Narrow versus wide tuning curves: What's best for a population code? Export

@article{citeulike:612667, abstract = {Neurophysiologists are often faced with the problem of evaluating the quality of a code for a sensory or motor variable, either to relate it to the performance of the animal in a simple discrimination task or to compare the codes at various stages along the neuronal pathway. One common belief that has emerged from such studies is that sharpening of tuning curves improves the quality of the code, although only to a certain point; sharpening beyond that is believed to be harmful. We show that this belief relies on either problematic technical analysis or improper assumptions about the noise. We conclude that one cannot tell, in the general case, whether narrow tuning curves are better than wide ones; the answer depends critically on the covariance of the noise. The same conclusion applies to other manipulations of the tuning curve profiles such as gain increase.}, address = {Georgetown Institute for Cognitive and Computational Sciences, New Research Building, Room EP04, 3970 Reservoir Road NW, Washington DC 20007, USA. alex@giccs.georgetown.edu}, author = {Pouget, A. and Deneve, S. and Ducom, J. C. and Latham, P. E.}, citeulike-article-id = {612667}, citeulike-linkout-0 = {http://neco.mitpress.org/cgi/content/abstract/11/1/85}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/9950723}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=9950723}, day = {1}, issn = {0899-7667}, journal = {Neural Comput}, keywords = {coding, population, tuning, variability}, month = {January}, number = {1}, pages = {85--90}, posted-at = {2007-09-26 18:41:08}, priority = {2}, title = {Narrow versus wide tuning curves: What's best for a population code?}, url = {http://neco.mitpress.org/cgi/content/abstract/11/1/85}, volume = {11}, year = {1999} }

TY - JOUR ID - citeulike:612667 L3 - citeulike-article-id:612667 N2 - Neurophysiologists are often faced with the problem of evaluating the quality of a code for a sensory or motor variable, either to relate it to the performance of the animal in a simple discrimination task or to compare the codes at various stages along the neuronal pathway. One common belief that has emerged from such studies is that sharpening of tuning curves improves the quality of the code, although only to a certain point; sharpening beyond that is believed to be harmful. We show that this belief relies on either problematic technical analysis or improper assumptions about the noise. We conclude that one cannot tell, in the general case, whether narrow tuning curves are better than wide ones; the answer depends critically on the covariance of the noise. The same conclusion applies to other manipulations of the tuning curve profiles such as gain increase. IS - 1 JF - Neural Comput SN - 0899-7667 AD - Georgetown Institute for Cognitive and Computational Sciences, New Research Building, Room EP04, 3970 Reservoir Road NW, Washington DC 20007, USA. alex@giccs.georgetown.edu EP - 90 TI - Narrow versus wide tuning curves: What's best for a population code? VL - 11 SP - 85 KW - coding KW - population KW - tuning KW - variability AU - Pouget, A AU - Deneve, S AU - Ducom, JC AU - Latham, PE PY - 1999/01/01/ UR - http://neco.mitpress.org/cgi/content/abstract/11/1/85 ER -