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

Share this post on: Twitter| Facebook| Google+| Email

Categories

Tags

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