Abstract:The retina extracts visual features for transmission to the brain. Different types of bipolar cell split the photoreceptor input into parallel channels and provide the excitatory drive for downstream visual circuits. Anatomically, mouse bipolar cell types have been described down to the ultrastructural level, but a similarly deep understanding of their functional diversity is lacking. By imaging light-driven glutamate release from more than 11,000 bipolar cell axon terminals in the intact retina, we here show … Show more
“…This suggests that mechanisms responsible for the nonlinear behavior of the postsynaptic excitatory current are localized to the bipolar-ganglion cell synapses. Possible sources of such nonlinear behavior include presynaptic inhibition from amacrine cells, which can directly gate glutamate release from bipolar cell terminals (Eggers and Lukasiewicz, 2011; Euler et al, 2014; Franke et al, 2016; Schubert et al, 2008; Zaghloul et al, 2007), and synaptic depression at bipolar terminals caused by vesicle depletion (Jarsky et al, 2011; Markram et al, 1998; Ozuysal and Baccus, 2012). 10.7554/eLife.19460.006Figure 2.The divisive suppression (DivS) model of synaptic currents.( A ) Schematic of retinal circuitry.…”
Section: Resultsmentioning
confidence: 99%
“…Although synaptic depression could also explain fast transient responses and contrast adaptation (Ozuysal and Baccus, 2012), synaptic depression will generally result in excitation and suppression that have the same spatial profiles (Figure 4—figure supplement 1 and 2), whereas we show that excitation and suppression have distinct spatial profiles (Figure 4). Therefore, the divisive suppression in our model likely depends partly on presynaptic inhibition from amacrine cells, which can extend their suppressive influence laterally (Euler et al, 2014; Franke et al, 2016; Schubert et al, 2008). This was somewhat surprising, because earlier studies had shown that contrast adaptation persisted in the presence of inhibitory receptor antagonists, suggesting that adaptation depended primarily on mechanisms intrinsic to the bipolar cell (e.g., synaptic depression), independent of synaptic inhibition (Beaudoin et al, 2007; Brown and Masland, 2001; Rieke, 2001).…”
Section: Discussionmentioning
confidence: 97%
“…We devised a tractable model of excitatory currents that incorporates a nonlinear structure based on realistic circuit elements. In particular, we allowed for divisive suppression acting on a ganglion cell’s excitatory inputs to capture the computations implemented by presynaptic inhibition (Eggers and Lukasiewicz, 2011; Franke et al, 2016) and synaptic depression (Jarsky et al, 2011; Ozuysal and Baccus, 2012) at bipolar cell terminals. Ganglion cell firing, further shaped by spike generation mechanisms, could be predicted by the model with millisecond precision.…”
Visual processing depends on specific computations implemented by complex neural circuits. Here, we present a circuit-inspired model of retinal ganglion cell computation, targeted to explain their temporal dynamics and adaptation to contrast. To localize the sources of such processing, we used recordings at the levels of synaptic input and spiking output in the in vitro mouse retina. We found that an ON-Alpha ganglion cell's excitatory synaptic inputs were described by a divisive interaction between excitation and delayed suppression, which explained nonlinear processing that was already present in ganglion cell inputs. Ganglion cell output was further shaped by spike generation mechanisms. The full model accurately predicted spike responses with unprecedented millisecond precision, and accurately described contrast adaptation of the spike train. These results demonstrate how circuit and cell-intrinsic mechanisms interact for ganglion cell function and, more generally, illustrate the power of circuit-inspired modeling of sensory processing.DOI:
http://dx.doi.org/10.7554/eLife.19460.001
“…This suggests that mechanisms responsible for the nonlinear behavior of the postsynaptic excitatory current are localized to the bipolar-ganglion cell synapses. Possible sources of such nonlinear behavior include presynaptic inhibition from amacrine cells, which can directly gate glutamate release from bipolar cell terminals (Eggers and Lukasiewicz, 2011; Euler et al, 2014; Franke et al, 2016; Schubert et al, 2008; Zaghloul et al, 2007), and synaptic depression at bipolar terminals caused by vesicle depletion (Jarsky et al, 2011; Markram et al, 1998; Ozuysal and Baccus, 2012). 10.7554/eLife.19460.006Figure 2.The divisive suppression (DivS) model of synaptic currents.( A ) Schematic of retinal circuitry.…”
Section: Resultsmentioning
confidence: 99%
“…Although synaptic depression could also explain fast transient responses and contrast adaptation (Ozuysal and Baccus, 2012), synaptic depression will generally result in excitation and suppression that have the same spatial profiles (Figure 4—figure supplement 1 and 2), whereas we show that excitation and suppression have distinct spatial profiles (Figure 4). Therefore, the divisive suppression in our model likely depends partly on presynaptic inhibition from amacrine cells, which can extend their suppressive influence laterally (Euler et al, 2014; Franke et al, 2016; Schubert et al, 2008). This was somewhat surprising, because earlier studies had shown that contrast adaptation persisted in the presence of inhibitory receptor antagonists, suggesting that adaptation depended primarily on mechanisms intrinsic to the bipolar cell (e.g., synaptic depression), independent of synaptic inhibition (Beaudoin et al, 2007; Brown and Masland, 2001; Rieke, 2001).…”
Section: Discussionmentioning
confidence: 97%
“…We devised a tractable model of excitatory currents that incorporates a nonlinear structure based on realistic circuit elements. In particular, we allowed for divisive suppression acting on a ganglion cell’s excitatory inputs to capture the computations implemented by presynaptic inhibition (Eggers and Lukasiewicz, 2011; Franke et al, 2016) and synaptic depression (Jarsky et al, 2011; Ozuysal and Baccus, 2012) at bipolar cell terminals. Ganglion cell firing, further shaped by spike generation mechanisms, could be predicted by the model with millisecond precision.…”
Visual processing depends on specific computations implemented by complex neural circuits. Here, we present a circuit-inspired model of retinal ganglion cell computation, targeted to explain their temporal dynamics and adaptation to contrast. To localize the sources of such processing, we used recordings at the levels of synaptic input and spiking output in the in vitro mouse retina. We found that an ON-Alpha ganglion cell's excitatory synaptic inputs were described by a divisive interaction between excitation and delayed suppression, which explained nonlinear processing that was already present in ganglion cell inputs. Ganglion cell output was further shaped by spike generation mechanisms. The full model accurately predicted spike responses with unprecedented millisecond precision, and accurately described contrast adaptation of the spike train. These results demonstrate how circuit and cell-intrinsic mechanisms interact for ganglion cell function and, more generally, illustrate the power of circuit-inspired modeling of sensory processing.DOI:
http://dx.doi.org/10.7554/eLife.19460.001
“…1c). Indeed, this can be seen in direct recordings from bipolar cells [28,29,30], measurements of bipolar cell output [31,11] or in recordings of RGC excitatory input [32] (and see Fig. 2c,d).…”
Section: Introductionmentioning
confidence: 99%
“…First, nonlinear processing in the retina can contribute more to decorrelating retinal ganglion cell (RGC) responses to natural scenes than the RF surround [10] (see also [11,12]). Second, human fixational eye movements can remove spatial correlations in natural inputs before any neural processing takes place [13,14,15,16].…”
SummaryAlmost every neuron in the early visual system has some form of an antagonistic receptive field surround. But the function of the surround is poorly understood, especially under naturalistic stimulus conditions. Anatomical and functional characterization of the surround of retinal ganglion cells suggests that surround signals combine with the excitatory feedforward pathway upstream of an important synaptic nonlinearity between bipolar cells and postsynaptic ganglion cells. This circuit architecture suggests at least two unexplored consequences for receptive field structure. First, center and surround inputs should interact nonlinearly, even prior to the summation across visual space that forms the full receptive field center. Second, inputs to the surround may alter the sensitivity of the center to spatial contrast in a scene. We test these hypotheses using both synthetic and naturalistic visual stimuli, and find that the statistics of natural scenes promote these nonlinear interactions. This work shows that, unexpectedly, the surround modulates spatial contrast encoding based on visual context.
Inhibition mediated by horizontal and amacrine cells in the outer and inner retina, respectively, are fundamental components of visual processing. Here, our purpose was to determine how these different inhibitory processes affect glutamate release from ON bipolar cells when the retina is stimulated with full-field light of various intensities. Light-evoked membrane potential changes (ΔV ) were recorded directly from axon terminals of intact bipolar cells receiving mixed rod and cone inputs (Mbs) in slices of dark-adapted goldfish retina. Inner and outer retinal inhibition to Mbs was blocked with bath applied picrotoxin (PTX) and NBQX, respectively. Then, control and pharmacologically modified light responses were injected into axotomized Mb terminals as command potentials to induce voltage-gated Ca influx (Q ) and consequent glutamate release. Stimulus-evoked glutamate release was quantified by the increase in membrane capacitance (ΔC ). Increasing depolarization of Mb terminals upon removal of inner and outer retinal inhibition enhanced the ΔV /Q ratio equally at a given light intensity and inhibition did not alter the overall relation between Q and ΔC . However, relative to control, light responses recorded in the presence of PTX and PTX + NBQX increased ΔC unevenly across different stimulus intensities: at dim stimulus intensities predominantly the inner retinal GABAergic inhibition controlled release from Mbs, whereas the inner and outer retinal inhibition affected release equally in response to bright stimuli. Furthermore, our results suggest that non-linear relationship between Q and glutamate release can influence the efficacy of inner and outer retinal inhibitory pathways to mediate Mb output at different light intensities.
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