Recognition of complex temporal sequences is a general sensory problem that requires integration of information over time. We describe a very simple 'organism' that performs this task, exemplified here by recognition of spoken monosyllables. The network's computation can be understood through the application of simple but generally unexploited principles describing neural activity. The organism is a network of very simple neurons and synapses; the experiments are simulations. The network's recognition capabilities are robust to variations across speakers, simple masking noises, and large variations in system parameters. The network principles underlying recognition of short temporal sequences are applied here to speech, but similar ideas can be applied to aspects of vision, touch, and olfaction. In this article, we describe only properties of the system that could be measured if it were a real biological organism. We delay publication of the principles behind the network's operation as an intellectual challenge: The essential principles of operation can be deduced based on the experimental results presented here alone. An interactive web site (http://neuron.princeton.edu/-moment) is available to allow readers to design and carry out their own experiments on the organism.
|Original language||English (US)|
|Number of pages||6|
|Journal||Proceedings of the National Academy of Sciences of the United States of America|
|State||Published - Dec 5 2000|
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