A large-scale model of the locust antennal lobe

Mainak Patel, Aaditya V. Rangan, David Cai

Research output: Contribution to journalArticlepeer-review

Abstract

The antennal lobe (AL) is the primary structure within the locust's brain that receives information from olfactory receptor neurons (ORNs) within the antennae. Different odors activate distinct subsets of ORNs, implying that neuronal signals at the level of the antennae encode odors combinatorially. Within the AL, however, different odors produce signals with long-lasting dynamic transients carried by overlapping neural ensembles, suggesting a more complex coding scheme. In this work we use a large-scale point neuron model of the locust AL to investigate this shift in stimulus encoding and potential consequences for odor discrimination. Consistent with experiment, our model produces stimulus-sensitive, dynamically evolving populations of active AL neurons. Our model relies critically on the persistence time-scale associated with ORN input to the AL, sparse connectivity among projection neurons, and a synaptic slow inhibitory mechanism. Collectively, these architectural features can generate network odor representations of considerably higher dimension than would be generated by a direct feed-forward representation of stimulus space.

Original languageEnglish (US)
Pages (from-to)553-567
Number of pages15
JournalJournal of Computational Neuroscience
Volume27
Issue number3
DOIs
StatePublished - 2009

Keywords

  • Linear discriminability
  • Principal component analysis

ASJC Scopus subject areas

  • Sensory Systems
  • Cognitive Neuroscience
  • Cellular and Molecular Neuroscience

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