Winner-Take-All Networks of O(N) Complexity
Winner-Take-All Networks of O(N) Complexity
A Neuromorphic aVLSI network chip with configurable plastic synapses
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
Temporal coding in a silicon network of integrate-and-fire neurons
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A modular CMOS design of a Hamming network
IEEE Transactions on Neural Networks
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Scaling energy per operation via an asynchronous pipeline
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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Results are presented from several spiking network experiments performed on a novel neuromorphic integrated circuit. The networks are discussed in terms of their computational significance, which includes applications such as arbitrary spatiotemporal pattern generation and recognition, winner-take-all competition, stable generation of rhythmic outputs, and volatile memory. Analogies to the behavior of real biological neural systems are also noted. The alternatives for implementing the same computations are discussed and compared from a computational efficiency standpoint, with the conclusion that implementing neural networks on neuromorphic hardware is significantly more power efficient than numerical integration of model equations on traditional digital hardware.