Noise in neurons is message dependent.
Neuronal responses are conspicuously variable. We focus on one particular aspect of that variability: the precision of action potential timing. We show that for common models of noisy spike generation, elementary considerations imply that such variability is a function of the input, and can be made arbitrarily large or small by a suitable choice of inputs. Our considerations are expected to extend to virtually any mechanism of spike generation, and we illustrate them with data from the visual pathway. Thus, a simplification usually made in the application of information theory to neural processing is violated: noise is not independent of the message. However, we also show the existence of error-correcting topologies, which can achieve better timing reliability than their components.
Cecchi
G A
GA
Center for Studies in Physics and Biology, The Rockefeller University, New York, NY 10021, USA. guille@tlon.rockefeller.edu
Sigman
M
M
Alonso
J M
JM
Martínez
L
L
Chialvo
D R
DR
Magnasco
M O
MO
D001921:Brain / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D009622:Noise; D013997:Time Factors