- Poster presentation
- Open Access
Development of place cells by a simple model in a closed loop context
- Tomas Kulvicius1, 2Email author,
- Minija Tamosiunaite2 and
- Florentin Wörgötter1
https://doi.org/10.1186/1471-2202-8-S2-P106
© Kulvicius et al; licensee BioMed Central Ltd. 2007
- Published: 6 July 2007
Keywords
- Food Source
- Firing Rate
- Path Integration
- Connection Weight
- Place Cell
Introduction
Experiments on rats show that visual cues play an important role in the formation of place cells. Nevertheless, rats also rely on other allothetic non-visual stimuli such as auditory, olfactory and somatosensory stimuli. Most researchers have seen navigation in the dark as evidence for the importance of path integration as an additional input to place cells. Many place cell models have been developed by combining visual and self motion (path integration) information. However, Save et al. have shown that olfactory cues rather than self-motion information have been used to stabilize the place fields (PF) of rats in the dark [1]. Based on these findings we model place cells by combining visual and olfactory information in a feed-forward network. We also analyze the influence of the directionality of place cells on a goal navigation task.
Methods
In a model we develop place cells from external visual and olfactory cues. Sensory inputs as well as place cells are affected whenever the rat navigates in the environment, thus closing the loop. We use a fully connected feed-forward network to create place cells where initially random connection weights W are used. Features X derived from visual and olfactory cues are fed to the input layer and the best matching unit (BMU) is found at each time step according to minimal Euclidian distance. We update weights of the BMU by W i t+1 = W i t + μ(X t –W i t ), where μ is a learning rate, μ<<1. The firing rate of place cells is calculated as the following: = exp(-||X t -W i t ||2/2 σ2), where σ defines the size of the place field. Obtained PFs are used for goal navigation where the model rat had to find the food source by ways of the Q-learning algorithm.
Results
(A) Example of place fields. (B) Average number of steps against number of trials needed to find a goal in 100 experiments.
Conclusion
In this study we have shown that formation of place fields by combining visual and olfactory cues and goal navigation by ways of simple model is possible in a closed loop context. We also emphasize the contribution and benefit of olfactory cues in a goal navigation task.
Authors’ Affiliations
References
- Save E, Nerad L, Poucet B: Contribution of multiple sensory information to place field stability in hippocampal place cells. Hippocampus. 2000, 10 (1): 64-76. 10.1002/(SICI)1098-1063(2000)10:1<64::AID-HIPO7>3.0.CO;2-Y.PubMedView ArticleGoogle Scholar
Copyright
This article is published under license to BioMed Central Ltd.