Spontaneous Motion on Two-dimensional Continuous Attractors

Abstract

Attractor models are simplified models used to describe the dynamics of firing rate profiles of a pool of neurons. The firing rate profile, or the neuronal activity, is thought to carry information. Continuous attractor neural networks (CANNs) describe the neural processing of continuous information such as object position, object orientation and direction of object motion. Recently, it was found that, in one-dimensional CANNs, short-term synaptic depression can destabilize bump-shaped neuronal attractor activity profiles. In this paper, we study two-dimensional CANNs with short-term synaptic depression and with spike frequency adaptation. We found that the dynamics of CANNs with short-term synaptic depression and CANNs with spike frequency adaptation are qualitatively similar. We also found that in both kinds of CANNs the perturbative approach can be used to predict phase diagrams, dynamical variables and speed of spontaneous motion.

58 pages, 11 figures

References in corpus (4)

• A Moving Bump in a Continuous Manifold: A Comprehensive Study of the Tracking Dynamics of Continuous Attractor Neural Networks
• Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy and Mobility
• A Rich Spectrum of Neural Field Dynamics in the Presence of Short-Term Synaptic Depression
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Cited by in corpus (2)

• Fluctuation-response Relation Unifies Dynamical Behaviors in Neural Fields
• Bimodular continuous attractor neural networks with static and moving stimuli