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Charles Anderson
Washington University School of Medicine

A Comparison of Neurobiological and Digital Computation

Monday 10th of April 2006 at 04:00pm
3105 Tolman Hall (Beach Room)

There is overwhelming evidence that neuronal systems represent analog variables with highly redundant population codes, a simple idea with a long history in neuroscience. This is also the principle that underlies all neural prosthesis research, such as retinal implants for the blind and artificial limb control for the paralyzed, where a few tens of electrodes interact with many hundreds to thousands of neurons. Starting from this premise, my students and I have developed a computational framework [1] that shows how large networks of spiking neurons can store and transform analog signals for sensory processing, motor control, and statistical inference. The resulting computational systems differ from traditional artificial neural networks that are focused on the highly nonlinear properties of individual neurons, and are more in line with modern Bayesian systems. The brain is more like an analog computer than a digital one; more like a Bayesian inference machine than a symbolic one. This talk will touch on these and other topics, contrasting the way digital and neurobiological systems handle common implementation issues such as the storage and transformation of variables, routing of information, virtual processing etc. Some speculations will be made on a generalized concept of pointers in which something like symbolic processing might be implemented in the brain. However, the emphasis is on low level processing issues and not on how our brain is organized to carry out high level cognitive processes.

[1] Chris Eliasmith and Charles H. Anderson, "Neural Engineering", MIT Press 2003. http://compneuro.uwaterloo.ca/


(video)


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