Temporal Precision in the Visual Pathway through the Interplay of Excitation and Stimulus-Driven Suppression

Abstract

Visual neurons can respond with extremely precise temporal patterning to visual stimuli that change on much slower time scales. Here, we investigate how the precise timing of cat thalamic spike trains – which can have timing as precise as one millisecond – is related to the stimulus, in the context of both artificial noise and natural visual stimuli. Using a nonlinear modeling framework applied to extracellular data, we demonstrate that the precise timing of thalamic spike trains can be explained by the interplay between an excitatory and a delayed suppressive input that resembles inhibition, such that neuronal responses only occur in brief windows where excitation exceeds suppression. The resulting description of thalamic computation resembles earlier models of contrast adaptation, suggesting a more general role for mechanisms of contrast adaptation in visual processing. Thus, we describe a more complex computation underlying thalamic responses to artificial and natural stimuli, which has implications for understanding how visual information is represented in the early stages of visual processing.

Information Coding through Adaptive Gating of Synchronized Thalamic Bursting

“…Within thalamus, both visual and somatosensory thalamic relay nuclei receive inhibitory input from the reticular nucleus, but the visual thalamus also incorporates inhibitory input from interneurons. The reticular thalamus provides a strong level of inhibitory tone (Halassa et al, 2014; Pinault, 2004) while interneurons within visual thalamus are hypothesized to play a role in shaping feature selectivity in the pathway (Butts et al, 2011). The absence or presence of inhibitory interneurons could play an important role in determining both the temporal dynamics of the feature selectivity and the burst response properties of the excitatory thalamocortical neurons in these sensory pathways (Alitto et al, 2005; Denning and Reinagel, 2005; Lesica and Stanley, 2004).…”

Inferring the role of inhibition in auditory processing of complex natural stimuli

“…To address this limitation of linear STRF-based models, we use a new approach, the GNM, which explicitly models excitation and inhibition with separate linear STRFs that are combined nonlinearly to produce the neural response (Butts et al 2011). The GNM framework models excitation and inhibition as separate nonlinear elements, each composed of a linear STRF that specifies the stimulus tuning of the element (Fig.…”

“…⌬(filt stim), Change in filtered stimulus. sensitive measure for model performance than r 2 (Butts et al 2011) and is particularly useful at the high time resolutions used here (5 ms), which typically require more than five repeated trials to estimate accurately firing-rate based metrics such as r 2 . Using this measure allows us to report accurately model performance across the population of neurons recorded (n ϭ 94), which shows significant improvement in the GNM over the STRF-based model (Fig.…”

“…The generalized nonlinear model. The GNM framework is composed of nonlinear elements designed to simulate the effects of excitation and inhibition and also can be fit to data using maximum likelihood estimation (Butts et al 2011). It is a cascade model where the predicted firing rate r(t) is given by (Fig.…”

“…As a result, each choice of the excitatory and inhibitory STRFs has a LL associated with it, and they can thus be optimized through gradient ascent calculation as well (Butts et al 2011).…”