Minimal model of strategy switching in the plus-maze navigation task

International audiencePrefrontal cortex (PFC) has been implicated in the ability to switch behavioral strategies in response to changes in reward contingencies. A recent experimental study has shown that separate subpopulations of neurons in the prefrontal cortex were activated when rats switched between allocentric place strategies and egocentric response strategies in the plus maze. In this paper we propose a simple neural-network model of strategy switching, in which the learning of the two strategies as well as learning to select between those strategies is governed by the same temporal-difference (TD) learning algorithm. We show that the model reproduces the experimental data on both behavioral and neural levels. On the basis of our results we derive testable prediction concerning a spatial dynamics of the phasic dopamine signal in the PFC, which is thought to encode reward-prediction error in the TD-learning theory

Spatial Representation and Navigation in a Bio-inspired Robot

A biologically inspired computational model of rodent repre-sentation?based (locale) navigation is presented. The model combines visual input in the form of realistic two dimensional grey-scale images and odometer signals to drive the firing of simulated place and head direction cells via Hebbian synapses. The space representation is built incrementally and on-line without any prior information about the environment and consists of a large population of location-sensitive units (place cells) with overlapping receptive fields. Goal navigation is performed using reinforcement learning in continuous state and action spaces, where the state space is represented by population activity of the place cells. The model is able to reproduce a number of behavioral and neuro-physiological data on rodents. Performance of the model was tested on both simulated and real mobile Khepera robots in a set of behavioral tasks and is comparable to the performance of animals in similar tasks

Robust Gain Scheduling for Smart-Structures in Parallel Robots

Smart-structures offer the potential to increase the productivity of parallel robots by reducing disturbing vibrations caused by high dynamic loads. In parallel robots the vibration behavior of the structure is position dependent. A single robust controller is not able to gain satisfying control performance within the entire workspace. Hence, vibration behavior is linearized at several operating points and robust controllers are designed. Controllers can be smoothly switched by gain-scheduling. A stability proof for fast varying scheduling parameters based on the Small-Gain Theorem is developed. Experimental data from Triglide, a four degree of freedom (DOF) parallel robot of the Collaborative Research Center 562, validate the presented concepts

Neuro-inspired navigation strategies shifting for robots: Integration of a multiple landmark taxon strategy

Rodents have been widely studied for their adaptive navigation capabilities. They are able to exhibit multiple navigation strategies; some based on simple sensory-motor associations, while others rely on the construction of cognitive maps. We previously proposed a computational model of parallel learning processes during navigation which could reproduce in simulation a wide set of rat behavioral data and which could adaptively control a robot in a changing environment. In this previous robotic implementation the visual approach (or taxon) strategy was how-ever paying attention to the intra-maze landmark only and learned to approach it. Here we replaced this mechanism by a more realistic one where the robot autonomously learns to select relevant landmarks. We show experimentally that the new taxon strategy is efficient, and that it combines robustly with the planning strategy, so as to choose the most efficient strategy given the available sensory informatio

Effects of Inescapable Stress on LTP in the Amygdala versus the Dentate Gyrus of Freely Behaving Rats

Stress impairs hippocampal long‐term potentiation (LTP), a model of synaptic plasticity that is assumed to underlie memory formation. In the amygdala, little is known about the effects of stress on LTP, or about its longevity. Here we assessed the ability of entorhinal cortex (EC) stimulation to induce LTP simultaneously in the basal amygdaloid nucleus (B) and in the dentate gyrus (DG) of freely behaving Wistar rats. We also tested whether LTP persists over days. Once established, we investigated the effects of acute vs. repeated inescapable stressful experiences on LTP in both structures. Results show that B, like DG, sustained LTP for 7 days. Furthermore, a single exposure to moderate stress facilitated LTP in B but did not affect DG LTP. Stress re‐exposure inhibited LTP in DG but only long‐lasting LTP (\u3e3 days) in B. Behaviourally, animals exhibited a higher immobility when re‐exposed to the stressor than with a single/first exposure. These data support a role for B in memory storage. Furthermore, they support a differential involvement of the amygdala and hippocampus in memory formation and storage depending on the emotional characteristics of the experience