A generalized linear model of the impact of direct and indirect inputs to the lateral geniculate nucleus

Abstract

Relay neurons in the Lateral Geniculate Nucleus (LGN) receive direct visual input predominantly from a single retinal ganglion cell (RGC), in addition to indirect input from other sources including interneurons, thalamic reticular nucleus (TRN) and the visual cortex. To address the extent of influence of these indirect sources on the response properties of the LGN neurons, we fit a Generalized Linear Model (GLM) to the spike responses of cat LGN neurons driven by spatially homogeneous spots that were rapidly modulated by a pseudo-random luminance sequence. Several spot sizes were used to probe the spatial extent of the indirect visual effects. Our extracellular recordings captured both the LGN spikes and the incoming RGC input (S potentials), allowing us to divide the inputs to the GLM into two categories: the direct RGC input, and the indirect input to which we have access through the luminance of the visual stimulus. For spots no larger than the receptive field center, the effect of the indirect input is negligible, while for larger spots its effect can on average account for 5% of the variance of the data, and for as much as 25% in some cells. The polarity of the indirect visual influence is opposite to that of the linear receptive field of the neurons. We conclude that the indirect source of response modulation of the LGN relay neurons arises from inhibitory sources, compatible with thalamic interneurons or TRN.

Causal Role of Thalamic Interneurons in Brain State Transitions: A Study Using a Neural Mass Model Implementing Synaptic Kinetics

“…A study by Babadi et al (2010) suggests that around 33% of TCR cells in the LGN receive “locked” feed-forward inhibitory inputs from the IN; “locked” (“non-locked”) refers to the case when TCR and IN receive inputs from same (different) retinal ganglion cells (RGC). Furthermore, output of locked TCR cells bear a high degree of correlation with RGC inputs and is speculated to be a mechanism for increasing LGN response precision (Blitz and Regehr, 2005).…”

“…This is in spite of the IN constituting around 20–25% of the total number of cells in almost all thalamic nuclei processing sensory information in mammals; around 47% of the synaptic afferents of the the IN are from the information carrying spiking neurons of the retina (Sherman, 2004; Jones, 2007). Moreover, the critical role of the IN in the visual signal processing by the LGN and information transmission in the retino-geniculo-cortical pathway is now well established (Dublin and Cleland, 1977; Wang et al, 2007; Babadi et al, 2010; Saalmann and Kastner, 2011; Wang et al, 2011a,b; Pressler and Regehr, 2013; Bastos et al, 2014; Hirsch et al, 2015); also, their physiology and spiking characteristics are now understood fairly well (Pape and McCormick, 1995; Zhu et al, 1999a,b; Cox et al, 2003). Thus, it is surprising that the importance of the causality of IN on brain rhythms is underestimated in experimental research, perhaps due to the lack of appropriate technology (Zhu et al, 1999a,b) that prevented proper recordings of the IN population dynamics.…”

Effects of cortical feedback on the spatial properties of relay cells in the lateral geniculate nucleus

“…However, even in this stimulation paradigm that clearly engages significant retinal driving, recent studies using transcranial magnetic stimulation (TMS) inactivation have nevertheless shown changes to spot responses (de Labra et al 2007). Furthermore, when assessing the direct (i.e., retinal) and indirect (i.e., thalamus onward) inputs via generalized linear models, Babadi et al (2010) found that 5-25% of the inputs driven by larger spot diameter stimuli to cat LGN cells were potentially indirect. This reinforces the point that, even in stimuli that drive retinal afferents optimally, one cannot easily disentangle the modulatory mechanisms at the level of the visual thalamus.…”

Inhibitory circuits for visual processing in thalamus

“…[30*], (Figure 2a). The models suggest that excitatory stimuli that extend beyond the center into the surround recruit inhibition.…”

“…This process of down-sampling across the synapse reduces redundancy in information transmitted about slowly changing signals such as natural stimuli. In addition to inhibition [30*,39*,43] processes such as temporal summation [44-46] help increase the economy of the neural code.…”