Manipulating neural dynamics to tune motion detection

Abstract

Neurons integrate excitatory and inhibitory signals to produce their outputs, but the role of input timing in this integration remains poorly understood. Motion detection is a paradigmatic example of this integration, since theories of motion detection rely on different delays in visual signals. These delays allow circuits to compare scenes at different times to calculate the direction and speed of motion. It remains untested how response dynamics of individual cell types drive motion detection and velocity sensitivity. Here, we sped up or slowed down specific neuron types in Drosophila’s motion detection circuit by manipulating ion channel expression. Altering the dynamics of individual neurons upstream of motion detectors changed their integrating properties and increased their sensitivity to fast or slow visual motion, exposing distinct roles for dynamics in tuning directional signals. A circuit model constrained by data and anatomy reproduced the observed tuning changes. Together, these results reveal how excitatory and inhibitory dynamics jointly tune a canonical circuit computation.

Contrast normalization affects response time-course of visual interneurons

“…To characterize the response dynamics of visual interneurons in Drosophila, the following artificial stimuli were commonly employed in the past: a) gratings with defined spatial wavelength and contrast moving at various velocities [15,[21][22][23]; b) moving edges of defined polarity [12,24], i.e. either a dark edge on a bright background or the other way around; c) white noise stimuli consisting of statistically independent flickering pixels or bars [14,[25][26][27]; d) defined luminance pulses or steps placed in the center of the RF of the cell [23,28]. Importantly in the present context, these stimuli differ from each other with respect to the amount of contrast present in the surround.…”

“…We focused on the visual interneurons with band-pass filtering properties [14], that provide excitatory input onto direction-selective T4 and T5 cells [17], exhibit contrast normalization [19], and are often used as input signals of various motion detector model simulations [14,26]. Incorporating spatial contrast normalization into correlation-based models of motion vision has already been demonstrated to drastically improve the models' performance [19].…”