Brian Taba, Kwabena A. Boahen
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. Axons are guided by growth cones, which are modeled in analog VLSI for the first time. Growth cones migrate up neurotropin gradients, which are represented by charge diffusing in transistor channels. Virtual axons move by rerouting address-events. We refined an initially gross topographic projection by simulating retinal wave input.
1 Neuromorphic Systems
Neuromorphic engineers are attempting to match the computational efficiency of biological systems by morphing neurocircuitry into silicon circuits . One of the most detailed implementations to date is the silicon retina described in  . 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 and exceedingly plastic higher processing centers in cortex.
Instead of an explicit blueprint for every cortical area, what is needed is a developmental rule that can wire complex circuits from minimal specifications. One candidate is the famous "cells that fire together wire together" rule, which strengthens excitatory connections between coactive presynaptic and postsynaptic cells. We implemented a self-rewiring scheme of this type in silicon, taking our cue from axon remodeling during development.