Memory Updating
memory updating
Memory Decoding Journal Club: Reconstructing a new hippocampal engram for systems reconsolidation and remote memory updating
Join us for the Memory Decoding Journal Club, a collaboration between the Carboncopies Foundation and BPF Aspirational Neuroscience. This month, we're diving into a groundbreaking paper: 'Reconstructing a new hippocampal engram for systems reconsolidation and remote memory updating' by Bo Lei, Bilin Kang, Yuejun Hao, Haoyu Yang, Zihan Zhong, Zihan Zhai, and Yi Zhong from Tsinghua University, Beijing Academy of Artificial Intelligence, IDG/McGovern Institute of Brain Research, and Peking Union Medical College. Dr. Randal Koene will guide us through an engaging discussion on these exciting findings and their implications for neuroscience and memory research.
What is Foraging?
Foraging research aims at describing, understanding, and predicting resource-gathering behaviour. Optimal Foraging Theory (OFT) is a sub-discipline that emphasises that these aims can be aided by segmenting foraging behaviour into discrete problems that can be formally described and examined with mathematical maximization techniques. Examples of such segmentation are found in the isolated treatment of issues such as patch residence time, prey selection, information gathering, risky choice, intertemporal decision making, resource allocation, competition, memory updating, group structure, and so on. Since foragers face these problems simultaneously rather than in isolation, it is unsurprising that OFT models are ‘always wrong but sometimes useful’. I will argue that a progressive optimal foraging research program should have a defined strategy for dealing with predictive failure of models. Further, I will caution against searching for brain structures responsible for solving isolated foraging problems.
Contextual inference underlies the learning of sensorimotor repertoires
Humans spend a lifetime learning, storing and refining a repertoire of motor memories. However, it is unknown what principle underlies the way our continuous stream of sensori-motor experience is segmented into separate memories and how we adapt and use this growing repertoire. Here we develop a principled theory of motor learning based on the key insight that memory creation, updating, and expression are all controlled by a single computation – contextual inference. Unlike dominant theories of single-context learning, our repertoire-learning model accounts for key features of motor learning that had no unified explanation and predicts novel phenomena, which we confirm experimentally. These results suggest that contextual inference is the key principle underlying how a diverse set of experiences is reflected in motor behavior.