Mechanisms Underlying the Neural Computation of Head Direction

2020 ◽  
Vol 43 (1) ◽  
pp. 31-54 ◽  
Author(s):  
Brad K. Hulse ◽  
Vivek Jayaraman

Many animals use an internal sense of direction to guide their movements through the world. Neurons selective to head direction are thought to support this directional sense and have been found in a diverse range of species, from insects to primates, highlighting their evolutionary importance. Across species, most head-direction networks share four key properties: a unique representation of direction at all times, persistent activity in the absence of movement, integration of angular velocity to update the representation, and the use of directional cues to correct drift. The dynamics of theorized network structures called ring attractors elegantly account for these properties, but their relationship to brain circuits is unclear. Here, we review experiments in rodents and flies that offer insights into potential neural implementations of ring attractor networks. We suggest that a theory-guided search across model systems for biological mechanisms that enable such dynamics would uncover general principles underlying head-direction circuit function.

2010 ◽  
Vol 124 (1) ◽  
pp. 164-169 ◽  
Author(s):  
Matthijs A. A. van der Meer ◽  
Zoe Richmond ◽  
Rodrigo M. Braga ◽  
Emma R. Wood ◽  
Paul A. Dudchenko

Author(s):  
Toby St. Clere Smithe ◽  
Simon M Stringer

Abstract Place and head-direction (HD) cells are fundamental to maintaining accurate representations of location and heading in the mammalian brain across sensory conditions, and are thought to underlie path integration—the ability to maintain an accurate representation of location and heading during motion in the dark. Substantial evidence suggests that both populations of spatial cells function as attractor networks, but their developmental mechanisms are poorly understood. We present simulations of a fully self-organizing attractor network model of this process using well-established neural mechanisms. We show that the differential development of the two cell types can be explained by their different idiothetic inputs, even given identical visual signals: HD cells develop when the population receives angular head velocity input, whereas place cells develop when the idiothetic input encodes planar velocity. Our model explains the functional importance of conjunctive “state-action” cells, implying that signal propagation delays and a competitive learning mechanism are crucial for successful development. Consequently, we explain how insufficiently rich environments result in pathology: place cell development requires proximal landmarks; conversely, HD cells require distal landmarks. Finally, our results suggest that both networks are instantiations of general mechanisms, and we describe their implications for the neurobiology of spatial processing.


2014 ◽  
Vol 78 (4) ◽  
pp. 672-684 ◽  
Author(s):  
Kieran D. Collins ◽  
Jesus Lacal ◽  
Karen M. Ottemann

2016 ◽  
Vol 473 (22) ◽  
pp. 4083-4101 ◽  
Author(s):  
Mary Iconomou ◽  
Darren N. Saunders

Protein ubiquitylation is a widespread post-translational modification, regulating cellular signalling with many outcomes, such as protein degradation, endocytosis, cell cycle progression, DNA repair and transcription. E3 ligases are a critical component of the ubiquitin proteasome system (UPS), determining the substrate specificity of the cascade by the covalent attachment of ubiquitin to substrate proteins. Currently, there are over 600 putative E3 ligases, but many are poorly characterized, particularly with respect to individual protein substrates. Here, we highlight systematic approaches to identify and validate UPS targets and discuss how they are underpinning rapid advances in our understanding of the biochemistry and biology of the UPS. The integration of novel tools, model systems and methods for target identification is driving significant interest in drug development, targeting various aspects of UPS function and advancing the understanding of a diverse range of disease processes.


1991 ◽  
Vol 3 (2) ◽  
pp. 190-202 ◽  
Author(s):  
B. L. McNaughton ◽  
L. L. Chen ◽  
E. J. Markus

Behavioral and neurophysiological evidence strongly suggests that, within certain limits, rodents and humans can keep track of their directional heading relative to an inertial, and hence allocentric coordinate system. This “sense of direction” appears to involve the integration of angular velocity signals that arise primarily in the vestibular system. A hypothesis is proposed in which the integration process, an operation that may be difficult for neurons to implement, is replaced by a linear associative mapping, an operation that is at least theoretically easy to implement with neurons. The proposed system makes use of a set of linearly independent vectors representing the combination of the current head direction, and head angular velocity representations to “recall” the resulting head direction. It is then proposed that visual landmarks become incorporated into the directional system, enabling both the correction of cumulative error and, ultimately, the computation of novel, optimal trajectories between locations. According to the hypothesis, this occurs through the association of hippo-campal “local-view” cells (i.e., direction selective “place cells”) with “head-direction” cells located downstream in the dorsal presubiculum. The possible neurophysiological and neuroan-atomical bases for the proposed system are discussed.


2005 ◽  
Vol 17 (6) ◽  
pp. 1276-1314 ◽  
Author(s):  
Chris Eliasmith

Extending work in Eliasmith and Anderson (2003), we employ a general framework to construct biologically plausible simulations of the three classes of attractor networks relevant for biological systems: static (point, line, ring, and plane) attractors, cyclic attractors, and chaotic attractors. We discuss these attractors in the context of the neural systems that they have been posited to help explain: eye control, working memory, and head direction; locomotion (specifically swimming); and olfaction, respectively. We then demonstrate how to introduce control into these models. The addition of control shows how attractor networks can be used as subsystems in larger neural systems, demonstrates how a much larger class of networks can be related to attractor networks, and makes it clear how attractor networks can be exploited for various information processing tasks in neurobiological systems.


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