Nonlinear computations shaping temporal processing of precortical vision

Abstract

Computations performed by the visual pathway are constructed by neural circuits distributed over multiple stages of processing, and thus it is challenging to determine how different stages contribute on the basis of recordings from single areas. In the current article, we address this problem in the lateral geniculate nucleus (LGN), using experiments combined with nonlinear modeling capable of isolating various circuit contributions. We recorded cat LGN neurons presented with temporally modulated spots of various sizes, which drove temporally precise LGN responses. We utilized simultaneously recorded S-potentials, corresponding to the primary retinal ganglion cell (RGC) input to each LGN cell, to distinguish the computations underlying temporal precision in the retina from those in the LGN. Nonlinear models with excitatory and delayed suppressive terms were sufficient to explain temporal precision in the LGN, and we found that models of the S-potentials were nearly identical, although with a lower threshold. To determine whether additional influences shaped the response at the level of the LGN, we extended this model to use the S-potential input in combination with stimulus-driven terms to predict the LGN response. We found that the S-potential input "explained away" the major excitatory and delayed suppressive terms responsible for temporal patterning of LGN spike trains but revealed additional contributions, largely PULL suppression, to the LGN response. Using this novel combination of recordings and modeling, we were thus able to dissect multiple circuit contributions to LGN temporal responses across retina and LGN, and set the foundation for targeted study of each stage.

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

“…Every 10 s the contrast level switched between high and low. In high contrast, the stimulus evoked spike responses that were precisely timed from trial to trial (Figure 1A, left ), consistent with previous work performed both in vitro and in vivo (Berry and Meister, 1998; Butts et al, 2016; Butts et al, 2007; Passaglia and Troy, 2004; Reinagel and Reid, 2000; Uzzell and Chichilnisky, 2004). Such precision was not clearly present at low contrast (Figure 1A, right ).

10.7554/eLife.19460.003Figure 1.Precision of ganglion cell spike trains arises at the level of synaptic inputs.( A ) Spike rasters of an ON-Alpha cell to 10 repeated presentations of a temporally modulated noise stimulus ( top ) at two contrast levels.

…”

“…The generation of temporal precision involves nonlinear mechanisms within the retina, which may include both spike-refractoriness within ganglion cells (Berry and Meister, 1998; Keat et al, 2001; Pillow et al, 2005) and the interplay of excitation and inhibition (Baccus, 2007; Butts et al, 2016; Butts et al, 2011). Such distinct mechanisms contributing to ganglion cell computation are difficult to distinguish using recordings of the spike outputs alone, which naturally reflect the total effects of all upstream mechanisms.…”

“…The same suppression mechanism also likely underlies high temporal precision of LGN responses, which can be captured by a model with delayed suppression (Butts et al, 2011). Indeed, recordings of both LGN neurons and their major ganglion cell inputs suggest that precision of LGN responses is inherited from the retina and enhanced across the retinogeniculate synapse (Butts et al, 2016; Carandini et al, 2007; Casti et al, 2008; Rathbun et al, 2010; Wang et al, 2010b). Therefore, our results demonstrate that the temporal precision in the early visual system likely originates from nonlinear processing in the inputs to retinal ganglion cells.…”

“…For example, the LN model does not capture the effects of contrast adaptation, which includes a reduced gain (i.e., filter amplitude) at high contrast (Kim and Rieke, 2001; Meister and Berry, 1999; Shapley and Victor, 1978). The LN model also does not predict firing at high temporal resolution (Berry and Meister, 1998; Butts et al, 2016, 2007; Keat et al, 2001; Passaglia and Troy, 2004; Uzzell and Chichilnisky, 2004), and yet precise firing likely represents an essential element of downstream visual processing (Bruno and Sakmann, 2006; Havenith et al, 2011; Kelly et al, 2014; Wang et al, 2010a). …”

“…Such expansion terms better predict the spike train, but they are difficult to interpret functionally and with respect to the underlying circuitry (Butts et al, 2011; McFarland et al, 2013). The second approach targets specific aspects of the response, such as spike-refractoriness (Berry and Meister, 1998; Keat et al, 2001; Paninski, 2004; Pillow et al, 2005), gain changes associated with contrast adaptation (Bonin et al, 2005; Mante et al, 2008; Meister and Berry, 1999; Shapley and Victor, 1978), the interplay of excitation and inhibition (Butts et al, 2016, 2011), and rectification of synaptic release, associated with nonlinear spatial processing (Freeman et al, 2015; Gollisch, 2013; Schwartz and Rieke, 2011). However, each of these models primarily focuses on one type of nonlinear computation and does not generalize to explain a range of response properties.…”