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The corresponding page is
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Duration-tuned neurons from the inferior colliculus of the big brown bat (Aubie et al. 2009)
 
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Model Information
Model File
Citations
Accession:
144509
dtnet is a generalized neural network simulator written in C++ with an easy to use XML description language to generate arbitrary neural networks and then run simulations covering many different parameter values. For example, you can specify ranges of parameter values for several different connection weights and then automatically run simulations over all possible parameters. Graphing ability is built in as long as the free, open-source, graphing application GLE (http://glx.sourceforge.net/) is installed. Included in the examples folder are simulation descriptions that were used to generate the results in Aubie et al. (2009). Refer to the README file for instructions on compiling and running these examples. The most recent source code can be obtained from GitHub: <a href="https://github.com/baubie/dtnet">https://github.com/baubie/dtnet</a>
Reference:
1 .
Aubie B, Becker S, Faure PA (2009) Computational models of millisecond level duration tuning in neural circuits.
J Neurosci
29
:9255-70
[
PubMed
]
Model Information
(Click on a link to find other models with that property)
Model Type:
Realistic Network;
Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Inferior Colliculus;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
GabaA;
AMPA;
Gene(s):
Transmitter(s):
Gaba;
Glutamate;
Simulation Environment:
C or C++ program;
Model Concept(s):
Coincidence Detection;
Simplified Models;
Delay;
Rebound firing;
Implementer(s):
Aubie, Brandon [aubiebn at mcmaster.ca];
Search NeuronDB
for information about:
GabaA
;
AMPA
;
Gaba
;
Glutamate
;
Download the displayed file
/
baubie-dtnet-bc6b287
examples
m4
src
README
.gitignore
aclocal.m4
AUTHORS
autogen.sh
ChangeLog
configure.ac
COPYING
*
Other models using COPYING:
A Method for Prediction of Receptor Activation in the Simulation of Synapses (Montes et al. 2013)
An attractor network model of grid cells and theta-nested gamma oscillations (Pastoll et al 2013)
Coincidence detection in MSO principal cells (Goldwyn et al. 2019)
Cortical oscillations and the basal ganglia (Fountas & Shanahan 2017)
Deep belief network learns context dependent behavior (Raudies, Zilli, Hasselmo 2014)
Fast Spiking Basket cells (Tzilivaki et al 2019)
Grid cell model with compression effects (Raudies & Hasselmo, 2015)
Hierarchical Gaussian Filter (HGF) model of conditioned hallucinations task (Powers et al 2017)
Hierarchical network model of perceptual decision making (Wimmer et al 2015)
Hippocampal spiking model for context dependent behavior (Raudies & Hasselmo 2014)
Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018)
Inhibitory network bistability explains increased activity prior to seizure onset (Rich et al 2020)
Model of memory linking through memory allocation (Kastellakis et al. 2016)
PyMUS: A Python based Motor Unit Simulator (Kim & Kim 2018)
Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation (Luque et al 2019)
Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation (Luque et al 2019)
depcomp
Doxyfile
INSTALL
install-sh
*
Other models using install-sh:
An electrophysiological model of GABAergic double bouquet cells (Chrysanthidis et al. 2019)
An electrophysiological model of GABAergic double bouquet cells (Chrysanthidis et al. 2019)
An electrophysiological model of GABAergic double bouquet cells (Chrysanthidis et al. 2019)
An electrophysiological model of GABAergic double bouquet cells (Chrysanthidis et al. 2019)
An electrophysiological model of GABAergic double bouquet cells (Chrysanthidis et al. 2019)
An electrophysiological model of GABAergic double bouquet cells (Chrysanthidis et al. 2019)
ltmain.sh
Makefile.am
NEWS
SUBDIRS = src ACLOCAL_AMFLAGS = -I m4 --install
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