@article{10.1371/journal.pcbi.1002711,
author = {Naud, Richard AND Gerstner, Wulfram},
journal = {PLoS Comput Biol},
publisher = {Public Library of Science},
title = {Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram},
year = {2012},
month = {10},
volume = {8},
url = {http://dx.doi.org/10.1371%2Fjournal.pcbi.1002711},
pages = {1-14},
abstract = {Author SummaryHow can information be encoded and decoded in populations of adapting neurons? A quantitative answer to this question requires a mathematical expression relating neuronal activity to the external stimulus, and, conversely, stimulus to neuronal activity. Although widely used equations and models exist for the special problem of relating external stimulus to the action potentials of a single neuron, the analogous problem of relating the external stimulus to the activity of a population has proven more difficult. There is a bothersome gap between the dynamics of single adapting neurons and the dynamics of populations. Moreover, if we ignore the single neurons and describe directly the population dynamics, we are faced with the ambiguity of the adapting neural code. The neural code of adapting populations is ambiguous because it is possible to observe a range of population activities in response to a given instantaneous input. Somehow the ambiguity is resolved by the knowledge of the population history, but how precisely? In this article we use approximation methods to provide mathematical expressions that describe the encoding and decoding of external stimuli in adapting populations. The theory presented here helps to bridge the gap between the dynamics of single neurons and that of populations.

},
number = {10},
doi = {10.1371/journal.pcbi.1002711}
}