Population Coding of Self-Motion: Applying Bayesian Analysis to a Population of Visual Interneurons in the Fly

Abstract

Coding of sensory information often involves the activity of neuronal populations. We demonstrate how the accuracy of a population code depends on integration time, the size of the population, and noise correlation between the participating neurons. The population we study consists of 10 identified visual interneurons in the blowfly Calliphora vicina involved in optic flow processing. These neurons are assumed to encode the animal's head or body rotations around horizontal axes by means of graded potential changes. From electrophysiological experiments we obtain parameters for modeling the neurons' responses. From applying a Bayesian analysis on the modeled population response we draw three major conclusions. First, integration of neuronal activities over a time period of only 5 ms after response onset is sufficient to decode accurately the rotation axis. Second, noise correlation between neurons has only little impact on the population's performance. And third, although a population of only two neurons would be sufficient to encode any horizontal rotation axis, the population of 10 vertical system neurons is advantageous if the available integration time is short. For the fly, short integration times to decode neuronal responses are important when controlling rapid flight maneuvers.

Saccadic flight strategy facilitates collision avoidance: closed-loop performance of a cyberfly

“…Similar mechanisms have been investigated in the context of sideward peering in locusts and preying mantis (Kral et al 2000;Sobel 1990). In accordance with a previous study (Karmeier et al 2005), intersaccadic intervals of some ten milliseconds are sufficiently long for providing the controller with behaviourally relevant optic flow information.…”

Near-Optimal Decoding of Transient Stimuli from Coupled Neuronal Subpopulations

“…The rotational velocity was constant across simulations and set to 500°/s, falling well within the parameters of typical motion of the fly during flight (Egelhaaf et al, 2012). This value is also consistent with values considered in previous computational studies of the VS network (Karmeier et al, 2005;Cuntz et al, 2007;Weber et al, 2008;Elyada et al, 2009). Increasing or decreasing the rotational velocity to 250°/s or 750°/s did not affect the results quantitatively, nor change our general conclusions (data not shown).…”

“…Two studies are conceptually close to our work: Karmeier et al (2005) took a Bayesian approach to quantify the encoding efficiency of the axis of rotation in the VS population response. They also proposed time integrals of the VS membrane potentials as readout variables, and examined the impact of population size on encoding in the VS population.…”

“…They also proposed time integrals of the VS membrane potentials as readout variables, and examined the impact of population size on encoding in the VS population. We note several important differences: Karmeier et al (2005) did not investigate the effect of VS coupling, instead focusing on the effects of integration time and input correlations. Furthermore, they used a phenomenological model, in contrast to our biophysically plausible model.…”

Subcellular mapping of dendritic activity in optic flow processing neurons

“…Arranged in a row, VS cells are numbered from 1 to 10 from most distal to most proximal [68]. The center of their rotational receptive fields shifts accordingly across the mediolateral axis [70,71], which led to the hypothesis that they act as a set of matched filters for optic flows elicited by rotation of the animal around particular body axes [72,73]. For example, the left hemisphere VS 5 will respond best to an optic flow pattern elicited by a rightward roll, i.e., a rightward rotation around the longitudinal body axis.…”