title: Constructing Precisely Computing Networks with Biophysical Spiking Neurons.
abstract: While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation.
  
  
  CONCLUSIONS
  We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks including irregular, Poisson-like spike times, and a tight balance between excitation and inhibition. These results significantly increase the biological plausibility of the spike-based approach to network computation, and uncover how several components of biological networks may work together to efficiently carry out computation.
authors.affiliation: Mathematical Biosciences Institute, The Ohio State University, Columbus, Ohio 43210, schwemmer.2@mbi.osu.edu.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Schwemmer
authors.affiliation: Departments of Physiology and Biophysics, WRF UW Institute for Neuroengineering and Data Sciences Senior Fellow, University of Washington, Seattle, Washington 98195.
authors.forename: Adrienne L
authors.initials: AL
authors.lastname: Fairhall
authors.affiliation: Laboratoire de Neurosciences Cognitives, INSERM, CNRS, École Normale Supérieure, Paris, France 75005.
authors.forename: Sophie
authors.initials: S
authors.lastname: Denéve
authors.affiliation: Applied Mathematics, and.
authors.forename: Eric T
authors.initials: ET
authors.lastname: Shea-Brown
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D055592:Biophysical Phenomena / Q000502:physiology*; D001703:Biophysics; D003198:Computer Simulation; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D013569:Synapses / Q000502:physiology
keywords: biophysics; computational model; decision-making; integration; spike-based computations