Biology subjects

Song, H. F.

Publications and source records attributed to Song, H. F..

2 recordsLinked to original sources

bioRxiv2018

Circuit mechanisms for the maintenance and manipulation of information in working memory

Recently it has been proposed that information in short-term memory may not always be stored in persistent neuronal activity, but can be maintained in \"activity-silent\" hidden states such as synaptic efficacies endowed with short-term plasticity (STP). However, working memory involves manipulation as well as maintenance of information in the absence of external stimuli. In this work, we investigated working memory representation using recurrent neural network (RNN) models trained to perform several working memory dependent tasks. We found that STP can support the short-term maintenance of information provided that the memory delay period is sufficiently short. However, in tasks that require actively manipulating information, persistent neuronal activity naturally emerges from learning, and the amount of persistent neuronal activity scales with the degree of manipulation required. These results shed insight into the current debate on working memory encoding, and suggest that persistent neural activity can vary markedly between tasks used in different experiments.

neuroscience↗

bioRxiv2017

Clustering and compositionality of task representations in a neural network trained to perform many cognitive tasks

A neural system has the ability to flexibly perform many tasks, but the underlying mechanism cannot be elucidated in traditional experimental and modeling studies designed for one task at a time. Here, we trained a single network model to perform 20 cognitive tasks that may involve working memory, decision-making, categorization and inhibitory control. We found that after training, recurrent units developed into clusters that are functionally specialized for various cognitive processes. We introduce a measure to quantify relationships between single-unit neural representations of tasks, and report five distinct types of such relationships that can be tested experimentally. Surprisingly, our network developed compositionality of task representations, a critical feature for cognitive flexibility, whereby one task can be performed by recombining instructions for other tasks. Finally, we demonstrate how the network could learn multiple tasks sequentially. This work provides a computational platform to investigate neural representations of many cognitive tasks.

neuroscience↗