לוח שנה משולב

Research in the Ben-Shahar lab at Washington University in St. Louis is focused on several integrative projects at the interface of evolution, genetics, and neuroethology. Specifically, research in the lab follows two major themes: 1) The genetic and neuronal processes that regulate the interactions between individual animals and their social environment, including the evolution and signaling mechanisms associated with pheromonal communication in insects, and the neuronal circuits that drive pheromone-induced behaviors; 2) the molecular evolution and genetics of the neuronal stress response, with a specific focus on mechanistic tradeoffs between neuronal robustness and cognition.

We address the problem of stability of motor actions implemented by the central nervous system based on simple algorithms potentially reflecting physical (including physiological) processes within the body. A number of conceptually simple algorithms that solve motor tasks with a high probability of success may be based on feedback schemes that ensure stability of subspaces of neural variables associated with accomplishing those tasks. The task is formulated in terms of linear constrains imposed either on the human body mechanical variables or on neural variables; we discuss three reference frames relevant to these processes. We discuss underlying basic principles of such algorithms, their architecture, and efficiency, and compare the outcomes of implementation of such algorithms with the results of experiments performed on the human hand.

Many visual tasks, such as separation of figures from ground and navigation, benefit from the extraction and the usage of local motion signals. Yet, there are many ways in which local motion signals are being represented (mostly based on mathematical and computational considerations). I’ll begin this talk by presenting a computational work that explored whether specific kinds of local motion signals occur in the natural world (Nitzany&Victor, 2014, Journal of Vision).
Next, I will present the results of a neurophysiological experiment where we recorded from the main visual brain areas of two visually accomplished, but very different, animals—macaque monkeys and dragonflies. We found similar responses to local motion signals across species, which may serve as neurophysiologic evidence that mammalian visual cortex and the visual centers of the dragonfly brain process motion using similar algorithms and may have converged on a common computational scheme for detecting visual motion.
Finally, I’ll present our current work, which extends and manipulates a few machine learning techniques to generate novel stimuli, where specific characteristics, with regards to local motion signals, are being preserved.
If time permits, I will discuss another line of work (Menda et. al., 2014, Current Biology, Shamble et. al., 2016, Current Biology), where we were able to record from neurons of jumping spiders. I will explain our approach that enables us to record from those tiny marvelous creatures and review our main findings with regards to visual and auditory cues.