Novel tracking function of moving target using chaotic dynamics in a recurrent neural network model

Abstract

Chaotic dynamics introduced in a recurrent neural network model is applied to controlling an object to track a moving target in two-dimensional space, which is set as an ill-posed problem. The motion increments of the object are determined by a group of motion functions calculated in real time with firing states of the neurons in the network. Several cyclic memory attractors that correspond to several simple motions of the object in twodimensional space are embedded. Chaotic dynamics introduced in the network causes corresponding complex motions of the object in two-dimensional space. Adaptively real-time switching of control parameter results in constrained chaos (chaotic itinerancy) in the state space of the network and enables the object to track a moving target along a certain trajectory successfully. The performance of tracking is evaluated by calculating the success rate over 100 trials with respect to nine kinds of trajectories along which the target moves respectively. Computer experiments show that chaotic dynamics is useful to track a moving target. To understand the relations between these cases and chaotic dynamics, dynamical structure of chaotic dynamics is investigated from dynamical viewpoint.

Vague-to-crisp dynamics of percept formation modeled as operant (selectionist) process

“…Models based on attractor dynamics have been used successfully for describing cognitive processing (Kay et al 1995;Satel et al 2009;Li and Nara 2008). It is anticipated that our proposed DL approach is of relevance to neurodynamics in a broader context, exemplified by the pioneering experimental and theoretical brain research by Freeman (1999).…”

Combined effects of LTP/LTD and synaptic scaling in formation of discrete and line attractors with persistent activity from non-trivial baseline

“…Here, these patterns can result from the intrinsic dynamics in local populations of neurons. Chaotic patterns of activity can be exploited to achieve motor tasks such as motion control (Yoshida et al 2010) and tracking (Li and Nara 2008).…”

Control of roving robot using chaotic dynamics in a quasi-layered recurrent neural network for sensing and driving

“…Complex motions generated by chaotic dynamics was found to be useful to find detour avoiding obstacles in mazes. Furthermore, Li and Nara developed the idea to tracking of a moving target [6]. On the other hand, we found that chaos has a sensitive response property to external input [5].…”