Pattern recognition with a simple olfactory bulb model

The olfactory system is a remarkable system capable of discriminating between very similar odorant mixtures. It is thought that this function is somewhat related with spatiotemporal activity patterns generated in mitral and tufted (M/T) cells of the olfactory bulb (OB) during odor presentation (1). These spatiotemporal patterns are in part controlled by reciprocal dendrodendritic synapses between M/T cells and granule cells (GCs). The spatial aspect of these patterns would be provided by contrast enhancement mechanisms generated by GC-mediated lateral inhibition, whereas the temporal aspect would result from synchronizing and desynchronizing processes conveyed by reciprocal feedback interactions between M/T cells and GCs (2). The efficient coding scheme for high-dimensional patterns used by the OB could be explored by artificial pattern recognition systems. Inspired by this, we developed a spiking neural network OB model and present here test results from its application to recognition tasks involving patterns taken from both artificial and real databases. The model consists of a simplified version of OB containing only mitral cells (MCs) and GCs. These two cell types were modeled according to the Izhikevich formalism (3). The two cell populations were arranged into two square arrays with the same size. The GC array has four times the number of cells in the MC array. The number of cells in the MC array depends on the dimension of the input space. The probability of a synaptic contact between a MC and a GC was given by P(d) = exp[−(d/λ)²], where d is the horizontal distance between them and λ is a parameter. Signal propagation times were neglected. For each established contact between MC and GC grids two synapses were created: one excitatory from the MC to the GC, and one inhibitory from the GC to the MC. These synapses were modeled as alpha functions with short-term facilitation and depression as in the dynamic synapse model (4). The output of the system is a vector with the same dimension as the number of cells in the MC array. The components of this vector are the MCs firing rates. The system thus transforms input patterns into spatiotemporal output patterns determined by its intrinsic connectivity and synaptic parameters. These dynamic output patterns can be transformed into stable states using readout modules (5). Virtually any type of input data can be fed into the system, perhaps requiring some pre-processing. We tested our system with five datasets: two were artificially created by us and are composed of spatial patterns with different overlap degrees, and three were taken from the UCI repository (6). These latter were “BC” (breast cancer), “Heart” and “Parkinsons”. These three datasets have instances with missing attributes – especially Heart –, which makes the classification task hard. All input patterns were converted to spike trains using a noisy integrate-and-fire model. The performance of the system was evaluated with respect to two parameters: λ and a synaptic weight parameter. Results for the artificial patterns show that there is an optimal range of parameters for which the system discriminates well patterns with high overlap degree. For strong synaptic weights the distance measure used to discriminate between patterns oscillates in time so that a threshold detector can be used as readout. Good classification performances for the three real-world datasets were obtained with the use of a parallel perceptron as readout. Without fine-tuning of the Parallel Perceptron parameters, classification rates for the three datasets were above 75% for BC and Heart and above 70% for Parkinsons.

 

Support: ACR is supported by a CNPq research grant. Work also supported by INCeMaq.

 

References

1.      Lledo, P.M., Gheusi, G., and Vincent, J.D. (2005). Information processing in the mammalian olfactory system. Physiol. Rev. 85, 281-317.

2.      Cleland, T.A., and Linster, C. (2005). Computation in the olfactory system. Chem. Senses 30, 801-813.

3.      Izhikevich, E.M. (2007) Dynamical Systems in Neuroscience: the geometry of excitability and bursting. Cambridge, MA: MIT Press.

4.      Tsodyks, M., Pawelzik, K., and Markran, H. (1998). Neural networks with dynamic synapses. Neural Comput. 10, 821-835.

5.      Maas, W., Natschläger, T., and Markran, H. (2002). Real-time computing with stable states: a new framework for neural computation based on perturbations. Neural Comput. 14, 2531-2560.

6.      Frank, A., and Asuncion, A. (2010). UCI Machine Learning Repository. http://archive.ics.uci.edu/ml. Irvine, CA: University of California, School of Information and Computer Science.