A computational model of the response of honey bee antennal lobe circuitry to odor mixtures: Overshadowing, blocking and unblocking can arise from lateral inhibition

Christiane Linster, Brian H. Smith

Research output: Contribution to journalArticlepeer-review

64 Scopus citations

Abstract

Recent studies of learning about elements of odorant mixtures in honey bees have identified several types of interactions between mixture components, such as overshadowing and blocking. The latter phenomenon in particular indicates at least a limited ability of subjects to identify the most salient element of a binary mixture. Here we show that the circuitry in the antennal lobes, the first neuropil in which synaptic interaction affects sensory processing, could give rise to both effects given the incorporation of modifiable synapses onto inhibitory circuitry. The neural model of the antennal lobe that we present incorporates identified cell types and includes a biologically realistic modulatory neuron with which modifiable Hebb-like synaptic interactions take place. A learning rule that incorporates modifiable connections from output (projection) neurons onto the modulatory neuron is sufficient to account for behavioral results on generalization and overshadowing. A second type of excitatory connection from the modulatory neuron onto local inhibitory interneurons is necessary to reproduce behavioral results from blocking and unblocking. We suggest that the neural representations of odor mixtures in the antennal lobe can be modified by previous exposure to one of the mixture components. These results provide testable hypotheses that will guide future behavioral and physiological analyses.

Original languageEnglish (US)
Pages (from-to)1-14
Number of pages14
JournalBehavioural Brain Research
Volume87
Issue number1
DOIs
StatePublished - Aug 1997
Externally publishedYes

Keywords

  • Antennal lobe
  • Associative learning
  • Blocking
  • Computational model
  • Honey bee
  • Mixtures
  • Olfaction
  • Overshadowing

ASJC Scopus subject areas

  • Behavioral Neuroscience

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