title: Rate and synchrony in feedforward networks of coincidence detectors: analytical solution.
abstract: We provide an analytical recurrent solution for the firing rates and cross-correlations of feedforward networks with arbitrary connectivity, excitatory or inhibitory, in response to steady-state spiking input to all neurons in the first network layer. Connections can go between any two layers as long as no loops are produced. Mean firing rates and pairwise cross-correlations of all input neurons can be chosen individually. We apply this method to study the propagation of rate and synchrony information through sample networks to address the current debate regarding the efficacy of rate codes versus temporal codes. Our results from applying the network solution to several examples support the following conclusions: (1) differential propagation efficacy of rate and synchrony to higher layers of a feedforward network is dependent on both network and input parameters, and (2) previous modeling and simulation studies exclusively supporting either rate or temporal coding must be reconsidered within the limited range of network and input parameters used. Our exact, analytical solution for feedforward networks of coincidence detectors should prove useful for further elucidating the efficacy and differential roles of rate and temporal codes in terms of different network and input parameter ranges.
authors.affiliation: Zanvyl Krieger Mind/Brain Institute and Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21218, USA. mikula@jhu.edu
authors.forename: Shawn
authors.initials: S
authors.lastname: Mikula
authors.forename: Ernst
authors.initials: E
authors.lastname: Niebur
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001288:Attention / Q000502:physiology; D002540:Cerebral Cortex / Q000502:physiology*; D003344:Cortical Synchronization; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009433:Neural Inhibition / Q000502:physiology; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology