Rapid online learning and robust recall in a neuromorphic olfactory circuit

TL;DR

This paper introduces a neuromorphic olfactory system that rapidly learns and identifies odors in noisy environments using spike timing and plasticity rules, inspired by biological olfaction, implemented on the Intel Loihi chip.

Contribution

It presents a biologically inspired, spike timing-based neural algorithm for rapid online odor learning and identification on neuromorphic hardware, with enhanced noise robustness and lifelong learning.

Findings

Effective odor identification despite noise and interference

Rapid one-shot learning of odor representations

Lifelong learning enabled by neurogenesis mechanisms

Abstract

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.