dorsal/arxiv
View SchemaHow behavioral constraints may determine optimal sensory representations
| Authors | Emilio Salinas |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0612036 |
| URL | https://arxiv.org/abs/q-bio/0612036 |
| DOI | 10.1371/journal.pbio.0040387 |
| Journal | Salinas E (2006) How behavioral constraints may determine optimal sensory representations. PLoS Biol 4(12): e387 |
Abstract
The sensory-triggered activity of a neuron is typically characterized in terms of a tuning curve, which describes the neuron's average response as a function of a parameter that characterizes a physical stimulus. What determines the shapes of tuning curves in a neuronal population? Previous theoretical studies and related experiments suggest that many response characteristics of sensory neurons are optimal for encoding stimulus-related information. This notion, however, does not explain the two general types of tuning profiles that are commonly observed: unimodal and monotonic. Here, I quantify the efficacy of a set of tuning curves according to the possible downstream motor responses that can be constructed from them. Curves that are optimal in this sense may have monotonic or non-monotonic profiles, where the proportion of monotonic curves and the optimal tuning curve width depend on the general properties of the target downstream functions. This dependence explains intriguing features of visual cells that are sensitive to binocular disparity and of neurons tuned to echo delay in bats. The numerical results suggest that optimal sensory tuning curves are shaped not only by stimulus statistics and signal-to-noise properties, but also according to their impact on downstream neural circuits and, ultimately, on behavior.
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"abstract": "The sensory-triggered activity of a neuron is typically characterized in\nterms of a tuning curve, which describes the neuron\u0027s average response as a\nfunction of a parameter that characterizes a physical stimulus. What determines\nthe shapes of tuning curves in a neuronal population? Previous theoretical\nstudies and related experiments suggest that many response characteristics of\nsensory neurons are optimal for encoding stimulus-related information. This\nnotion, however, does not explain the two general types of tuning profiles that\nare commonly observed: unimodal and monotonic. Here, I quantify the efficacy of\na set of tuning curves according to the possible downstream motor responses\nthat can be constructed from them. Curves that are optimal in this sense may\nhave monotonic or non-monotonic profiles, where the proportion of monotonic\ncurves and the optimal tuning curve width depend on the general properties of\nthe target downstream functions. This dependence explains intriguing features\nof visual cells that are sensitive to binocular disparity and of neurons tuned\nto echo delay in bats. The numerical results suggest that optimal sensory\ntuning curves are shaped not only by stimulus statistics and signal-to-noise\nproperties, but also according to their impact on downstream neural circuits\nand, ultimately, on behavior.",
"arxiv_id": "q-bio/0612036",
"authors": [
"Emilio Salinas"
],
"categories": [
"q-bio.NC"
],
"doi": "10.1371/journal.pbio.0040387",
"journal_ref": "Salinas E (2006) How behavioral constraints may determine optimal\n sensory representations. PLoS Biol 4(12): e387",
"title": "How behavioral constraints may determine optimal sensory representations",
"url": "https://arxiv.org/abs/q-bio/0612036"
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