Embodied cognitive morphogenesis as a route to intelligent systems

Abstract

The embryological view of development is that coordinated gene expression, cellular physics and migration provides the basis for phenotypic complexity. This stands in contrast with the prevailing view of embodied cognition, which claims that informational feedback between organisms and their environment is key to the emergence of intelligent behaviours. We aim to unite these two perspectives as embodied cognitive morphogenesis, in which morphogenetic symmetry breaking produces specialized organismal subsystems which serve as a substrate for the emergence of autonomous behaviours. As embodied cognitive morphogenesis produces fluctuating phenotypic asymmetry and the emergence of information processing subsystems, we observe three distinct properties: acquisition, generativity and transformation. Using a generic organismal agent, such properties are captured through models such as tensegrity networks, differentiation trees and embodied hypernetworks, providing a means to identify the context of various symmetry-breaking events in developmental time. Related concepts that help us define this phenotype further include concepts such as modularity, homeostasis and 4E (embodied, enactive, embedded and extended) cognition. We conclude by considering these autonomous developmental systems as a process called connectogenesis, connecting various parts of the emerged phenotype into an approach useful for the analysis of organisms and the design of bioinspired computational agents.

Keywords: 4E cognition; cognitive systems; developmental connectomics; embodied intelligence; morphogenesis.

Figure 1.

4E cognition in the context of agent development (generic embryo). Clockwise from top left: embodied (nervous system network in anatomical context), enactive (embodied nervous system network interacting with its physical and social environment), extended (cognition stored as symbols in the physical environment), and embedded (intra-agent interactions between nervous system and non-neuronal cellular networks).

Figure 2.

Tensegrity structures and their self-assembly. (a) Geodesic spheres arranged as single cells in a tensegrity network. (b) Space orbital station with 12 closed modules. Courtesy: Wikimedia user Segrim (http://hammer.bas.lv). Licensed under CC BY-SA 3.0.

Figure 3.

An example of a generic differentiation tree, originating at a zygote and differentiating over developmental time. Each branch represents a distinct tissue type, identified by a binary code. Differentiation into two tissues is defined by types of morphogenetic movement. Left-hand divisions correspond to a contraction wave (0), right-hand divisions correspond to an expansion wave (1).

Figure 4.

Differentiation tree for the tunicate Ciona intestinalis, which exhibits bilateral symmetry early in embryonic development. Differentiation tree (oriented by quadrant) shows a fate map up to the 256-cell stage, reorganized by tissue type. Centre circle: zygote. Green: endodermal; blue: germ cells; purple: neural plate; yellow: epidermal; red: mesodermal. These tissue types subsume many individual cells. Adapted from supplemental materials in [61].

Figure 5.

Schematics of a developing embryo as spatio-temporal embodied hypergraphs. (a) Cell division and differentiation information emphasizing a branching set of cell lineages (temporal) information. (b) Cell division and differentiation information organized by spatial regions and tissue types (spatio-temporal information). Examples (a) and (b) are based on a hypothetical organism. Orange: somatic cells; grey: developmental (embryonic stem) cells; white: neural cells; yellow: germ cells. Numbers represent individual cells per hypernode.

Figure 6.

Multi-modal connectogenesis as the interconnection of multiple network types in a hypothetical bilateral embryo. Shown here are the nervous system network, a bilateral tensegrity network and an anatomically embedded network of developmental cells.