Online learning model of olfactory bulb external plexiform layer network (Imam & Cleland 2020)

This model illustrates the rapid online learning of odor representations, and their recognition despite high levels of interference (other competing odorants), in a model of the olfactory bulb external plexiform layer (EPL) network. The computational principles embedded in this model are based on the those developed in the biophysical models of Li and Cleland (2013, 2017). This is a standard Python version of a model written for Intel's Loihi neuromorphic hardware platform (The Loihi code is available at

Model Type: Neuron or other electrically excitable cell

Region(s) or Organism(s): Olfactory bulb

Cell Type(s): Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron granule MC GABA cell

Model Concept(s): Neurogenesis; Synaptic Plasticity; Temporal Pattern Generation; Activity Patterns; Learning; Gamma oscillations; STDP; Coincidence Detection; Delay; Hebbian plasticity; Memory; Olfaction; Oscillations; Pattern Recognition; Synchronization

Simulation Environment: Python

Implementer(s): Imam, Nabil ; Cleland, Thomas [tac29 at]


Imam N, Cleland TA. (2020). Rapid online learning and robust recall in a neuromorphic olfactory circuit Nature Machine Intelligence. 2

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