Topographic map formation by silicon growth cones

We describe a self-configuring neuromorphic chip that uses a model of activity-dependent axon remodeling to automatically wire topographic maps based solely on input correlations. Growth cones are modeled in analog VLSI for the first time. Neurotropin is modeled by charge diffusing in transistor channels. Virtual axons migrate by remapping address-events. We simulated retinal wave input to refine an initially gross topographic projection. 1 Neuromorphic Systems Neuromorphic engineers are attempting to match the computational efficiency of biological systems by morphing neurocircuitry into silicon circuits [1]. One of the most detailed implementations to date is the silicon retina described in [2]. This chip comprises thirteen different cell types, each of which must be individually and painstakingly wired. While this circuit-level approach has been very successful in sensory systems, it is less helpful when modeling largely unelucidated an