1. Gain control in insect olfaction for efficient odor recognition Ramón Huerta Institute for Nonlinear Science UCSD

2. What is time and dynamics buying us for pattern recognition purposes? One way to tackle it 1. Start from the basics of pattern recognition: organization, connectivity, etc.. 2. See when dynamics (time) is required. The goal

3. How does an engineer address a pattern recognition problem? 1.Feature extraction. For example: edges, shapes, textures, etc… 2.Machine learning. For example: ANN, RBF, SVM, Fisher, etc.. What is easy ? What is difficult? 1.Feature extraction: very difficult (cooking phase) 2.Machine learning: very easy (automatic phase)

4. Feature Extraction High divergence-convergence ratios from layer to layer. Antennal Lobe (AL) Mushroom body (MB) Antenna Mushroom body lobes Location of learning How insects appear to do it Machine Learning Stage

5. Bad news The feature extraction stage is mostly genetically prewired Good news The machine learning section seems to be “plastic”

6. Feature Extraction Antennal Lobe (AL) Mushroom body (MB) Antenna Mushroom body lobes Machine Learning Stage Spatio-temporal coding occurs hereNo evidence of time here

7. The basic question Can we implement a learning machine with fan-in, fan-out connectivities, the proportion of neurons, local synaptic plasticity, and inhibition? Huerta et al, Neural Computation 16(8) 1601-1640 (2004)

8. Marr, D. (1969). A theory of cerebellar cortex. J. Physiol., 202:437- 470. Marr, D. (1970). A theory for cerebral neocortex. Proceedings of the Royal Society of London B, 176:161-234. Marr, D. (1971). Simple memory: a theory for archicortex. Phil. Trans. Royal Soc. London, 262:23-81. Willshaw D, Buneman O P, & Longuet-Higgins, HC (1969) Non-holographic associative memory, Nature 222:960

9. CALYX Display Layer Intrinsic Kenyon Cells PNs (~800)iKC(~50000)eKC(100?) AL MB lobes Decision layer Extrinsic Kenyon Cells No learning required Learning required k-winner- take-all Stage I: Transformation into a large display Stage II: Learning “perception” of odors

10. KCs coordinates 1 0 0 0 1 0 1 1 Class 1Class 2 1 0 1 AL coordinates Hyperplane: Connections from the KCs to MB lobes MB lobe neuron: decision

11. Odor classification Odor 4 Odor 3 Odor 2 Odor 1 Odor N Class 1 Class 2

12. Sparse code Probability of discrimination # of active KCs

13. Capacity for discriminating We look for maximum number of odors that can be discriminated for different activate KCs, Note: we use Drosophila numbers # of active KCs TOTAL # OF ODORS

14. It has been shown both in Locust (Laurent) and Honeybee (Menzel) the existence of sparse code ~1% activity

15. Narrow areas of sparse activity Without GAIN CONTROL There can be major FAILURE

16. Feature Extraction Antennal Lobe (AL) Mushroom body (MB) Antenna Mushroom body lobes Machine Learning Stage GAIN CONTROL But nobody knows why

17. Evidence for gain control in the AL These neurons can fire up to100 Hz The baseline firing rate is 3-4Hz Data from Mark Stopfer, Vivek Jayaraman and Gilles Laurent

18. Honeybee: Galizia’s group There seems to be local GABA circuits in the MBs. Locust and honeybee circuits are different: Honeybee 10 times more inhibitory neurons than locust

19. Let’s concentrate on the locust problem: How do we design the AL circuit such that it has gain control?

20. Mean field of 4 populations of neurons

21. We apply mean field

22. Define new set of variables To obtain the mean field eq. Where we use

23. We look for the condition such that Whose condition is: with and This works if and are linear BUT! The gain control depends only on the inhibitory connections

24. The excitatory neurons are not at high spiking frequencies or silent, but but not very high (3-4) Hz. So SIMULATIONS: 400 Neurons

25. The gain control condition from the MF can be estimated as

26. A few conclusions: Gain control can be implemented in the AL network It can be controlled by the inhibitory connectivity. The rest of the parameters are free. Things to do: I do not know whether under different odor intensities the AL representation is the same.

27. Thanks to Marta Garcia-Sanchez Loig Vaugier Thomas Nowotny Misha Rabinovich Vivek Jayaraman Ofer Mazor Gilles Laurent