Divisive suppression explains high-precision firing and contrast adaptation in retinal ganglion cells

Abstract

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

Diverse inhibitory and excitatory mechanisms shape temporal tuning in transient OFF α ganglion cells in the rabbit retina

“…; Enroth‐Cugell & Freeman, ; Benardete & Kaplan, ; Molnar & Werblin, ; Cui et al . ). Overall, the data are consistent with a time delay between excitation and presynaptic inhibition, which allows GABAergic feedback inhibition to suppress excitation at low temporal frequencies, and to become disinhibitory at higher frequencies and enhance the EPSC amplitude.…”

“…; Cui et al . ). These changes in gain vary across temporal frequency, since increases in contrast speed up responses and shift the temporal tuning of Y‐cells to higher frequencies (Shapley & Victor, , ; Enroth‐Cugell & Freeman, ; Benardete & Kaplan, ).…”

“…This conclusion appears to be at odds with a recent study proposing non‐linear (shunting/divisive) interactions between inhibition and excitation in bipolar cell terminals presynaptic to ON α cells in the mouse retina (Cui et al . ). It would be interesting to compare the tuning properties of rabbit ON and OFF α cells to determine whether there are species differences or pathway differences.…”

Derivatives and inverse of cascaded linear+nonlinear neural models

“…In the same vein, a number of alternative models also assume multiple L+NL stages and adaptive non-linearities [17,[50][51][52][53]57]. And they all are successfully fitted via gradient based methods to data.…”

“…And they all are successfully fitted via gradient based methods to data. While some of them provide a detailed account on how the parameters are fitted [50][51][52], others rely on automatic differentiation [17,53,57]. An interesting example related with the approach proposed here is the model considered in [107], where they explicitly provide not only the Jacobian wrt the parameters to fit the model, but also the Jacobian wrt the input to synthesize MAD stimuli.…”