Chaotic Neurodynamics for Autonomous Agents

Abstract

Abstract-Mesoscopic level neurodynamics study the collective dynamical behavior of neural populations. Such models are becoming increasingly important in understanding large-scale brain processes. Brains exhibit aperiodic oscillations with a much more rich dynamical behavior than fixed-point and limitcycle approximation allow. Here we present a discretized model inspired by Freeman's K-set mesoscopic level population model. We show that this version is capable of replicating the important principles of aperiodic/chaotic neurodynamics while being fast enough for use in real-time autonomous agent applications. This simplification of the K model provides many advantages not only in terms of efficiency but in simplicity and its ability to be analyzed in terms of its dynamical properties. We study the discrete version using a multi-layer, highly recurrent model of the neural architecture of perceptual brain areas. We use this architecture to develop example action selection mechanisms in an autonomous agent.

Implementing intentional robotics principles using SSR2K platform

“…The feasibility and competitiveness of K-based mobile robot control has been demonstrated on various simple platforms. KIII-based navigation has been implemented on a Khepera robot simulation environment [35]. The results compare very well with Vershure's results in the original Distributed Adaptive Control experiment [36], and with the object avoidance performance of Schmitt trigger [37].…”

The KIV model of intentional dynamics and decision making

“…They model the hierarchy of the brain starting from the mm scale to the complete hemisphere. Today, K sets are used in a wide range of applications, including classification (Chang, Freeman, & Burke, 1998;Freeman, Kozma, & Werbos, 2001), image recognition (Li, Lou, Wang, Li, & Freeman, 2006), time series prediction (Beliaev & Kozma, 2007), and robot navigation (Harter & Kozma, 2005;Voicu, Kozma, Wong, & Freeman, 2004). Recent developments include KIV sets for sensor fusion (Kozma & Tunstel, 2005;Kozma & Muthu, 2004), and autonomous control (Kozma, 2007a;Kozma et al, 2008).…”

Effects on hypothalamus when CPG is fed back to basal ganglia based on KIV model

“…In the perceptual systems, input from the sensors perturbs the neuronal ensembles from the chaotic background. The result is that the system transitions into a new attractor that represents the meaning of the sensory input, given the context of the state of the organism and its environment (Harter and Kozma 2005). The phase diagram of M 11 and G 11 is chaotic when no signal is fed back to the basal ganglia.…”

“…The K-sets have been used for dynamic memory designs and for robust classification and pattern recognition (Gutierrez-Osuna and Gutierrez-Galvez 2003;Harter and Kozma 2005). It is deserved further investigation that the K-sets are used for the classification and pattern recognition of the CPG which corresponds to different locomotion (Dong et al 2011).…”