nips nips2002 nips2002-200 nips2002-200-reference knowledge-graph by maker-knowledge-mining
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Author: Brian Taba, Kwabena A. Boahen
Abstract: 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 [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 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
[1] C. Mead (1990) Neuromorphic electronic systems. IEEE Proc, 78(10): 1629-1636.
[2] K.A. Zagh1ou1 (2002) A silicon implementation of a novel model for retinal processing. PhD thesis, University of Pennsylvania.
[3] M. Sur and C.A. Leamy (2001) Development and plasticity of cortical areas and networks . Nat Rev Neurosci, 2:251-262 .
[4] E.J. Huang and L.F. Reichardt (2001) Neurotrophins: roles in neuronal development and function. Annu Rev Neurosci , 24 :677-736.
[5] M.B. Feller, D.A. Butts, H.L. Aaron, D.S. Rokhsar, and C.J. Shatz (1997) Dynamic processes shape spatiotemporal properties of retinal waves. Neuron, 19:293-306 .
[6] K.A. Boahen (2000) Point-to-point connectivity between neuromorphic chips using address-events. IEEE Transactions on Circuits and Systems II, 47 :416-434.
[7] J.G. Elias (1993) Artificial dendritic trees. Neural Comp, 5:648-663.
[8] K.A. Boahen and A.G. Andreou (1991) A contrast-sensitive silicon retina with reciprocal synapses. Advances in Neural Information Processing Systems 4, J.E. Moody and R.P. Lippmann, eds. , pp 764-772, Morgan Kaufman, San Mateo, CA.
[9] E . Culurciello, R. Etienne-Cummings, and K. Boahen (2001) Arbitrated address event representation digital image sensor. IEEE International Solid State Circuits Conference, pp 92-93.
[10] T. Elliott and N.R. Shadbolt (1999) A neurotrophic model of the development of the retinogeniculocortical pathway induced by spontaneous retinal waves. J Neurosci, 19:7951-7970.