Rapid online learning and robust recall in a neuromorphic olfactory circuit
This work addresses signal identification problems in high-dimensional, noisy environments, such as olfaction, with potential applications in robotics and sensing, but it is incremental as it builds on existing neuromorphic and biological models.
The paper tackles the problem of rapid online learning and robust identification of odorants under noise by presenting a neuromorphic olfactory circuit algorithm, achieving reliable identification despite strong destructive interference.
We present a neural algorithm for the rapid online learning and identification of odorant samples under noise, based on the architecture of the mammalian olfactory bulb and implemented on the Intel Loihi neuromorphic system. As with biological olfaction, the spike timing-based algorithm utilizes distributed, event-driven computations and rapid (one-shot) online learning. Spike timing-dependent plasticity rules operate iteratively over sequential gamma-frequency packets to construct odor representations from the activity of chemosensor arrays mounted in a wind tunnel. Learned odorants then are reliably identified despite strong destructive interference. Noise resistance is further enhanced by neuromodulation and contextual priming. Lifelong learning capabilities are enabled by adult neurogenesis. The algorithm is applicable to any signal identification problem in which high-dimensional signals are embedded in unknown backgrounds.