Biophysical network modeling of the dLGN circuit: Effects of cortical feedback on spatial response properties of relay cells

Abstract

Despite half-a-century of research since the seminal work of Hubel and Wiesel, the role of the dorsal lateral geniculate nucleus (dLGN) in shaping the visual signals is not properly understood. Placed on route from retina to primary visual cortex in the early visual pathway, a striking feature of the dLGN circuit is that both the relay cells (RCs) and interneurons (INs) not only receive feedforward input from retinal ganglion cells, but also a prominent feedback from cells in layer 6 of visual cortex. This feedback has been proposed to affect synchronicity and other temporal properties of the RC firing. It has also been seen to affect spatial properties such as the center-surround antagonism of thalamic receptive fields, i.e., the suppression of the response to very large stimuli compared to smaller, more optimal stimuli. Here we explore the spatial effects of cortical feedback on the RC response by means of a a comprehensive network model with biophysically detailed, single-compartment and multicompartment neuron models of RCs, INs and a population of orientation-selective layer 6 simple cells, consisting of pyramidal cells (PY). We have considered two different arrangements of synaptic feedback from the ON and OFF zones in the visual cortex to the dLGN: phase-reversed (‘push-pull’) and phase-matched (‘push-push’), as well as different spatial extents of the corticothalamic projection pattern. Our simulation results support that a phase-reversed arrangement provides a more effective way for cortical feedback to provide the increased center-surround antagonism seen in experiments both for flashing spots and, even more prominently, for patch gratings. This implies that ON-center RCs receive direct excitation from OFF-dominated cortical cells and indirect inhibitory feedback from ON-dominated cortical cells. The increased center-surround antagonism in the model is accompanied by spatial focusing, i.e., the maximum RC response occurs for smaller stimuli when feedback is present.

Firing-Rate Based Network Modeling of the dLGN Circuit: Effects of Cortical Feedback on Spatiotemporal Response Properties of Relay Cells

“…These results support that a phase-reversed arrangement of the cortical feedback, where the ON-ON feedback is inhibitory while the OFF-ON feedback is excitatory, as suggested by data from [74], is more effective to enhance the center-surround antagonism of relay cells as observed in experiments [16,17,22,23,39]. However, with this arrangement a reduction in response to the optimal diameter stimulus in the size tuning curves was observed in our model (Fig.…”

“…A main advantage of pyLGN is its computational and conceptual ease. The computation of visual responses corresponds to direct evaluation of two-dimensional or three-dimensional integrals in the case of static or dynamic (i.e., movie) stimuli, respectively, contrasting numerically extensive LGN network simulation based on spiking neurons [31,35,39,82,95] or models where each neuron is represented as individual firing-rate unit [36,37]. This computational simplicity of pyLGN allows, for example, for fast and comprehensive exploration of a wide range of candidate scenarios for the organization of the cortical feedback.…”

“…Here the inhibitory and excitatory ON-ON feedback connections are accompanied by excitatory and inhibitory OFF-ON connections, respectively, following a phasereversed arrangement [39]. For one this feedback organization accounts for the feedback-induced enhancement of center-surround antagonism of relay cells as observed in experiments [16,17,22,23,39]. Further, it seems well suited to dynamically modulate both the center-surround suppression and spatial resolution, for example, to adapt to changing light conditions [41].…”

“…The area-response curves in Fig. 9 are for static spot stimuli, but area-response curves are also commonly recorded for patch-grating stimuli [17,22,39,80]. In our formalism such response curves are readily obtained by use of the circular patchgrating stimulus function S in Eq.…”

“…Several studies have used computational modeling to investigate cortical feedback effects on spatial and/or temporal visual response properties of dLGN cells [31][32][33][34][35][36][37][38][39]. These have typically involved numerically extensive dLGN network simulations based on spiking neurons [31-33, 35, 39] or models where each neuron is represented as individual firing-rate unit [36,37].…”