  Neuromorphic computing holds the promise to achieve the energy efficiency and
robust learning performance of biological neural systems. To realize the
promised brain-like intelligence, it needs to solve the challenges of the
neuromorphic hardware architecture design of biological neural substrate and
the hardware amicable algorithms with spike-based encoding and learning. Here
we introduce a neural spike coding model termed spiketrum, to characterize and
transform the time-varying analog signals, typically auditory signals, into
computationally efficient spatiotemporal spike patterns. It minimizes the
information loss occurring at the analog-to-spike transformation and possesses
informational robustness to neural fluctuations and spike losses. The model
provides a sparse and efficient coding scheme with precisely controllable spike
rate that facilitates training of spiking neural networks in various auditory
perception tasks. We further investigate the algorithm-hardware co-designs
through a neuromorphic cochlear prototype which demonstrates that our approach
can provide a systematic solution for spike-based artificial intelligence by
fully exploiting its advantages with spike-based computation.
