Combined Computational Systems Biology and Computational Neuroscience Approaches Help Develop of Future “Cognitive Developmental Robotics”

Understanding cognitive functions and mechanisms of development in animals is essential for the future generation of more intelligent systems (Hirel et al., 2011; Hassabis et al., 2017). In traditional robotics the robots perform predefined tasks in a fixed environment. However, the field of modern robotics is seeking approaches to develop artificial systems to execute tasks in less predefined dynamic environments. Such robotic systems should learn from information extracted from the environment to demonstrate actions like natural intelligence (Matarić, 1998). However, such capabilities cannot be achieved sufficiently with classical control approaches (Christaller, 1999; Hassabis et al., 2017). Bio-inspired robots are usually developed using general network architectures of biological neural systems (Meyer andGuillot, 2008), synaptic plasticity (Grinke et al., 2015), correlation-based learning rule with synaptic scaling (Tetzlaff et al., 2011). Recent progresses in cognitive sciences and developmental neurobiology have promoted a new branch of robotics named “Cognitive Developmental Robotics (CDR)” (Asada et al., 2009; Asada, 2013; Min et al., 2016). Such robots behave in response to a dynamic environment by Spiking Neural Networks based controllers. CDR has emerged as a scientific field of research aiming to develop robots with abilities to effectively interact with dynamic environments and show brain-like cognitive abilities such as memory and learning. CDR has just started and its design principles and methodology have not been established (Wang et al., 2002). To construct a software of a CDR system, a computational model of agent-environment interaction that define dynamical response of the CDR executed by a SNN with a sufficient architecture is required. Briefly, it is done as follows (Asada et al., 2009; Asada, 2013; Xu et al., 2014):