no code implementations • 22 Dec 2023 • Lulu Gong, Xudong Chen, ShiNung Ching
We are specifically interested in how the attractor landscapes of such networks become altered as a function of the strength and nature (Hebbian vs. anti-Hebbian) of learning, which may have a bearing on the ability of such rules to mediate large-scale optimization problems.
no code implementations • 6 Nov 2023 • Lulu Gong, Fabio Pasqualetti, Thomas Papouin, ShiNung Ching
We then embed this model in a bandit-based reinforcement learning task environment, and show how the presence of time-scale separated astrocytic modulation enables learning over multiple fluctuating contexts.
no code implementations • 22 May 2022 • Lulu Gong, Weijia Yao, Jian Gao, Ming Cao
Recently, an evolutionary game dynamics model taking into account the environmental feedback has been proposed to describe the co-evolution of strategic actions of a population of individuals and the state of the surrounding environment; correspondingly a range of interesting dynamic behaviors have been reported.
no code implementations • 10 May 2021 • Lulu Gong, Ming Cao
The fast-slow dynamics of an eco-evolutionary system are studied, where we consider the feedback actions of environmental resources that are classified into those that are self-renewing and those externally supplied.
