Alexander B. Johnson
Papers
1
Total Citations
7
H-Index
1
About
Alexander B. Johnson is a computational neuroscientist whose research lies at the intersection of artificial intelligence, robotics, and biological memory systems. His primary contributions focus on neuroevolution—the use of evolutionary algorithms to train neural networks—particularly in replicating complex cognitive behaviors observed in animal models. In his most-cited work, Johnson evolved the weights of a recurrent neural network (RNN) to simulate spatial and working memory in a virtual robotic rat navigating a maze. By mirroring both the behavior and neural activity patterns of real rodents, his approach offers a powerful framework for understanding how biological memory emerges from neural dynamics. Though still early in his career, his 2021 paper has already garnered 7 citations, signaling growing interest in his methodology. Johnson’s work stands out for bridging computational modeling with empirical neuroscience, providing testable predictions about hippocampal function and memory consolidation. His research holds promise for advancing both neurorobotics and our understanding of cognitive disorders, making him a rising voice in the field of embodied cognition and evolutionary robotics.
Research Focus
Key Achievements
Top Papers
- 1