Modelling spatiotemporal olfactory data in two steps: From binary to Hodgkin-Huxley neurones

Brigitte Quenet, Rémi Dubois, Sevan Sirapian, Gérard E. Dreyfus, David Horn

Research output: Contribution to journalArticlepeer-review

Abstract

Network models of synchronously updated McCulloch-Pitts neurones exhibit complex spatiotemporal patterns that are similar to activities of biological neurones in phase with a periodic local field potential, such as those observed experimentally by Wehr and Laurent (1996, Nature 384, 162-166) in the locust olfactory pathway. Modelling biological neural nets with networks of simple formal units makes the dynamics of the model analytically tractable. It is thus possible to determine the constraints that must be satisfied by its connection matrix in order to make its neurones exhibit a given sequence of activity (see, for instance, Quenet et al., 2001, Neurocomputing 38-40, 831-836). In the present paper, we address the following question: how can one construct a formal network of Hodgkin-Huxley (HH) type neurones that reproduces experimentally observed neuronal codes? A two-step strategy is suggested in the present paper: first, a simple network of binary units is designed, whose activity reproduces the binary experimental codes; second, this model is used as a guide to design a network of more realistic formal HH neurones. We show that such a strategy is indeed fruitful: it allowed us to design a model that reproduces the Wehr-Laurent olfactory codes, and to investigate the robustness of these codes to synaptic noise.

Original languageEnglish
Pages (from-to)203-211
Number of pages9
JournalBioSystems
Volume67
Issue number1-3
DOIs
StatePublished - Oct 2002

Keywords

  • Formal neural network
  • Hodgkin-Huxley model
  • Hopfield-Little dynamics
  • Neural coding
  • Olfactory system
  • Spatiotemporal patterns

Fingerprint

Dive into the research topics of 'Modelling spatiotemporal olfactory data in two steps: From binary to Hodgkin-Huxley neurones'. Together they form a unique fingerprint.

Cite this