Embodied neural models

Using neural models controling robot bodies to study how the nervous system, body, and environment interact to produce behavior

To understand adaptive behavior, it is important to realize that the nervous system does not function in isolation. The brain is embodied, and the body is embedded in the environment. The properties, mechanisms, and functions of the nervous system arise only as it interacts with the rest of the body and as the animal behaves in the world.

Therefore I studied synthetic neural models performing behavioral tasks comparable to those used in animal experiments. The models are embodied in physical or virtual robots that interact richly with a complex environment. These neurorobotic models are used to explore potential neural mechanisms supporting behavior without ignoring the complexities of brain-body-environment interaction. Additionally, neurorobotic models can yield useful methods for engineering adaptive behavior in robots. Thus my work on adaptive behavior has two branches, one that addresses questions about the mechanisms of the mind through synthetic neural modeling, and one that uses the understanding generated by those models to create autonomous adaptive machines.

My collaborators and I have taken a neurorobotic approach to study the neural processes underlying episodic memory, working memory, visual perception, mental rotation, and consciousness.

Relevant publications

  • JL McKinstry, JG Fleischer, Y Chen, WE Gall, GM Edelman (2016). Imagery may arise from associations formed through sensory experience: a network of spiking neurons controlling a robot learns visual sequences in order to perform a mental rotation task. PLOS One, 11(9): e0162155.
  • JG Fleischer (2014). Persistent activity through multiple mechanisms in a spiking network that solves DMS tasks. Abstract and poster at Computational and Systems Neuroscience Meeting (COSYNE)
  • JG Fleischer and AE Kozarev (2012). Perceptual grouping and figure-ground segre- gation arising from short-term plasticity in a spiking network. Abstract and poster at Computational and Systems Neuroscience Meeting (COSYNE)
  • JG Fleischer and GM Edelman (2009). Brain-based devices: An embodied approach to linking nervous system structure and function to behavior. IEEE Robotics & Automation Magazine, 16(3):33–41.
  • JG Fleischer and JL Krichmar (2007). Sensory integration and remapping in a me- dial temporal lobe model during maze navigation by a brain-based device. Journal of Integrative Neuroscience, 6(3):403–431.
  • JG Fleischer, JA Gally, GM Edelman, and JL Krichmar (2007). Retrospective and prospective responses arising in a modeled hippocampus during maze navigation by a brain-based device. Proceedings of the National Academy of Sciences USA, 104(9):3556–3561.
  • JL Krichmar, AK Seth, DA Nitz, JG Fleischer, and GM Edelman (2005). Spatial navigation and causal analysis in a brain-based device modeling cortical-hippocampal interactions. Neuroinformatics, 3(3):197–222.

We have also use neural models to create useful engineering solutions in robotics.