dorsal/arxiv
View SchemaSensory Coding with Dynamically Competitive Networks
| Authors | M. I. Rabinovich, R. Huerta, A. Volkovskii, Henry D. I. Abarbanel, G. Laurent |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0309033 |
| URL | https://arxiv.org/abs/q-bio/0309033 |
Abstract
Studies of insect olfactory processing indicate that odors are represented by rich spatio-temporal patterns of neural activity. These patterns are very difficult to predict a priori, yet they are stimulus specific and reliable upon repeated stimulation with the same input. We formulate here a theoretical framework in which we can interpret these experimental results. We propose a paradigm of ``dynamic competition'' in which inputs (odors) are represented by internally competing neural assemblies. Each pattern is the result of dynamical motion within the network and does not involve a ``winner'' among competing possibilities. The model produces spatio-temporal patterns with strong resemblance to those observed experimentally and possesses many of the general features one desires for pattern classifiers: large information capacity, reliability, specific responses to specific inputs, and reduced sensitivity to initial conditions or influence of noise. This form of neural processing may thus describe the organizational principles of neural information processing in sensory systems and go well beyond the observations on insect olfactory processing which motivated its development.
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"abstract": "Studies of insect olfactory processing indicate that odors are represented by\nrich spatio-temporal patterns of neural activity. These patterns are very\ndifficult to predict a priori, yet they are stimulus specific and reliable upon\nrepeated stimulation with the same input. We formulate here a theoretical\nframework in which we can interpret these experimental results. We propose a\nparadigm of ``dynamic competition\u0027\u0027 in which inputs (odors) are represented by\ninternally competing neural assemblies. Each pattern is the result of dynamical\nmotion within the network and does not involve a ``winner\u0027\u0027 among competing\npossibilities. The model produces spatio-temporal patterns with strong\nresemblance to those observed experimentally and possesses many of the general\nfeatures one desires for pattern classifiers: large information capacity,\nreliability, specific responses to specific inputs, and reduced sensitivity to\ninitial conditions or influence of noise. This form of neural processing may\nthus describe the organizational principles of neural information processing in\nsensory systems and go well beyond the observations on insect olfactory\nprocessing which motivated its development.",
"arxiv_id": "q-bio/0309033",
"authors": [
"M. I. Rabinovich",
"R. Huerta",
"A. Volkovskii",
"Henry D. I. Abarbanel",
"G. Laurent"
],
"categories": [
"q-bio.NC"
],
"title": "Sensory Coding with Dynamically Competitive Networks",
"url": "https://arxiv.org/abs/q-bio/0309033"
},
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