We present a novel computational model aimed at exploring the concept of developmental exaptations, a previously hypothesized phenomenon wherein existing aspects of a developmental process are reused for a new, beneficial function. We leverage computational simulations to study this due to the challenges of observing developmental exaptations directly in nature. Employing a special fitness landscape, our model simulates the evolution of developmental strategies, allowing the study of how certain developmental instructions, which were initially evolved for a different function (or no function), can be repurposed to enhance fitness. The model utilizes indirect encoding to mimic biological processes, facilitating the evolution of exaptations. We demonstrate how crucial mutations contribute to achieving global fitness maxima, indicative of developmental exaptations for our special landscape. Our results not only provide computational evidence for the plausibility of developmental exaptations, but also open avenues for further research into detecting and understanding these phenomena in more complex and dynamic environments. This work establishes a new approach for modeling and analyzing developmental exaptations.

Neuromodulation is a pervasive biological process impacting neural activity at many scales. Changes in the concentration of a single neuromodulator can drastically alter the dynamics of a circuit. Nevertheless, how circuits can be both sensitive to the effects of neuromodulators, yet maintain stable behaviors in the face of constantly changing concentrations of them, is still poorly understood. Past work addressing this has focused on isolated circuits or individual neurons. In this paper, we study the effects of neuromodulation in the context of a complete brain-body-environment model. We use a genetic algorithm to find configurations of a dynamical neural network able to walk with and without the presence of an extrinsic neuromodulatory signal. We analyze, in some detail, networks, which break and cope under the effects of neuromodulation. We identify common stability mechanisms among successful networks, which correspond to previously proposed ideas. In addition, results indicate that proprioceptive feedback provides a stability mechanism for coping with neuromodulation that has not previously been considered in the literature.