Fat-trees: universal networks for hardware-efficient supercomputing
IEEE Transactions on Computers
Analog VLSI and neural systems
Analog VLSI and neural systems
Winner-take-all networks of O(N) complexity
Advances in neural information processing systems 1
Range queries in OLAP data cubes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Models of Computation: Exploring the Power of Computing
Models of Computation: Exploring the Power of Computing
Neural circuits for pattern recognition with small total wire length
Theoretical Computer Science - Natural computing
Learning a Sparse Representation for Object Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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We introduce wire length as a salient complexity measure for analyzing the circuit complexity of sensory processing in biological neural systems. This new complexity measure is applied in this paper to two basic computational problems that arise in translation- and scale-invariant pattern recognition, and hence appear to be useful as benchmark problems for sensory processing. We present new circuit design strategies for these benchmark problems that can be implemented within realistic complexity bounds, in particular with linear or almost linear wire length. Finally, we derive some general bounds which provide information about the relationship between new complexity measure wire length and traditional circuit complexity measures.