Volume 16 Supplement 1
Dynamical sensory representations establish a rapid odor code in a spiking model of the insect olfactory system
© Betkiewicz et al. 2015
Published: 18 December 2015
In their natural environment, animals sense and evaluate olfactory cues of time-varying composition and concentration. Their olfactory pathways are adapted to the natural stimulus statistics, thus it is not surprising that odor processing is fast . Honey bees, for example, learn to discriminate odors presented as short as 200 ms . The neural odor code in these animals emerges within 50ms after stimulus onset and neural representation changes dynamically during and after an odorant is present [1, 3]. How is the insect olfactory system optimized to reliably estimate spatial and temporal aspects of the olfactory environment and what are the mechanisms behind rapid odor processing?
Our model displays sparse and robust odor representation in the Mushroom Body . Typically, less than 10% of the Kenyon Cell population is activated by an odor, with only 2-3 spikes at the odor onset (Figure 1A). KC spikes establish a rapid odor identity code at stimulus onset, while intrinsic adaptation currents provide a persistent and prolonged odor trace (Figure 1B). Our approach allows us to investigate dynamical changes in odor representations and predict odor after images.
BMBF grant 01GQ0941 Insect Inspired Robots within the Bernstein Focus Learning and Memory (BFNL). Research Training Group Sensory Computation in Neural Systems (GRK 1589) funded by the German DFG.
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