Rudy Behnia, Ph.D.
Animals face complex environments teeming with sensory stimuli. Light, odor, taste, sound and touch need to be properly encoded in the brain to allow them to understand their natural surrounding and adapt their behavior to survive and thrive. Light in particular is an essential cue for many diurnal animals: it is sensed in the eye by photoreceptors; highly specialized neurons that detect photons. How do neuronal circuits in the brain interpret these photoreceptor signals to extract features of the visual scene, such as object, shape and color? For a given visual feature, our goal is to describe both the underlying mathematic operations ( i.e. algorithms) that govern this transformation, as well as the neural circuits that implement them.
In addition, we are interested in understanding how the processing properties of visual neurons change with the statistics of the scene as well as with the behavioral state of the animal, in order to optimize visual encoding in different conditions. We also aim to understand how, in turn, visual circuits accommodate for these stimulus and state dependent changes to allow for a faithful, or useful, reconstruction of the visual scene.
Lastly, my lab is increasingly interested in understanding how animals learn and make decisions in the context of a multi-sensory world and what role sensory integration plays in these processes.
We use a variety of complementary techniques: in vivo single cell patch-clamp recordings, two-photon activity-imaging, optogenetic, as well as behavioral paradigms. We collaborated extensively with theorists at the Zuckerman Institute to build models of sensory processing that guide our experiments. We work with fruit flies, not only because well known advantages of this model system such as genetic tractability, which helps us test our circuit models, but also because of the increasing availability of connectomics data in this animal, which we use to ground our computational models.
Behnia R and Desplan C. Visual circuits in flies: Beginning to see the whole picture. Curr Op Neurobiol. 34, 125-132 (2015).
Behnia R, Clark DA, Carter AG, Clandinin TR and Desplan C. Processing properties of Drosophila ON and OFF pathway for motion detection. Nature. 512, 427-30 (2014).
Seong HJ, Behnia R and Carter AG. The impact of subthreshold membrane potential on synaptic responses at individual spines in the basal dendrites of layer 5 pyramidal neurons. J Neurophysiol. 111, 1960-72 (2014).
Johnston RJ Jr, Otake Y, Sood P, Vogt N, Behnia R, Vasiliauskas D, McDonald E, Xie B, Koenig S, Wolf R, Cook T, Gebelein B, Kussell E, Nakagoshi H and Desplan C. Interlocked feedforward loops control cell-type-specific Rhodopsin expression in the Drosophila eye. Cell. 145, 956-68 (2011).
Johansen JP, Hamanaka H, Monfils MH, Behnia R, Deisseroth K, Blair HT and LeDoux JE. Optical activation of lateral amygdala pyramidal cells instructs associative fear learning. Proc Natl Acad Sci. 107, 12692-7 (2010).
Behnia R, Barr FA, Flanagan JJ, Barlowe C and Munro S. The yeast orthologue of GRASP65 forms a complex with a coiled-coil protein that contributes to ER to Golgi traffic. J Cell Biol. 176, 255-61 (2007).
Behnia R and Munro S. Organelle identity and the signposts for membrane traffic. Nature. 438, 597-604 (2005).
Behnia R, Panic B, Whyte JR and Munro S. Targeting of the Arf-like GTPase Arl3p to the Golgi requires N-terminal acetylation and the membrane protein Sys1p. Nature Cell Biol. 6, 405-13 (2004).
Kohn JR, Portes JP, Christenson MP, Abbott LF, Behnia R. State and stimulus dependence reconcile motion computation and the Drosophila connectome bioRxiv (2021)
Heath SL, Christenson MP, Oriol E, Saavedra-Weisenhaus M, Kohn JR, Behnia R. Circuit Mechanisms Underlying Chromatic Encoding in Drosophila Photoreceptors. Current Biology. 30, 264-275.e8 (2020)