zador-anthony@CS.YALE.EDU (anthony zador) (12/20/89)
As a grad student in neuroscience studying neural nets, I was recently asked to lead a discussion for a small seminar in the Physiology dept here to address the question: What has NN research taught us about the brain? The seminar was organized by a professor here who studies the simultaneous activity of some large fraction of the neurons of Aplysia. He feels that even Aplysia may be way too complicated to understand. He wonders how we can hope to understand how the 10^12 neurons of the brain do their stuff when even the 10^3 neurons of Aplysia are a problem. In any case, I had to single out some paper in the field of neural nets to present. The idea was to convince this group of sceptics that NNets offer something to biological understanding. I wont tell what i chose. Rather, i'd be interested in hearing if anyone has any ideas. Note that since the audience consisted of experimental scientists, the goal was to find a paper that presented a *testable* (or better, tested) hypothesis or theory, and one that they couldnt have come up with themselves. Any ideas??? Tony Zador
bill@boulder.Colorado.EDU (12/20/89)
In article <9096@cs.yale.edu> zador-anthony@CS.YALE.EDU (anthony zador) writes: > > [. . . I'd] be interested in hearing if anyone has >any ideas. Note that since the audience consisted of experimental scientists, >the goal was to find a paper that presented a *testable* (or better, tested) >hypothesis or theory, and one that they couldnt have come up with themselves. > >Any ideas??? > What spring immediately to mind are David Marr's three papers on the cerebellum (1969), cerebral cortex (1970), and hippocampus (1971). Although each is out of date in some respects, they contain dozens of specific predictions, some of which have been confirmed, and are necessary reading for any neuroscientists working on these structures. Refs: A theory of cerebellar cortex, D Marr, J Physiol 202, pp 437-470, (1969). A theory for cerebral cortex, D Marr, Proc Roy Soc Lond B 176, pp 161-234, (1970). Simple Memory: a theory for archicortex, D Marr, Phil Trans Roy Soc Lond 262, pp 23-81, (1971).
slehar@bucasd.bu.edu (Lehar) (12/20/89)
PAPER TO ADDRESS THE QUESTION "WHAT HAS NN TAUGHT US ABOUT THE BRAIN?" My vote would go to Stephen Grossberg's models. These models are unique in several aspects: 1: the models are based on behavioral data, rather than being arbitrary networks that perform cute tricks, these networks are models of behavior of an entire organism, such as in classical conditioning, and have made quantitative behavioral predictions on numerous occasions 2: the models also extend to the neurological level, where Grossberg makes specific predictions about certain pathways in hippocampus, cerebellum, visual cortex, etc. many of which have already been confirmed 3: the models reflect the dynamic nature of brain mechanisms, being expressed as dynamic systems and differential equations. This is an aspect of cognition which is ignored by most neural models for simplicity. Grossberg's models have complicated dynamics and are difficult to simulate and analyze, but in return they display a diversity of subtle dynamic behaviors unseen in other models, and reminiscent of the subtle complexity of natural networks, like Aplysia. 4: Grossberg has written on so many interesting and diverse fields including vision: the boundary contour system & feature contour system is a network that was designed to replicate human visual illusions- where human vision fails, to understand how human vision works. Each element of the model is rigorously derived from psychophysical data on illusions, yet they correspond remarkably with known neural architectures of the visual system, while making predictions about what is not yet known (some recently confirmed). motor control: Grossberg's models begin with postulates on whether feedback or feedforward systems are used, and results in a hybrid system where initial feedback errors are used to learn feedforward precision. Components of the model correspond to known motor control architectures in limbs, spinal chord and cerebellum. cognition: Grossberg has a whole lineage of models describing ever more complex aspects of classical conditioning, arousal, motivation, feedback, normalization of inputs, etc. culminating in high level cognitive models such as adaptive resonance theory, with neurological analogs in hippocampus, hypothalamus and cortex. To me, this represents the finest in neural modeling, building models that are inspired by behavior, making predictions about neurophysiology, and explaining dynamic processes in the brain. Stephen Grossberg & Dejan Todorovic NEURAL DYNAMICS OF 1-D AND 2-D BRIGHTNESS PERCEPTION: A UNIFIED MODEL OF CLASSICAL AND RECENT PHENOMENA Perception & Psychophysics 1987 Stephen Grossberg & Ennio Mingolla NEURAL DYNAMICS OF PERCEPTUAL GROUPING: TEXTURES, BOUNDARIES AND EMERGENT SEGMENTATIONS Perception & Psychophysics 1985, 38 (2) 141-171 Gail Carpenter & Stephen Grossberg A MASSIVELY PARALLEL ARCHITECTURE FOR A SELF-ORGANIZING NEURAL PATTERN RECOGNITION MACHINE Computer Vision, Graphics, and Image Processing 1987, 37, 54-115 Acedmic Press, Inc. Stephen Grossberg & Gregory Stone NEURAL DYNAMICS OF ATTENTION SWITCHING AND TEMPORAL ORDER INFORMATION IN SHORT TERM MEMORY Memory and Cognition 1986, 14 (6), 451-468 Stephen Grossberg & Daniel S. Levine NEURAL DYNAMICS OF ATTENTIONALLY MODULATED PAVLOVIAN CONDITIONING: BLOCKING, INTER-STIMULUS INTERVAL, AND SECONDARY REINFORCEMENT Applied Optics 1987, 26, 5015-5030 Steven Grossberg & Nestor A. Schmajuk NEURAL DYNAMICS OF ATTENTIONALLY MODULATED PAVLOVIAN CONDITIONING: CONDITIONED REINFORCEMENT, INHIBITION, AND OPPONENT PROCESSING Psychobiology 1987, 15, 195-240 Daniel Bullock and Steven Grossberg NEURAL DYNAMICS OF PLANNED ARM MOVEMENTS: EMERGENT INVARIANTS AND SPEED-ACCURACY PROPERTIES DURING TRAJECTORY FORMATION -- (O)((O))(((O)))((((O))))(((((O)))))(((((O)))))((((O))))(((O)))((O))(O) (O)((O))((( slehar@bucasb.bu.edu )))((O))(O) (O)((O))((( Steve Lehar Boston University Boston MA )))((O))(O) (O)((O))((( (617) 424-7035 (H) (617) 353-6425 (W) )))((O))(O) (O)((O))(((O)))((((O))))(((((O)))))(((((O)))))((((O))))(((O)))((O))(O)