Dana H. Ballard

"In order to be adaptable, an organism must be suboptimal."

"This book provides a comprehensive introduction to the computational material that forms the underpinnings of the currently evolving set of brain models. It is now clear that the brain is unlikely to be understood without recourse to computational theories. The theme of An Introduction to Natural Computation is that ideas from diverse areas such as neuroscience, information theory, and optimization theory have recently been extended in ways that make them useful for describing the brains programs. This book provides a comprehensive introduction to the computational material that forms the underpinnings of the currently evolving set of brain models. It stresses the broad spectrum of learning models--ranging from neural network learning through reinforcement learning to genetic learning--and situates the various models in their appropriate neural context."

"To write about models of the brain before the brain is fully understood is a delicate matter. Very detailed models of the neural circuitry risk losing track of the task the brain is trying to solve. At the other extreme, models that represent cognitive constructs can be so abstract that they lose all relationship to neurobiology. An Introduction to Natural Computation takes the middle ground and stresses the computational task while staying near the neurobiology."

"My main research interest is in computational theories of the brain with emphasis on human vision and motor control. In 1985 Chris Brown and I led a team that designed and built a high speed binocular camera control system capable of simulating human eye movements. The system was mounted on a robotic arm that allowed it to move at one meter per second in a two meter radius workspace. This system has led to an increased understanding of the role of behavior in vision, in particular that visual computations can be simpler when interacting in the 3D world. To pursue this avenue we became interested in pursuing this research by using high DOF models of humans' natural behavior in virtual reality environments. To be able to monitor human eye movements in these enviroments, with Mary Hayhoe and Jeff Pelz we build the first head montor that ecorporated a eye tracker inside the HMD. My PhD research focused on biomedical image processing but as an assistant professor I became more interested on the general problem of modeling vision and collaborated with my University of Rochester colleague Chris Brown to write the first text in computer vision in 1982, which is still accessible on the web. In the middle 80s the field discovered biologically motivated active vision could be much more efficient than static picture analysis. Chris and I built the first real-time binocular robot that could make saccades and pursue moving targets. The robot was enormously influential in showing that motion actually made many of the vision computations simpler by providing efference copy information. As a result, my interests progressed to the human brain realized vision. Through Rochester's Bridging program to learn tract tracing in visual cortex in cats and rats in the department of Anatomy and Physiology. I became hooked on the brain, but realized my talents were in mathematical modelingThat experience has led to a lifelong interest in neural mode . A brilliant student, Raj Rao was able to define a predictive coding model that showed that neural systems spanning cortical maps could be seen as pivotal in learning memory representations 1. That model has been enormously successful, but a frustration for me has been that it was cast at a level above spikes. Working with a postdoc, Janneke Jehee, we put together a spiking model, which exhibited several desirable properties, but had unsolved problems. Most recently I have resolved these problems with another star student Ruohan Zhang. We realized that we are at the stage that the elements of the spiking model could be tested. In that process, I have been extraordinarily fortunate in starting a collaboration with Luc Genet who is a leader in cell patch clamp technology, particularly in the two-cell patches we will need for next step. We have been collaborating for the two years and our joint efforts are extremely promising. This effort dovetails with a second interest in How the brain represents movement. Our newest papers show that humans use common postures in whole body tasks and that these are local energy minima, suggesting new constraints for motor cortex. 1."