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 https://github.com/intel-nrc-ecosystem/models/tree/master/official/epl).
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 cornell.edu]
References:
Imam N, Cleland TA. (2020). Rapid online learning and robust recall in a neuromorphic olfactory circuit Nature Machine Intelligence. 2