이상완 교수

Abstracts of the 25th Annual Computational Neuroscience\nMeeting: CNS-2016\nSeogwipo City, Jeju-do, South Korea. 2–7 July 2016

Background: While there is good evidence that reward learning is underpinned by two distinct decision control systems – a cognitive ‘model-based’ and a habitbased ‘model-free’ system, a comparable distinction for punishment avoidance has been much less clear. Methods: We implemented a pain avoidance task that placed differential emphasis on putative model-based and model-free processing, mirroring a paradigm and modelling approach recently developed for reward-based decision-making. Subjects per

The traveling salesmen problem (TSP)-one of the most fundamental NP-hard problems in combinatorial optimization-has received considerable attention owing to its direct applicability to real-world routing. Recent studies on TSP have adopted a deep policy network to learn a stochastic acceptance rule. Despite its success in some cases, the structural and functional complexity of the deep policy networks makes it hard to explore the problem space while performing a local search at the same time. We

The external globus pallidus (GPe) is a central part of the basal ganglia indirect pathway implicated in movement and decision-making. As a hub connecting the dorsal striatum and subthalamic nucleus (STN), the GPe guides repetitive and routine behaviors. However, it remains unknown how diverse GPe cells engage in routine formation while learning action sequences in repetitive reward-seeking conditioning. Here, in male mice, we investigated the Ca 2+ dynamics of two GPe cell types, astrocytes and

These invited talks discuss the following: Interactive Design Support System Using Kansei Information; Dynamic Fuzzy Q-learning with Forgetting Facility; Brain-inspired Artificial Intelligence: Model-based and Model-free Control; (Multivariate) Empirical Mode Decomposition Filter Banks and a Quintet Singular Value Decomposition; Visual Analysis and Representations of Type-2 Fuzzy Membership Functions.

MASSiVE (Multi – Layer Architecture for Semi-Autonomous Service Robots with Verified Task Execution) is a software framework that provides an infrastructure concept for distributed sensor and actuator systems such as service robots, operating in environments that are equipped with smart components. Besides this modular and extensible architecture, a principle of task knowledge specification and verification with processstructures is included in MASSiVE that is able to guarantee task planning in