@incollection{marderModelingSmallNetworks1998,
  title = {Modeling Small Networks},
  booktitle = {Methods in Neuronal Modeling: From Ions to Networks},
  author = {Marder, Eve and Abbott, L F},
  editor = {Koch, Christof and Segev, Idan},
  year = {1998},
  series = {Computational Neuroscience},
  edition = {2nd ed},
  publisher = {{MIT Press}},
  address = {{Cambridge, Mass}},
  isbn = {978-0-262-11231-4},
  lccn = {QP363.3 .M46 1998},
  keywords = {Computer simulation,Nervous system,Neural networks (Neurobiology),Neurons},
  file = {/home/alex/Zotero/storage/U46DZGH4/Abbott and Marder - MODELING SMALL NETWORKS.pdf}
}

@article{prinzAlternativeHandTuningConductanceBased2003,
  title = {Alternative to {{Hand-Tuning Conductance-Based Models}}: {{Construction}} and {{Analysis}} of {{Databases}} of {{Model Neurons}}},
  shorttitle = {Alternative to {{Hand-Tuning Conductance-Based Models}}},
  author = {Prinz, Astrid A. and Billimoria, Cyrus P. and Marder, Eve},
  year = {2003},
  month = dec,
  journal = {Journal of Neurophysiology},
  volume = {90},
  number = {6},
  pages = {3998--4015},
  publisher = {{American Physiological Society}},
  issn = {0022-3077},
  doi = {10.1152/jn.00641.2003},
  urldate = {2023-03-13},
  abstract = {Conventionally, the parameters of neuronal models are hand-tuned using trial-and-error searches to produce a desired behavior. Here, we present an alternative approach. We have generated a database of about 1.7 million single-compartment model neurons by independently varying 8 maximal membrane conductances based on measurements from lobster stomatogastric neurons. We classified the spontaneous electrical activity of each model neuron and its responsiveness to inputs during runtime with an adaptive algorithm and saved a reduced version of each neuron's activity pattern. Our analysis of the distribution of different activity types (silent, spiking, bursting, irregular) in the 8-dimensional conductance space indicates that the coarse grid of conductance values we chose is sufficient to capture the salient features of the distribution. The database can be searched for different combinations of neuron properties such as activity type, spike or burst frequency, resting potential, frequency\textendash current relation, and phase-response curve. We demonstrate how the database can be screened for models that reproduce the behavior of a specific biological neuron and show that the contents of the database can give insight into the way a neuron's membrane conductances determine its activity pattern and response properties. Similar databases can be constructed to explore parameter spaces in multicompartmental models or small networks, or to examine the effects of changes in the voltage dependence of currents. In all cases, database searches can provide insight into how neuronal and network properties depend on the values of the parameters in the models.},
  file = {/home/alex/Zotero/storage/MDRM7IIF/Prinz et al_2003_Alternative to Hand-Tuning Conductance-Based Models.pdf}
}

@article{prinzSimilarNetworkActivity2004,
  title = {Similar Network Activity from Disparate Circuit Parameters},
  author = {Prinz, Astrid A and Bucher, Dirk and Marder, Eve},
  year = {2004},
  month = dec,
  journal = {Nature Neuroscience},
  volume = {7},
  number = {12},
  pages = {1345--1352},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/nn1352},
  urldate = {2018-11-02},
  langid = {english},
  file = {/home/alex/Zotero/storage/CYIQUILT/Prinz et al. - 2004 - Similar network activity from disparate circuit pa.pdf}
}
