Rapid online learning and robust recall in a neuromorphic olfactory circuit

This week on Journal Club session Damien Drix will talk about a paper "Rapid online learning and robust recall in a neuromorphic olfactory circuit".

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.


Papers:

Date: 2021/02/05
Time: 16:00
Location: online

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neural networks Associative memory Studentship cerebellum Artificial intelligence olfaction Dendritic computation Purkinje cell Neuronal Morphology Evolutionary algorithms Neuromorphic hardware Persistent homology Open position COVID-19 Network connectivity robotics Deep learning Convolutional Neural Networks Neurons computational neuroscience neuroscience computational modelling Homoeostasis Software development machine learning PCA Computer Science optimization Structural plasticity synaptic plasticity