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Accurate angular integration with only a handful of neurons

Mathematical Biology

Speaker: Marcella Noorman, HHMI Janelia Research Campus
Related Webpage: https://www.janelia.org/people/marcella-noorman
Location: Online (via Zoom)
Start time: Mon, May 18 2020, 3:10PM

Navigation is a necessary survival skill for mammals and insects alike. In order to navigate, animals need to accurately track their movements over time. In mammals, this ability is thought to rely on so-called “head direction” cells that maintain an internal representation of the direction the animal is facing. For over two decades, ring attractor networks have been studied as a putative model of this head-direction system [1,2]. Such networks rely on large numbers of neurons to accurately integrate angular velocity in order to maintain a precise representation of heading. However, recent findings in the fruitfly Drosophila melanogaster suggest that this computation can be robustly carried out by a small network consisting of relatively few neurons [3-6]. These findings call into question our current understanding of continuous attractors and their implementation in small circuits.

In this work, we analyze failures—such as the inability to integrate small velocity input, variable integration of constant velocity input, and drift in the absence of velocity input—that emerge in small ring attractor networks with limited numbers of neurons. Motivated by the observation that these failures are seldom seen in a walking fly’s heading network [3,5], we mathematically derive conditions under which such a constrained system can overcome these shortcomings and achieve the performance of the unconstrained system. Together, these results show how even small networks can accurately integrate angular velocity in order to guide navigation.

References. [1] Skaggs et al. (1995), NeurIPS; [2] Zhang (1996), J. Neurosci.; [3] Seelig & Jayaraman (2015), Nature; [4] Kim, Rouault et al. (2017), Science; [5] Turner-Evans, Wegener et al. (2017), eLife; [6] Green et al. (2017), Nature.



Note that this seminar is online at https://ucdavisdss.zoom.us/j/99480513904. Please email Rishidev Chaudhuri for the password.