A model for spatio-temporal odor representation in the locust antennal lobe

1. A model for spatio-temporal odor representation in the locust antennal lobe • Experimental results (in vivo recordings from locust) • Model of the antennal lobe (how does it work and why can it be important for odor processing) • Fast odor learning in the antennal lobe

2. The locust olfactory circuits In the locust each antenna contains about 50,000 cholinergic olfactory receptor neurons that send their axons to the ipsilateral antennal lobe. Each axon terminates in a few of the approximately 1,000 glomeruli in the locust AL where it forms synapses with the dendrites of GABAergic local neurons (LNs) and cholinergic projection neurons (PNs), which relay information on to the mushroom body.

3. Transient synchronization Individual PNs phase-lock with population oscillations at times that depend on the stimulus. This fine structure to the timing of PN action potentials within the population response is stable over trials and is different for different PNs.

4. Slow temporal patterns Odor stimulation evokes complex temporal patterns consisting of alternating epochs of excitation (characterized by production of sodium spikes) and inhibition (when PN activity is reduced or subthreshold).

5. Antennal lobe model (geometries) We constructed a simplified computational model of the AL that included 90 PNs and 30 LNs based on the Hodgkin-Huxley kinetics for the ionic currents in these cells.

6. Spiking patterns

7. Antennal lobe model (patterns)

8. Transient synchronization

9. Mechanisms of transient synchronization

10. Similar stimuli

11. Stimuli discrimination

12. Mechanisms of transient synchronization The alternation of LN-mediated GABAergic input provides a potential mechanism for PN synchronization and the transient nature of PN synchronization is linked to variations in inhibitory drive from LNs over the duration of a response. The factors contributing to the oscillatory response: • PN-LN excitatory/inhibitory loop The factors contributing to the alternations of LN activity: • Lateral LN-LN inhibition • K(Ca)-dependent LN spikes adaptation • Identity of directly stimulated LNs

13. Difficulties • In vivo picrotoxin application, which blocks inhibitory synapses onto PNs and onto LNs, causes no loss of information in the odor--evoked spike patterns of individual PNs. • Each PN typically fires Na+ spikes during some fraction of the total duration of odor stimulation only.

14. Slow temporal patterns

15. PCT application

16. Fine and slow temporal patterns

17. Misclassification rate Why do two distinct mechanisms for temporal coding coexist?

18. Fast odor learning (in vivo) During initial stimulation with novel odors, the fine temporal structure is missing from PN spike trains; this structure emerged only after repeated or prolonged stimulation (Stopfer & Laurent, 1999).

19. Fast odor learning (model) Results from our model suggest this effect may be caused by partial network disinhibition resulting from weak GABAA synapses between LNs and PNs.

20. Conclusion • The alternation of LN-mediated GABAergic input provides a potential mechanism for PN synchronization and the transient nature of PN synchronization is linked to variations in inhibitory drive from LNs over the duration of a response. • The strength of the inhibitory synapses must fall within a certain range to enable the fine temporal structure characteristic of PN spike synchronization. • The fine temporal structure of PN synchrony significantly increases the ability of the AL neural network to correctly identify similar stimuli. • Fast and slow inhibition in the antennal lobe may create alternative mechanisms for stimuli discrimination. Slow temporal patterning appears critical for preliminary identification of a novel odor. Fine temporal structure, emerged only after repeated or prolonged stimulation, is essential for precise odor identification.