---
res:
  bibo_abstract:
  - Uniform random sparse network architectures are ubiquitous in computational neuroscience,
    but the implicit hypothesis that they are a good representation of real neuronal
    networks has been met with skepticism. Here we used two experimental data sets,
    a study of triplet connectivity statistics and a data set measuring neuronal responses
    to channelrhodopsin stimuli, to evaluate the fidelity of thousands of model networks.
    Network architectures comprised three neuron types (excitatory, fast spiking,
    and nonfast spiking inhibitory) and were created from a set of rules that govern
    the statistics of the resulting connection types. In a high-dimensional parameter
    scan, we varied the degree distributions (i.e., how many cells each neuron connects
    with) and the synaptic weight correlations of synapses from or onto the same neuron.
    These variations converted initially uniform random and homogeneously connected
    networks, in which every neuron sent and received equal numbers of synapses with
    equal synaptic strength distributions, to highly heterogeneous networks in which
    the number of synapses per neuron, as well as average synaptic strength of synapses
    from or to a neuron were variable. By evaluating the impact of each variable on
    the network structure and dynamics, and their similarity to the experimental data,
    we could falsify the uniform random sparse connectivity hypothesis for 7 of 36
    connectivity parameters, but we also confirmed the hypothesis in 8 cases. Twenty-one
    parameters had no substantial impact on the results of the test protocols we used.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Christian
      foaf_name: Tomm, Christian
      foaf_surname: Tomm
  - foaf_Person:
      foaf_givenName: Michael
      foaf_name: Avermann, Michael
      foaf_surname: Avermann
  - foaf_Person:
      foaf_givenName: Carl
      foaf_name: Petersen, Carl
      foaf_surname: Petersen
  - foaf_Person:
      foaf_givenName: Wulfram
      foaf_name: Gerstner, Wulfram
      foaf_surname: Gerstner
  - foaf_Person:
      foaf_givenName: Tim P
      foaf_name: Vogels, Tim P
      foaf_surname: Vogels
      foaf_workInfoHomepage: http://www.librecat.org/personId=CB6FF8D2-008F-11EA-8E08-2637E6697425
    orcid: 0000-0003-3295-6181
  bibo_doi: 10.1152/jn.00629.2013
  bibo_issue: '8'
  bibo_volume: 112
  dct_date: 2014^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0022-3077
  - http://id.crossref.org/issn/1522-1598
  dct_language: eng
  dct_publisher: American Physiological Society@
  dct_title: Connection-type-specific biases make uniform random network models consistent
    with cortical recordings@
  fabio_hasPubmedId: '24944218'
...