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Kiyonaga, A.

Publications and source records attributed to Kiyonaga, A..

2 recordsLinked to original sources

Structured memory representations develop at multiple time scales in hippocampal-cortical networks

Influential views of systems memory consolidation posit that the hippocampus rapidly forms representations of specific events, while neocortical networks extract regularities across events, forming the basis of schemas and semantic knowledge. Neocortical extraction of schematic memory representations is thought to occur on a protracted timescale of months, especially for information that is unrelated to prior knowledge. However, this theorized evolution of memory representations across extended timescales, and differences in the temporal dynamics of consolidation across brain regions, lack reliable empirical support. To examine the temporal dynamics of memory representations, we repeatedly exposed human participants to structured information via sequences of fractals, while undergoing longitudinal fMRI for three months. Sequence-specific activation patterns emerged in the hippocampus during the first 1-2 weeks of learning, followed one week later by high-level visual cortex, and subsequently the medial prefrontal and parietal cortices. Schematic, sequence-general representations emerged in the prefrontal cortex after 3 weeks of learning, followed by the medial temporal lobe and anterior temporal cortex. Moreover, hippocampal and most neocortical representations showed sustained rather than time-limited dynamics, suggesting that representations tend to persist across learning. These results show that specific hippocampal representations emerge early, followed by both specific and schematic representations at a gradient of timescales across hippocampal-cortical networks as learning unfolds. Thus, memory representations do not exist only in specific brain regions at a given point in time, but are simultaneously present at multiple levels of abstraction across hippocampal-cortical networks.

neuroscience↗

Long-term learning transforms prefrontal cortex selectivity during working memory

The lateral prefrontal cortex (lPFC) is reliably active during working memory (WM) across human and animal models, but the role of lPFC in successful WM is under debate. For instance, non-human primate (NHP) electrophysiology research finds that lPFC circuitry stores WM representations. Human neuroimaging instead suggests that lPFC plays a control function over WM content that is stored in sensory cortices. These seemingly incompatible WM accounts are often confounded by differences in the amount of task training and stimulus exposure across studies (i.e., NHPs tend to be trained extensively). Here, we test the possibility that such long-term training may alter the role of lPFC in WM maintenance. We densely sampled WM-related activity across learning, in three human participants, using a longitudinal functional MRI (fMRI) protocol. Over three months, participants trained on (1) a serial reaction time (SRT) task, wherein complex fractal stimuli were embedded within probabilistic sequences, and (2) a delayed recognition task probing WM for trained or novel stimuli. Participants were scanned frequently throughout training, to track how WM activity patterns change with repeated stimulus exposure and long-term associative learning. WM task performance improved for trained (but not novel) fractals and, neurally, delay activity significantly increased in distributed lPFC voxels across learning. Pattern similarity analyses also found that item-level WM representations became detectable within lPFC, but not in sensory cortices, and lPFC delay activity increasingly reflected sequence relationships from the SRT task, even though that information was task-irrelevant for WM. These findings demonstrate that human lPFC can show stimulus-selective WM responses with learning and WM representations are shaped by long-term experience. Therefore, influences from training and long-term memory may reconcile competing accounts of lPFC function during WM.

neuroscience↗