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Neurons in the mammalian primary visual cortex are selective along multiple stimulus dimensions, including retinal position, spatial frequency, and orientation. Neurons tuned to different stimulus features but the same retinal position are grouped into retinotopic arrays of hypercolumns. This paper describes a neuromorphic implementation of orientation hypercolumns, which consists of a single silicon retina feeding multiple chips, each of which contains an array of neurons tuned to the same orientation and spatial frequency, but different retinal locations. All chips operate in continuous time, and communicate with each other using spikes transmitted by the address-event representation protocol. This system is modular in the sense that orientation coverage can be increased simply by adding more chips, and expandable in the sense that its output can be used to construct neurons tuned to other stimulus dimensions. We present measured results from the system, demonstrating neuronal selectivity along position, spatial frequency and orientation. We also demonstrate that the system supports recurrent feedback between neurons within one hypercolumn, even though they reside on different chips. The measured results from the system are in excellent concordance with theoretical predictions.
Address-event representation (AER), Gabor filter, image processing, mixed analog-digital integrated circuits, neural chips, neuromorphic engineering, visual cortex
Choi, T. Y., Merolla, P. A., Arthur, J. V., Boahen, K. A., & Shi, B. E. (2005). Neuromorphic Implementation of Orientation Hypercolumns. Retrieved from https://repository.upenn.edu/be_papers/62
Date Posted: 22 November 2005
This document has been peer reviewed.