Simulating odor representations and responses in the olfactory bulb using eligibility propagation (e-prop)

Abstract

Understanding the neural mechanisms underlying odor representation and memory in the olfactory bulb (OB) is critical for advancing our knowledge of sensory processing and neural plasticity. Inspired by in vivo experimental findings [1, 2], we applied the eligibility propagation (e-prop) learning rule [3] to simulate the cholinergic and GABAergic responses to distinct odors. [1, 2] found differential activation patterns in glomeruli of the OB in response to different odor exposures. The modulation of activity by cholinergic and GABAergic projections from the horizontal diagonal band (HDB) was shown to enhance odor discrimination by either amplifying or suppressing glomerular responses.

In our simulation, we utilized a recurrent spiking neural network (SNN) model implemented in the NEST simulator [4], incorporating both regular and adaptive leaky integrate-and-fire (LIF) neurons. The applied e-prop learning rule facilitates synaptic plasticity through local eligibility traces modulated by feedback signals. This approach allowed us to replicate the distinct patterns of glomerular activity observed during the presentation of two odors, as well as the modulatory effects of cholinergic and GABAergic inputs.

References:
[1] Inna Schwarz and Monika Müller, Irina Pavlova, Jens Schweihoff, Fabrizio Musacchio, Manuel Mittag, Martin Fuhrmann, Martin K. Schwarz, The diagonal band of Broca regulates olfactory-mediated behaviors by modulating odor-evoked responses within the olfactory bulb, 2021, bioRxiv 2020.11.07.372649, doi: https://doi.org/10.1101/2020.11.07.372649
[2] Monika Müller, Odor-evoked modulation of the olfactory bulb by projections from the horizontal limb of the diagonal band of Broca, Bonn, 2021, Dissertation, Rheinische Friedrich-Wilhelms-Universität Bonn. Online-Ausgabe in bonndoc: https://nbn-resolving.org/urn:nbnhbz:5-63028
[3] Guillaume Bellec, Franz Scherr, Anand Subramoney, Elias Hajek, Darjan Salaj, Robert Legenstein, Wolfgang Maass, A solution to the learning dilemma for recurrent networks of spiking neurons, 2020, Nature Communications, Vol. 11, Issue 1, pages n/a, doi: 10.1038/s41467-020-17236-y
[4] Marc-Oliver Gewaltig and Markus Diesmann, NEST (NEural Simulation Tool), 2007 Scholarpedia, 2(4), 1430, doi: https://doi.org/10.4249/scholarpedia.1430