Fast population coding (Huys et al. 2007)

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"Uncertainty coming from the noise in its neurons and the ill-posed nature of many tasks plagues neural computations. Maybe surprisingly, many studies show that the brain manipulates these forms of uncertainty in a probabilistically consistent and normative manner, and there is now a rich theoretical literature on the capabilities of populations of neurons to implement computations in the face of uncertainty. However, one major facet of uncertainty has received comparatively little attention: time. In a dynamic, rapidly changing world, data are only temporarily relevant. Here, we analyze the computational consequences of encoding stimulus trajectories in populations of neurons. ..."
1 . Huys QJ, Zemel RS, Natarajan R, Dayan P (2007) Fast population coding. Neural Comput 19:404-41 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network;
Brain Region(s)/Organism:
Cell Type(s):
Gap Junctions:
Simulation Environment: MATLAB;
Model Concept(s): Methods;

Example code for fast population coding with sparse spike trains. 
This code is released in conjunction with the paper 

	Huys QJM, Zemel RS, Natarajan R and Dayan P (2006): Fast population
	coding. Neural Computation
and can be downloaded from

The paper can be downloaded from

Copyright Quentin Huys 2006


To use the code, unzip it, eg on a linux machine type
	gunzip hznd06_code.gz

Which will create a directory with all the files. 

From within Matlab, change to that directory by typing eg
	cd hznd06_code

Edit the file PARAM.M to change any parameters you want, like whether to use a
smooth or a OU prior and what firing rate and tuning widths to use. You
should not have to edit any of the other files. 

To run the inference, simply type

and hit ENTER. Enjoy. 


I don't know of any bugs at the moment, but please do let me know if you find
any (