Building on our organization’s (Orthogonal Research and Education Laboratory) past two years as a Google Summer of Code participant, we seek to combine the work on two Artificial Intelligence paradigms: Developmental Braitenberg Vehicles and Contextual Geometric Structures. In the case of the former (Developmental Braitenberg Vehicles), we learned lessons on how to create computational analogues of nervous systems that emerge during the developmental period. Through a variety of implementations, these embodied nervous systems can perform behaviors such as spatial navigation, multisensory integration, and coordinated emergence. In the case of the latter (Contextual Geometric Structures), we were able to instantiate a series of mathematical models of brain function and cultural classifications into code.
This year’s project will involve integrating elements of these two initiatives into something called Contextual Neurodevelopmental Dynamics. We are interested in building artificial nervous systems that can expand their number of neurons and connections (nodes and links) over time, with each node possessing a representation that provides context to the information flow across the network. These representations will be based on a Contextual Geometric Structure (CGS) architecture, descriptions of which can be found in the CGS project repository. A CGS is a kernel that classifies a range of sensory inputs according to a set of beliefs specific to that kernel. Code-level implementation of both the Developmental Braitenberg Vehicle and CGS approaches currently exist in Python and Kotlin, while simulations have involved the use of genetic algorithms, physics-based and agent-based models, and Hebbian learning. Contextual Connection Machines are an instance of a meta-brain model, a hybrid model that captures multiple aspects of intelligent behavior and scales of the brain.
As a student, you will join our Representational Brains and Phenotypes group, as well as participate in our Saturday Morning NeuroSim meeting. You will also contribute to building upon emerging intra-organizational topics such as Meta-Brain Models and Cybernetics. The successful student should approach this project with both technical skill and intellectual curiosity. A background in programming languages such as Python, Kotlin, or Julia are essential, and an interest in techniques such as genetic algorithms or agent-based models are preferred.