IMR Press / JIN / Volume 17 / Issue 1 / DOI: 10.31083/JIN-170036
Open Access Research article
Modeling the interaction of learning systems in a reward-based virtual navigation task
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1 Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Brach, Islamic Azad University, Qazvin, Iran
*Correspondence: somayeh.rdana@gmail.com (Somayeh Raiesdana)
J. Integr. Neurosci. 2018, 17(1), 27–42; https://doi.org/10.31083/JIN-170036
Submitted: 7 April 2017 | Accepted: 19 May 2017 | Published: 15 January 2018
Abstract

Existence of allocentric and egocentric systems for human navigation, mediating spatial, and response learning, respectively, has so far been discussed. It is controversial whether navigational strategies and their underlying learning systems and, accordingly, the activation of their associated brain areas are independent/parallel or whether they functionally/causally interact in a competitive or in a cooperative manner to solve navigational tasks. The insights provided by neural networks involved in reward-based navigation attributed to individual involvement or interactions of learning systems have been surveyed. This paper characterizes the interactions of neural networks by constructing generative neural models and investigating their functional and effective connectivity patterns. A single-subject computer-based virtual reality environment was constructed to simulate a navigation task within a naturalistic large-scale space wherein participants were rewarded for using either a place, response, or mixed strategy at different navigational stages. First, functional analyses were undertaken to evaluate neural activities via mapping brain activation and making statistical inference. Effects of interest, spatial and response learning/retrieval, and their competition and cooperation were investigated. The optimal generative model was then estimated using dynamic casual modeling to quantify effective connectivities within the network. This analysis revealed how experimental conditions supported competition and cooperation strategies and how they modulated the underlying network. Results suggest that when navigational strategies cooperated, there were statistically significant, functional, and effective connectivities between hippocampus and striatum. However, when the strategies competed, effective connections were not established among these regions. Instead, connections between hippocampus/striatum and prefrontal cortex were strengthened. It can be inferred that a type of dynamical reconfiguration occurs within a network responsible for navigation when strategies interact either cooperatively or competitively. This supports adaptive causal organization of the brain when it is engaged with goal directed behavior.

Keywords
Navigational strategies
spatial and response learning
radial maze
functional interactions
effective connectivities
dynamic causal modeling
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