Machine Learning
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Learning classification in the olfactory system of insects
Neural Computation
Attractor Networks for Shape Recognition
Neural Computation
A Sparse and Locally Shift Invariant Feature Extractor Applied to Document Images
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Self-organization in the olfactory system: one shot odor recognition in insects
Biological Cybernetics
A categorizing associative memory using an adaptive classifier and sparse coding
IEEE Transactions on Neural Networks
Modeling the manifolds of images of handwritten digits
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Engineering Applications of Artificial Intelligence
A model of non-elemental olfactory learning in Drosophila
Journal of Computational Neuroscience
Inhibition in multiclass classification
Neural Computation
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We propose a model for pattern recognition in the insect brain. Departing from a well-known body of knowledge about the insect brain, we investigate which of the potentially present features may be useful to learn input patterns rapidly and in a stable manner. The plasticity underlying pattern recognition is situated in the insect mushroom bodies and requires an error signal to associate the stimulus with a proper response. As a proof of concept, we used our model insect brain to classify the well-known MNIST database of handwritten digits, a popular benchmark for classifiers. We show that the structural organization of the insect brain appears to be suitable for both fast learning of new stimuli and reasonable performance in stationary conditions. Furthermore, it is extremely robust to damage to the brain structures involved in sensory processing. Finally, we suggest that spatiotemporal dynamics can improve the level of confidence in a classification decision. The proposed approach allows testing the effect of hypothesized mechanisms rather than speculating on their benefit for system performance or confidence in its responses.