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Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition
In contrast, spiking neural networks (SNNs) have the potential to improve both the efficiency and biological plausibility of object recognition systems.
Efficient visual object representation using a biologically plausible spike-latency code and winner-take-all inhibition
Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory.
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