Odor supported place cell model and goal navigation in rodents (Kulvicius et al. 2008)

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Accession:118434
" ... Here we model odor supported place cells by using a simple feed-forward network and analyze the impact of olfactory cues on place cell formation and spatial navigation. The obtained place cells are used to solve a goal navigation task by a novel mechanism based on self-marking by odor patches combined with a Q-learning algorithm. We also analyze the impact of place cell remapping on goal directed behavior when switching between two environments. ..."
Reference:
1 . Kulvicius T, Tamosiunaite M, Ainge J, Dudchenko P, Worgotter F (2008) Odor supported place cell model and goal navigation in rodents. J Comput Neurosci 25:481-500 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Rate-coding model neurons; Reinforcement Learning; Place cell/field; Spatial Navigation;
Implementer(s):
This is the readme for the matlab scripts associated with the paper:

Kulvicius T, Tamosiunaite M, Ainge J, Dudchenko P, Worgotter F (2008)
Odor supported place cell model and goal navigation in rodents.
J Comput Neurosci 25:481-500


navPFdevelv03.m - place field model implemented by using feed-forward
                   network with winner takes all learning
                   algorithm.  Place cells are formed from visual
                   and olfactory input.

createCellsAll.m - creates place fields. This script is called by
                   "navPFdevelv03.m".

plotPFM.m - plots place field maps. This function is called by
                   "navPFdevelv03.m".

navPFQLv14.m - goal navigation based on palce cells and Q-learning
                   with function aproximation.

navUrineBasedv04.m - goal navigation based on self generated odor
                   marks (no place fields).

navPFQLUv07.m - goal navigation using combined navigation algorithm
                   (Q-learning based on place cells + self-marking
                   navigation)

These files were supplied by Tomas Kulvicius.

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