We present a position-based dynamics model for microcolony growth. In addition to achieving fast and stable simulation of thousands of cells, this model allows for the computation of cell interaction with the environment without sacrificing robustness and predictability. We introduce state-of-the-art principles of synthetic biology into our framework to enable biologically-informed microcolony pattern formation. We give detailed implementation of growth, communication, and external influences within the system and demonstrate our method for several scenarios which are experimentally verified. Finally, we provide a use case for rapid simulations enabled through our method including parameter search for tuning spoke-based pattern formation utilizing predefined and formulated biological primitives.

One of the challenges of researching spiking neural networks (SNN) is translation from temporal spiking behavior to classic controller output. While many encoding schemes exist to facilitate this translation, there are few benchmarks for neural networks that inherently utilize a temporal controller. In this work, we consider the common reinforcement problem of animat locomotion in an environment suited for evaluating SNNs. Using this problem, we explore novel methods of reward distribution as they impacts learning. Hebbian learning, in the form of spike time dependent plasticity (STDP), is modulated by a dopamine signal and affected by reward-induced neural activity. Different reward strategies are parameterized and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is used to find the best strategies for fixed animat morphologies. The contribution of this work is two-fold: to cast the problem of animat locomotion in a form directly applicable to simple temporal controllers, and to demonstrate novel methods for reward modulated Hebbian learning.

We present the use of a new computationaly efficient 3D physics model for the simulation of cells in a virtual aquatic world. In this model, cells can freely assemble and disconnect along the simulation without any separation between the development and evaluation stages, as is the case in most evo-devo models which only consider one cell cluster. While allowing for the discovery of interesting behaviors through the addition of new degrees of freedom, this 3D center-based physics engine and its associated virtual world also come with their drawbacks when applied to evolutionnary experiments: larger search space and numerous local optima. In this paper, we have designed an experiment in which cells must learn to survive by keeping their genome alive as long as possible in a demanding world. No morphology or strategy is explicitly enforced; the only objective the cells have to optimize is the survival time of the organism they build. We show that a novelty metric, adapted to our evo-devo matter, dramatically improves the outcome of the evolutionary runs. This paper also details some of the developmental strategies the evolved multicellular organisms have found in order to survive.