Sparse identification of contrast gain control in the fruit fly photoreceptor and amacrine cell layer

Abstract

The fruit fly's natural visual environment is often characterized by light intensities ranging across several orders of magnitude and by rapidly varying contrast across space and time. Fruit fly photoreceptors robustly transduce and, in conjunction with amacrine cells, process visual scenes and provide the resulting signal to downstream targets. Here we model the first step of visual processing in the photoreceptor-amacrine cell layer. We propose a novel divisive normalization processor (DNP) for modeling the computation taking place in the photoreceptor-amacrine cell layer. The DNP explicitly models the photoreceptor feedforward and temporal feedback processing paths and the spatio-temporal feedback path of the amacrine cells. We then formally characterize the contrast gain control of the DNP and provide sparse identification algorithms that can efficiently identify each the feedforward and feedback DNP components. The algorithms presented here are the first demonstration of tractable and robust identification of the components of a divisive normalization processor. The sparse identification algorithms can be readily employed in experimental settings, and their effectiveness is demonstrated with several examples.

The Functional Logic of Odor Information Processing in the Drosophila Antennal Lobe

“…which functionally reminds us of the Divisive Normalization Processor proposed in [24]. Since the models of the Antennal Lobe operate on the circuit level described as a dynamical system, we refer to the model in Eq.…”

“…Since the models of the Antennal Lobe operate on the circuit level described as a dynamical system, we refer to the model in Eq. ( 1) as the differential DNP, and the model in [24] as the convolutional DNP (see Fig. Terminal can be described by Global Differential DNP equations:…”

“…We showed that the Antennal Lobe circuits can be algorithmically described by (differential) spatio-temporal Divisive Normalization Processors (DNPs) [24]. Specifically, the AL circuit is equivalent to 3 separate DNPs, each modeling the Pre-LN/Post-eLN/Post-iLN pathways, and extracting the concentration invariance and ON/OFF contrast boosting features in parallel and independently of each other pathway.…”

“…In steady-state, single-channel PN responses exhibit a significantly reduced dependency of the amplitude of the concentration waveform (see also Fig. 11 Addressing the challenges with a theoretical approach, we demonstrate that the AL circuit can be described by a functionally equivalent circuit with 3 parallel differential Divisive Normalization Processors (DNPs) [24], extracting the concentrationinvariance and ON/OFF contrast-boosting features independently of each other.…”

Accelerating with FlyBrainLab the discovery of the functional logic of the Drosophila brain in the connectomic and synaptomic era

“…The VisTrans Library accelerates the study of the contribution of photoreceptors towards the overall spatiotemporal processing of visual scenes. It also serves as an entry point for discovering circuit function in the visual system of the fruit fly ( Lazar et al, 2020b ).…”