Cortical-subcortical neural networks for motor learning and storing sequence memory.
Journal:
Neural networks : the official journal of the International Neural Network Society
Published Date:
May 7, 2025
Abstract
Motor sequence learning relies on the synergistic collaboration of multiple brain regions. However, most existing models for motor sequence learning primarily focus on functional-level analyses of sequence memory mechanisms, providing limited neurophysiological insights into how biological neural systems intrinsically encode the ordering of sequential element. Based on physiological and anatomical evidence, this study establishes a cortico-subcortical neuronal network model that differs from existing functional frameworks, emphasizing the neural mechanisms of sequence learning in the brain. The proposed model is biological plausibility and represents a potential mechanism for human sequential learning. It achieves the sequential selection and learning of elements through the cortico-basal ganglia-thalamic circuit, where the working memory function of the prefrontal cortex serves as the basis for Hebbian learning among cortical neurons, enabling the encoding of sequential order. The model successfully reproduces physiological experimental phenomena, validating its biological rationality. Furthermore, we explore the role of cholinergic interneurons in sequence learning, revealing their ability to enhance the robustness of learning. Finally, we demonstrate the model's applicability by deploying it to control a robotic arm in drawing and handwriting tasks, highlighting its adaptability to complex real-world scenarios. These biologically inspired results aim to offer a mechanistic explanation for sequence learning and memory formation in the human brain, providing valuable insights into brain-like control systems and neural networks.
