Ben Chandler

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About

Ben Chandler is a researcher at the intersection of neuromorphic engineering and adaptive robotics, with a focus on developing neural network models that can be implemented on emerging hardware. His most influential work, a 2011 review on the stability properties of neural plasticity rules for memristive neuromorphic hardware (14 citations), critically analyzed how synaptic learning algorithms must be adapted for next-generation, high-density memory devices. This foundational review anticipated the convergence of memristive technology, scalable parallel hardware, and neural network research that would enable large-scale adaptive systems. Chandler also contributed to the DARPA SyNAPSE program through his work on the Visually-Guided Adaptive Robot (ViGuAR) platform, which employed the Cog ex Machina neural modeling framework to demonstrate visually-guided behaviors in robotic agents. His research bridges the gap between theoretical neural computation and practical hardware implementation, addressing key challenges in stability and scalability. Chandler’s work remains relevant for researchers developing neuromorphic systems for autonomous agents, particularly those interested in how biological learning rules can be translated into robust, energy-efficient artificial neural networks.