Neuronal Micronets as Nodal Elements.
YALE UNIV NEW HAVEN CT DEPT OF PSYCHOLOGY
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We have been working on developing a computationally efficient way to emulate neurons and to emulate circuits and networks of same. We made considerable progress in compressing realistic representations of neuronal computations into what we consider functionally equivalent inputoutput devices, which are now being incorporated into dynamic networks that learn associations and encode time. Our initial hypothesis about how to do this was rejected. Our new hypothesis offers great promise for scaling. This newer hypothesis resulted from examining simulations of realistic neurons and thinking about the scaling problem. The latter was funded by the ONR.