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Gain control in insect olfaction for efficient odor recognition

This presentation explores the use of gain control in insect olfaction to improve odor recognition efficiency. It covers the basics of pattern recognition, the role of feature extraction and machine learning, and examines the neural networks involved in insect olfaction. The goal is to understand how gain control can be implemented in the antennal lobe (AL) circuitry to enhance odor discrimination.

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Gain control in insect olfaction for efficient odor recognition

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

  2. The goal 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.

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

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

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

  6. Antenna Antennal Lobe (AL) Spatio-temporal coding occurs here No evidence of time here Mushroom body (MB) Machine Learning Stage Feature Extraction Mushroom body lobes

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

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

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

  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, TOTAL # OF ODORS # of active KCs Note: we use Drosophila numbers

  14. It has been shown both inLocust (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. Antenna Antennal Lobe (AL) GAIN CONTROL Mushroom body (MB) Machine Learning Stage Feature Extraction But nobody knows why Mushroom body lobes

  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 The gain control depends only on the inhibitory connections This works if and are linear BUT!

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

  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

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