A Recurrent Dynamic Model for Efficient Bayesian Optimization

Bayesian optimization is an important black-box optimization method used in active learning. An implementation of the algorithm using vector embeddings from Vector Symbolic Architectures was proposed as an efficient, neuromorphic approach to solving these implementation problems. However, a clear path to neural implementation has not been explicated. In this paper, we explore an implementation of this algorithm expressed as recurrent dynamics that can be easily translated to neural populations, and present an implementation within the Lava programming framework for Intel's neuromorphic computers. We compare the performance of the algorithm using different resolution representations of real-valued data, and demonstrate that the ability to find optima is preserved. This work provides a path forward to the implementation of Bayesian optimization on low-power neuromorphic computers, permitting the deployment of active learning techniques in low-power, edge computing applications.