scholarly journals Retinal Synaptic Pathways Underlying the Response of the Rabbit Local Edge Detector

2010 ◽  
Vol 103 (5) ◽  
pp. 2757-2769 ◽  
Author(s):  
Thomas L. Russell ◽  
Frank S. Werblin
Keyword(s):  
Feedback Inhibition ◽  
Receptive Fields ◽  
Cell Voltage ◽  
Retinal Ganglion ◽  
Edge Detector ◽  
Temporal Properties ◽  
Field Sensitivity ◽  
Glycinergic Inhibition ◽  
Local Edge ◽  
Feedforward Inhibition

We studied the circuitry that underlies the behavior of the local edge detector (LED) retinal ganglion cell in rabbit by measuring the spatial and temporal properties of excitatory and inhibitory currents under whole cell voltage clamp. Previous work showed that LED excitation is suppressed by activity in the surround. However, the contributions of outer and inner retina to this characteristic and the neurotransmitters used are currently unknown. Blockage of retinal inhibitory pathways (GABAA, GABAC, and glycine) eliminated edge selectivity. Inverting gratings in the surround with 50-μm stripe sizes did not stimulate horizontal cells, but suppressed on and off excitation by roughly 60%, indicating inhibition of bipolar terminals (feedback inhibition). On pharmacologic blockage, we showed that feedback inhibition used both GABAA and GABAC receptors, but not glycine. Glycinergic inhibition suppressed GABAergic feedback inhibition in the center, enabling larger excitatory currents in response to luminance changes. Excitation, feedback inhibition, and direct (feedforward) inhibition responded to luminance-neutral flipping gratings of 20- to 50-μm widths, showing they are driven by independent subunits within their receptive fields, which confers sensitivity to borders between areas of texture and nontexture. Feedforward inhibition was glycinergic, its rise time was faster than decay time, and did not function to delay spiking at the onset of a stimulus. Both the on and off phases could be triggered by luminance shifts as short in duration as 33 ms and could be triggered during scenes that already produced a high baseline level of feedforward inhibition. Our results show how LED circuitry can use subreceptive field sensitivity to detect visual edges via the interaction between excitation and feedback inhibition and also respond to rapid luminance shifts within a rapidly changing scene by producing feedforward inhibition.

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.

Signals and noise in the elasmobranch electrosensory system

1999 ◽  
Vol 202 (10) ◽  
pp. 1349-1355 ◽  
Author(s):  
J.C. Montgomery ◽  
D. Bodznick
Keyword(s):  
Direct Current ◽  
Noise Suppression ◽  
Receptive Fields ◽  
Sensory System ◽  
Mate Recognition ◽  
Common Mode ◽  
Central Processing ◽  
Temporal Properties ◽  
Efferent Neurons ◽  
Bioelectric Fields

Analyzing signal and noise for any sensory system requires an appreciation of the biological and physical milieu of the animal. Behavioral studies show that elasmobranchs use their electrosensory systems extensively for prey detection, but also for mate recognition and possibly for navigation. These biologically important signals are detected against a background of self-generated bioelectric fields. Noise-suppression mechanisms can be recognized at a number of different levels: behavior, receptor anatomy and physiology, and at the early stages of sensory processing. The peripheral filters and receptor characteristics provide a detector with permissive temporal properties but restrictive spatial characteristics. Biologically important signals probably cover the range from direct current to 10 Hz, whereas the bandwidth of the receptors is more like 0.1-10 Hz. This degree of alternating current coupling overcomes significant noise problems while still allowing the animal to detect external direct current signals by its own movement. Self-generated bioelectric fields modulated by breathing movement have similar temporal characteristics to important external signals and produce very strong modulation of electrosensory afferents. This sensory reafference is essentially similar, or common-mode, across all afferent fibers. The principal electrosensory neurons (ascending efferent neurons; AENs) of the dorsal octavolateralis nucleus show a greatly reduced response to common-mode signals. This suppression is mediated by the balanced excitatory and inhibitory components of their spatial receptive fields. The receptive field characteristics of AENs determine the information extracted from external stimuli for further central processing.

Image Sharpness and Contrast Tuning in the Early Visual Pathway

2017 ◽  
Vol 27 (08) ◽  
pp. 1750045 ◽  
Author(s):  
Eduardo Sánchez ◽  
Rubén Ferreiroa ◽  
Adrián Arias ◽  
Luis M. Martínez
Keyword(s):  
Functional Organization ◽  
Ganglion Cells ◽  
Receptive Fields ◽  
Local Contrast ◽  
Retinal Ganglion ◽  
Image Sharpness ◽  
Image Processing Techniques ◽  
Thalamic Relay ◽  
Processing Techniques

The center–surround organization of the receptive fields (RFs) of retinal ganglion cells highlights the presence of local contrast in visual stimuli. As RF of thalamic relay cells follow the same basic functional organization, it is often assumed that they contribute very little to alter the retinal output. However, in many species, thalamic relay cells largely outnumber their retinal inputs, which diverge to contact simultaneously several units at thalamic level. This gain in cell population as well as retinothalamic convergence opens the door to question how information about contrast is transformed at the thalamic stage. Here, we address this question using a realistic dynamic model of the retinothalamic circuit. Our results show that different components of the thalamic RF might implement filters that are analogous to two types of well-known image processing techniques to preserve the quality of a higher resolution version of the image on its way to the primary visual cortex.

Amacrine-to-amacrine cell inhibition: Spatiotemporal properties of GABA and glycine pathways

2011 ◽  
Vol 28 (3) ◽  
pp. 193-204 ◽  
Author(s):  
XIN CHEN ◽  
HAIN ANN HSUEH ◽  
FRANK S. WERBLIN
Keyword(s):  
Amacrine Cell ◽  
Ganglion Cells ◽  
Amacrine Cells ◽  
Peak Level ◽  
Wide Field ◽  
Gabaergic Inhibition ◽  
Onset Latency ◽  
Temporal Properties ◽  
Glycinergic Inhibition ◽  
Cell Inhibition

AbstractWe measured the spatial and temporal properties of GABAergic and glycinergic inhibition to amacrine cells in the whole-mount rabbit retina. The amacrine cells were parsed into two morphological classes: narrow-field cells with processes spreading less than 200 μm and wide-field cells with processes extending more than 300 μm. The inhibition was also parsed into two types: sustained glycine and transient GABA. Narrow-field amacrine cells receive 1) very transient GABAergic inhibition with a fast onset latency of 140 ± 16 ms decaying to 30% of the peak level within 208 ± 27 ms elicited broadly over a lateral distance of up to 1500 μm and 2) sustained glycinergic inhibition with a medium onset latency of 286 ± 23 ms that was elicited over a spatial area often broader than the processes of the narrow-field amacrine cells. Wide-field amacrine cells received sustained glycinergic inhibition but no broad transient GABAergic inhibition. Surprisingly, neither of these amacrine cell classes received sustained local GABAergic inhibition, commonly found in an earlier study of ganglion cells.

Mathematical models for the spatial receptive-field organization of nonlagged X-cells in dorsal lateral geniculate nucleus of cat

2000 ◽  
Vol 17 (6) ◽  
pp. 871-885 ◽  
Author(s):  
G.T. EINEVOLL ◽  
P. HEGGELUND
Keyword(s):  
Receptive Field ◽  
Ganglion Cell ◽  
Lateral Geniculate Nucleus ◽  
Discrete Model ◽  
Receptive Fields ◽  
Dorsal Lateral Geniculate Nucleus ◽  
Retinal Ganglion ◽  
Lateral Geniculate ◽  
X Cells ◽  
Dorsal Lateral Geniculate

Spatial receptive fields of relay cells in dorsal lateral geniculate nucleus (dLGN) have commonly been modeled as a difference of two Gaussian functions. We present alternative models for dLGN cells which take known physiological couplings between retina and dLGN and within dLGN into account. The models include excitatory input from a single retinal ganglion cell and feedforward inhibition via intrageniculate interneurons. Mathematical formulas describing the receptive field and response to circular spot stimuli are found both for models with a finite and an infinite number of ganglion-cell inputs to dLGN neurons. The advantage of these models compared to the common difference-of-Gaussians model is that they, in addition to providing mathematical descriptions of the receptive fields of dLGN neurons, also make explicit contributions from the geniculate circuit. Moreover, the model parameters have direct physiological relevance and can be manipulated and measured experimentally. The discrete model is applied to recently published data (Ruksenas et al., 2000) on response versus spot-diameter curves for dLGN cells and for the retinal input to the cell (S-potentials). The models are found to account well for the results for the X-cells in these experiments. Moreover, predictions from the discrete model regarding receptive-field sizes of interneurons, the amount of center-surround antagonism for interneurons compared to relay cells, and distance between neighboring retinal ganglion cells providing input to interneurons, are all compatible with data available in the literature.

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