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Link to original content: https://doi.org/10.1038/s42256-020-0159-4
Rapid online learning and robust recall in a neuromorphic olfactory circuit | Nature Machine Intelligence
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Rapid online learning and robust recall in a neuromorphic olfactory circuit

Nature Machine Intelligence volume 2, pages 181–191 (2020)Cite this article

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A preprint version of the article is available at arXiv.

Abstract

We present a neural algorithm for the rapid online learning and identification of odourant 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 odour representations from the activity of chemosensor arrays mounted in a wind tunnel. Learned odourants 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.

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Fig. 1: Model structure and signal encoding.
Fig. 2: Plasticity rules.
Fig. 3: Odourant-evoked MC activity patterns are attracted to learned representations.
Fig. 4: Multi-odour learning.
Fig. 5: Odour learning with plume dynamics.
Fig. 6: Performance evaluation.

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Data availability

The Vergara et al. gas sensor dataset4 is freely available from the UCI Machine Learning database (http://archive.ics.uci.edu/ml/datasets/gas+sensor+arrays+in+open+sampling+settings).

Code availability

A software version of the model reproducing the primary results of Figs. 3–5 is freely available from the ModelDB public archive (http://modeldb.yale.edu/261864). The Loihi source code is freely available from Github (https://github.com/intel-nrc-ecosystem/models/tree/master/official/epl).

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Acknowledgements

This work was funded by the National Institute on Deafness and Other Communication Disorders (R01 DC014701 and R01 DC014367 to TAC).

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Authors and Affiliations

  1. Neuromorphic Computing Laboratory, Intel Corporation, San Francisco, CA, USA

    Nabil Imam

  2. Computational Physiology Laboratory, Department of Psychology, Cornell University, Ithaca, NY, USA

    Thomas A. Cleland

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  1. Nabil Imam
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Contributions

T.A.C. conceived the neural algorithm. N.I. instantiated the algorithm and enhanced the learning rules. T.A.C. and N.I. wrote the manuscript.

Corresponding authors

Correspondence to Nabil Imam or Thomas A. Cleland.

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Competing interests

The authors declare competing interests as follows. The underlying platform-independent algorithm is the subject of a Cornell University patent application on which the authors are listed as inventors. N.I. is currently employed by Intel Labs, developers of the Loihi neuromorphic system. T.A.C. is a member of the Intel Neuromorphic Research Community and has received research funding from Intel for related work.

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Imam, N., Cleland, T.A. Rapid online learning and robust recall in a neuromorphic olfactory circuit. Nat Mach Intell 2, 181–191 (2020). https://doi.org/10.1038/s42256-020-0159-4

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