---
_id: '8023'
abstract:
- lang: eng
  text: 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.
article_processing_charge: No
article_type: original
author:
- first_name: Christian
  full_name: Tomm, Christian
  last_name: Tomm
- first_name: Michael
  full_name: Avermann, Michael
  last_name: Avermann
- first_name: Carl
  full_name: Petersen, Carl
  last_name: Petersen
- first_name: Wulfram
  full_name: Gerstner, Wulfram
  last_name: Gerstner
- first_name: Tim P
  full_name: Vogels, Tim P
  id: CB6FF8D2-008F-11EA-8E08-2637E6697425
  last_name: Vogels
  orcid: 0000-0003-3295-6181
citation:
  ama: Tomm C, Avermann M, Petersen C, Gerstner W, Vogels TP. Connection-type-specific
    biases make uniform random network models consistent with cortical recordings.
    <i>Journal of Neurophysiology</i>. 2014;112(8):1801-1814. doi:<a href="https://doi.org/10.1152/jn.00629.2013">10.1152/jn.00629.2013</a>
  apa: Tomm, C., Avermann, M., Petersen, C., Gerstner, W., &#38; Vogels, T. P. (2014).
    Connection-type-specific biases make uniform random network models consistent
    with cortical recordings. <i>Journal of Neurophysiology</i>. American Physiological
    Society. <a href="https://doi.org/10.1152/jn.00629.2013">https://doi.org/10.1152/jn.00629.2013</a>
  chicago: Tomm, Christian, Michael Avermann, Carl Petersen, Wulfram Gerstner, and
    Tim P Vogels. “Connection-Type-Specific Biases Make Uniform Random Network Models
    Consistent with Cortical Recordings.” <i>Journal of Neurophysiology</i>. American
    Physiological Society, 2014. <a href="https://doi.org/10.1152/jn.00629.2013">https://doi.org/10.1152/jn.00629.2013</a>.
  ieee: C. Tomm, M. Avermann, C. Petersen, W. Gerstner, and T. P. Vogels, “Connection-type-specific
    biases make uniform random network models consistent with cortical recordings,”
    <i>Journal of Neurophysiology</i>, vol. 112, no. 8. American Physiological Society,
    pp. 1801–1814, 2014.
  ista: Tomm C, Avermann M, Petersen C, Gerstner W, Vogels TP. 2014. Connection-type-specific
    biases make uniform random network models consistent with cortical recordings.
    Journal of Neurophysiology. 112(8), 1801–1814.
  mla: Tomm, Christian, et al. “Connection-Type-Specific Biases Make Uniform Random
    Network Models Consistent with Cortical Recordings.” <i>Journal of Neurophysiology</i>,
    vol. 112, no. 8, American Physiological Society, 2014, pp. 1801–14, doi:<a href="https://doi.org/10.1152/jn.00629.2013">10.1152/jn.00629.2013</a>.
  short: C. Tomm, M. Avermann, C. Petersen, W. Gerstner, T.P. Vogels, Journal of Neurophysiology
    112 (2014) 1801–1814.
date_created: 2020-06-25T13:08:30Z
date_published: 2014-10-15T00:00:00Z
date_updated: 2021-01-12T08:16:35Z
day: '15'
ddc:
- '570'
doi: 10.1152/jn.00629.2013
extern: '1'
external_id:
  pmid:
  - '24944218'
file:
- access_level: open_access
  checksum: 7c06a086da6f924342650de6dc555c3f
  content_type: application/pdf
  creator: cziletti
  date_created: 2020-07-16T10:12:13Z
  date_updated: 2020-07-16T10:12:13Z
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  file_name: 2014_JNeurophysiol_Tomm.pdf
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file_date_updated: 2020-07-16T10:12:13Z
fulldoi: https://doi.org/10.1152/jn.00629.2013
has_accepted_license: '1'
intvolume: '       112'
issue: '8'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/3.0/
month: '10'
oa: 1
oa_version: Published Version
page: 1801-1814
pmid: 1
publication: Journal of Neurophysiology
publication_identifier:
  eissn:
  - 1522-1598
  issn:
  - 0022-3077
publication_status: published
publisher: American Physiological Society
quality_controlled: '1'
status: public
title: Connection-type-specific biases make uniform random network models consistent
  with cortical recordings
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/3.0/legalcode
  name: Creative Commons Attribution 3.0 Unported (CC BY 3.0)
  short: CC BY (3.0)
type: journal_article
user_id: D865714E-FA4E-11E9-B85B-F5C5E5697425
volume: 112
year: '2014'
...