Population models of temporal differentiation (Tripp and Eliasmith 2010)

 Download zip file 
Help downloading and running models
"Temporal derivatives are computed by a wide variety of neural circuits, but the problem of performing this computation accurately has received little theoretical study. Here we systematically compare the performance of diverse networks that calculate derivatives using cell-intrinsic adaptation and synaptic depression dynamics, feedforward network dynamics, and recurrent network dynamics. Examples of each type of network are compared by quantifying the errors they introduce into the calculation and their rejection of high-frequency input noise. ..."
1 . Tripp BP, Eliasmith C (2010) Population models of temporal differentiation. Neural Comput 22:621-59 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Gap Junctions:
Receptor(s): AMPA; Gaba;
Simulation Environment: Nengo;
Model Concept(s): Temporal Pattern Generation; Simplified Models;
Implementer(s): Tripp, Bryan [bryan.tripp at mail.mcgill.ca]; Eliasmith, Chris [celiasmith at uwaterloo.ca];
Search NeuronDB for information about:  AMPA; Gaba;

This package contains the simulation software for 

Tripp & Eliasmith (2010) Population models of temporal
differentiation, Neural Computation. 22(3):621-59

This software is not self-contained -- it runs within the Nengo
simulation environment (www.nengo.ca). The enclosed code consists of
Java classes, which contain the models, and Python scripts, which
automate loading and simulation of the models.

Browsing The Code

If you just want to see precisely how we did something, you may want
to read the Java code. This code is based on the Nengo API, which is
documented at http://nengo.ca/javadoc/index.html A good place to start
is the class com.bptripp.diff.DifferentiatorNetwork, which is the
parent class for all of the models.

Running the Code

1) Download and install Nengo from www.nengo.ca
2) Add the enclosed diff.jar (which contains compiled versions of the
Java classes) to the "plugins" directory under the Nengo install.
3) Start Nengo. 
4) Open the Python script console within Nengo, and type "run
[path]loadNetworks.py" where [path] is the path to your copy of the
Python scripts.

You may then run other scripts of interest, e.g. simulations.py.


If you run into difficulties, please do not hesitate to contact Bryan
Tripp (bptripp at gmail.com).

Loading data, please wait...