Abstract
It is assumed that the cause of cognitive and behavioral capacities of living systems is to be found in the complex structure-function relationship of their brains; a property that is still difficult to decipher. Based on a neurodynamics approach to embodied cognition this paper introduces a method to guide the development of modular neural systems into the direction of enhanced cognitive abilities. It uses formally the synchronization of subnetworks to split the dynamics of coupled systems into synchronized and asynchronous components. The concept of a synchronization core is introduced to represent a whole family of parameterized neurodynamical systems living in a synchronization manifold. It is used to identify those coupled systems having a rich spectrum of dynamical properties. Special coupling structures—called generative—are identified which allow to make the synchronized dynamics more “complex” than the dynamics of the isolated parts. Furthermore, a criterion for coupling structures is given which, in addition to the synchronized dynamics, allows also for an asynchronous dynamics by destabilizing the synchronization manifold. The large class of synchronization equivalent systems contains networks with very different coupling structures and weights allsharing the same dynamical properties. To demonstrate the method a simple example is discussed in detail.
Highlights
That the complexity of neural systems, how ever defined, is the source for the cognitive and behavioral capacities of living systems is an almost unquestioned assumption underlying most discussions about the impressive abilities of brains and brain-like systems
What is the effect of conservative couplings? Because the result of these couplings is a synchronization core CS(w+), which is the same as the structure of the parts CS(A) = CS(B), it will not generate a synchronized dynamics with a richer dynamical spectrum than that of the parts
Based on the assumption that cognitive abilities of brains and brain-like systems rest on their dynamical properties, the development of artificial neural networks providing cognitive abilities calls for systems having a manifold of different nontrivial attractors between which can be switched by external sensor signals (Pasemann, 2017)
Summary
That the complexity of neural systems, how ever defined, is the source for the cognitive and behavioral capacities of living systems is an almost unquestioned assumption underlying most discussions about the impressive abilities of brains and brain-like systems. It should be a promising strategy to develop larger neural networks with enriched cognitive faculties by coupling smaller networks in such a way that the resulting system has an extended capacity for sensorimotor control and enhanced cognitive solutions for challenging environmental situations This is one goal of a modular neurodynamics approach to embodied cognition (Anderson, 2003; Ziemke, 2003). It introduces synchronization and obstruction weight matrices, synchronization cores, and discusses the stability of synchronized and asynchronous dynamics and the decomposition of larger networks into synchronizable submodules. The paper concludes with a short discussion of results
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
