Noise shaping in populations of coupled model neurons.
Biological information-processing systems, such as populations of sensory and motor neurons, may use correlations between the firings of individual elements to obtain lower noise levels and a systemwide performance improvement in the dynamic range or the signal-to-noise ratio. Here, we implement such correlations in networks of coupled integrate-and-fire neurons using inhibitory coupling and demonstrate that this can improve the system dynamic range and the signal-to-noise ratio in a population rate code. The improvement can surpass that expected for simple averaging of uncorrelated elements. A theory that predicts the resulting power spectrum is developed in terms of a stochastic point-process model in which the instantaneous population firing rate is modulated by the coupling between elements.
Mar
D J
DJ
Center for BioDynamics and Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA. djmar@bu.edu
Chow
C C
CC
Gerstner
W
W
Adams
R W
RW
Collins
J J
JJ
D008433:Mathematics; D008959:Models, Neurological; D009046:Motor Neurons / Q000502:physiology; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D009475:Neurons, Afferent / Q000502:physiology; D011930:Reaction Time; D013269:Stochastic Processes