Faculty Opinions recommendation of Double-ring network model of the head-direction system.

Aiming at the problems of low power supply reliability, poor transfer capacity between stations, and low line utilization in the current distribution network, this paper proposes a diamond-shaped distribution network structure with a clear structure. First, we investigated the typical wiring patterns of medium-voltage distribution networks in Tokyo, Japan, Paris, France, and China’s developed cities, and summarized experience and shortcomings. Secondly, combining the typical wiring patterns of distribution networks in China and abroad, construct a diamond-shaped distribution network structure, and study its adaptability, safety and flexibility, power supply reliability, and economy. Finally, take the transformation of the wiring mode of a regional distribution network in a certain city as an example, compare the use of the diamond-shaped distribution network structure in this article with the use of cable double-ring network wiring, cable “double petal” wiring, and Shanghai diamond-type wiring distribution network grid reconstruction The effect verifies the superiority of the diamond-shaped distribution network structure in this paper.

Download Full-text

An oscillatory criterion for a time delayed neural ring network model

Neural Networks ◽

10.1016/j.neunet.2012.01.008 ◽

2012 ◽

Vol 29-30 ◽

pp. 70-79 ◽

Cited By ~ 11

Author(s):

Chunhua Feng ◽

Réjean Plamondon

Keyword(s):

Network Model ◽

Ring Network

Download Full-text

The Role of Idiothetic Signals, Landmarks, and Conjunctive Representations in the Development of Place and Head-Direction Cells: A Self-Organizing Neural Network Model

Cerebral Cortex Communications ◽

10.1093/texcom/tgab052 ◽

2021 ◽

Author(s):

Toby St. Clere Smithe ◽

Simon M Stringer

Keyword(s):

Network Model ◽

Signal Propagation ◽

Cell Types ◽

Visual Signals ◽

Mammalian Brain ◽

Head Direction ◽

State Action ◽

Attractor Networks ◽

Planar Velocity ◽

Self Organizing

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.

Download Full-text

Learning accurate path integration in a ring attractor model of the head direction system

10.1101/2021.03.12.435035 ◽

2021 ◽

Author(s):

Pantelis Vafidis ◽

David Owald ◽

Tiziano D’Albis ◽

Richard Kempter

Keyword(s):

Supervised Learning ◽

Network Model ◽

Path Integration ◽

Internal Representation ◽

Learning Rule ◽

Developmental Phase ◽

Head Direction ◽

Optogenetic Stimulation ◽

Attractor Model ◽

The One

SummaryRing attractor models for angular path integration have recently received strong experimental support. To function as integrators, head-direction (HD) circuits require precisely tuned connectivity, but it is currently unknown how such tuning could be achieved. Here, we propose a network model in which a local, biologically plausible learning rule adjusts synaptic efficacies during development, guided by supervisory allothetic cues. Applied to the Drosophila HD system, the model learns to path-integrate accurately and develops a connectivity strikingly similar to the one reported in experiments. The mature network is a quasi-continuous attractor and reproduces key experiments in which optogenetic stimulation controls the internal representation of heading, and where the network remaps to integrate with different gains. Our model predicts that path integration requires supervised learning during a developmental phase. The model setting is general and also applies to architectures that lack the physical topography of a ring, like the mammalian HD system.

Download Full-text