title: Neural population structures and consequences for neural coding.
abstract: Researchers studying neural coding have speculated that populations of neurons would more effectively represent the stimulus if the neurons "cooperated:" by interacting through lateral connections, the neurons would process and represent information better than if they functioned independently. We apply our new theory of information processing to determine the fidelity limits of simple population structures to encode stimulus features. We focus on noncooperative populations, which have no lateral connections. We show that they always exhibit positively correlated responses and that as population size increases, they perfectly represent the information conveyed by their inputs regardless of the individual neuron's coding scheme. Cooperative populations, which do have lateral connections, can, depending on the nature of the connections, perform better or worse than their noncooperative counterparts. We further show that common notions of synergy fail to capture the level of cooperation and to reflect the information processing properties of populations.
authors.affiliation: Department of Electrical & Computer Engineering, MS 366, Rice University, Houston, Texas, 77251-1892, USA. dhj@rice.edu
authors.forename: Don H
authors.initials: DH
authors.lastname: Johnson
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D001330:Electronic Data Processing; D008959:Models, Neurological; D009474:Neurons / Q000145:classification* / Q000502:physiology*