff alpha and tOff 388 mini RGCs could not be explained by dendritic morphology alone but require dendritic ion channels in 389 agreement with earlier simulation studies (Maturana et al, 2014). One possible reason might be that 390 for most RGCs, action potentials generated in the soma can back propagate to the dendritic arbour 391 (Velte and Masland, 1999), which needs dendritic ion channels to enable the efficient backpropagation 392 (van Rossum et al, 2003;Velte and Masland, 1999).…”
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.
“…Nonetheless, this multi-compartments model here does not represent all RGCs on account of different types and morphologies of RGCs in retina. Previous studies have shown the effect of RGC morphology on its electrophysiological responses [30]. It is worth further studying that how RGC morphology influences optimization of noise parameters.…”
Section: Discussionmentioning
confidence: 99%
“…Each compartment was modeled as a 10-μm-long cylinder except longer dendritic branch which was 15-μm-long [29]. A schematic diagram of the cylinder model of RGC and the diameter and length of each segment [29, 30] are illustrated in Fig. 1.Fig.…”
Section: Methodsmentioning
confidence: 99%
“…In this paper, sodium persistent, hyperpolarization-activated and T-type low voltage activated (LVA) calcium conductances are added to this RGC model. The ion currents, , is calculated as follows [30] where is the maximum conductance corresponding to each ionic current, their parameters were given in Table 1. , , , , and are the reversal potential of ion channels and their parameters were given in Table 2.…”
BackgroundEpiretinal prosthesis is one device for the treatment of blindness, which target retinal ganglion cells (RGCs) by electrodes on retinal surface. The stimulating current of epiretinal prosthesis is an important factor that influences the safety threshold and visual perception. Stochastic resonance (SR) can be used to enhance the detection and transmission of subthreshold stimuli in neurons. Here, it was assumed that SR was a potential way to improve the performance of epiretinal prosthesis. The effect of noises on the response of RGCs to electrical stimulation and the energy of stimulating current was studied based on a RGC model.MethodsThe RGC was modeled as a multi-compartment model consisting of dendrites and its branches, soma and axon. To evoke SR, a subthreshold signal, a series of bipolar rectangular pulse sequences, plus stochastic biphasic pulse sequences as noises, were used as a stimulus to the model. The SR-type behavior in the model was characterized by a “power norm” measure. To decrease energy consumption of the stimulation waveform, the stochastic biphasic pulse sequences were only added to the cathode and anode phase of the subthreshold pulse and the noise parameters were optimized by using a genetic algorithm (GA).ResultsWhen certain intensity of noise is added to the subthreshold signal, RGC model can fire. With the noise’s RMS amplitudes increased, more spikes were elicited and the curve of power norm presents the inverted U-like graph. The larger pulse width of stochastic biphasic pulse sequences resulted in higher power norm. The energy consumption and charges of the single bipolar rectangular pulse without noise in threshold level are 468.18 pJ, 15.30 nC, and after adding optimized parameters’s noise to the subthreshold signal, they became 314.8174 pJ, 11.9281 nC and were reduced by 32.8 and 22.0%, respectively.ConclusionsThe SR exists in the RGC model and can enhance the representation of RGC model to the subthreshold signal. Adding the stochastic biphasic pulse sequences to the cathode and anode phase of the subthreshold signal helps to reduce stimulation threshold, energy consumption and charge of RGC stimulation. These may be helpful for improving the performance of epiretinal prosthesis.
“…These models have led to a quantitative understanding of many dynamical phenomena in the RGCs, including spike frequency adaptation [2], rebound activities [3,4], burst firing [4], sub-threshold activities [4], action potential (AP) initiation [5], dendritic processing [6], as well as the effect of extracellular stimuli [7], temperature [8] and cell morphology [9]. However, existing ionic models of RGCs have been largely limited to identification of individual RGC types without considering the functional significance of regional membrane channel distributions/kinetics and complex morphology.…”
Retinal ganglion cells (RGCs) demonstrate a large range of variation in their ionic channel properties and morphologies. These cell-specific properties are responsible for the unique way they process synaptic inputs. A cell-specific modeling approach allows us to examine the functional significance of regional membrane channel expression and cell morphology. ON and OFF RGC models based on accurate biophysics and realistic representation of morphologies were used to study the contribution of different ion channel properties and spatial structure of neurons to RGC electrical activity. Using this approach, morphologically-complex retinal neurons such as amacrine cells or RGCs can be modelled and their interactions and processing can be better understood.
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