Biology subjects

Graves, K. N.

Publications and source records attributed to Graves, K. N..

4 recordsLinked to original sources

bioRxiv2023

Hippocampal Codes for Real-World Human Navigation

As animals move through the world, the hippocampus represents their location, direction, and speed. Parallel studies in humans have been mostly limited to virtual navigation because of physical constraints of suitable neural recording technologies. However, there are known differences between real-world and virtual navigation, leaving open the question of how the hippocampus supports real-world navigation in humans. Here we report evidence from ambulatory patients with chronic brain implants that the location, direction, and speed of humans walking along a linear track are represented in local field potentials from the hippocampus. We further show in a subset of patients who were tested twice after long delays that these representations can be reliable over time. These findings provide the first demonstration of multiple, stable neural codes for real-world navigation in the human hippocampus.

neuroscience↗

bioRxiv2023

Online abstraction during statistical learning revealed by neural entrainment from intracranial recordings

We encounter the same people, places, and objects in predictable sequences and configurations. These regularities are learned efficiently by humans via statistical learning. Importantly, statistical learning creates knowledge not only of specific regularities, but also of more abstract, generalizable regularities. However, prior evidence of such abstract learning comes from post-learning behavioral tests, leaving open the question of whether abstraction occurs online during initial exposure. We address this question by measuring neural entrainment during statistical learning with intracranial recordings. Neurosurgical patients viewed a stream of scene photographs with regularities at one of two levels: In the Exemplar-level Structured condition, the same photographs appeared repeatedly in pairs. In the Category-level Structured condition, the photographs were trial-unique but their categories were paired across repetitions. In a baseline Random condition, the same photographs repeated but in a scrambled order. We measured entrainment at the frequency of individual photographs, which was expected in all conditions, but critically also at half of that frequency -- the rate at which to-be-learned pairs appeared in the two structured conditions (but not the random condition). Neural entrainment to both exemplar and category pairs emerged within minutes throughout visual cortex and in frontal and temporal brain regions. Many electrode contacts were sensitive to only one level of structure, but a significant number encoded both exemplar and category regularities. These findings suggest that abstraction occurs spontaneously during statistical learning, providing insight into the brains unsupervised mechanisms for building flexible and robust knowledge that generalizes across input variation and conceptual hierarchies.

neuroscience↗

bioRxiv2022

Temporal dynamics of competition between statistical learning and episodic memory in intracranial recordings of human visual cortex

The function of long-term memory is not just to reminisce about the past, but also to make predictions that help us behave appropriately and efficiently in the future. This predictive function of memory provides a new perspective on the classic question from memory research of why we remember some things but not others. If prediction is a key outcome of memory, then the extent to which an item generates a prediction signifies that this information already exists in memory and need not be encoded. We tested this principle using human intracranial EEG as a time-resolved method to quantify prediction in visual cortex during a statistical learning task and link the strength of these predictions to subsequent episodic memory behavior. Epilepsy patients of both sexes viewed rapid streams of scenes, some of which contained regularities that allowed the category of the next scene to be predicted. We verified that statistical learning occurred using neural frequency tagging and measured category prediction with multivariate pattern analysis. Although neural prediction was robust overall, this was driven entirely by items that were subsequently forgotten. Such interference provides a mechanism by which prediction can regulate memory formation to prioritize encoding of information that could help learn new predictive relationships. Significance StatementWhen faced with a new experience, we are rarely at a loss for what to do. Rather, because many aspects of the world are stable over time, we rely upon past experiences to generate expectations that guide behavior. Here we show that these expectations during a new experience come at the expense of memory for that experience. From intracranial recordings of visual cortex, we decoded what humans expected to see next in a series of photographs based on patterns of neural activity. Photographs that generated strong neural expectations were more likely to be forgotten in a later behavioral memory test. Prioritizing the storage of experiences that currently lead to weak expectations could help improve these expectations in future encounters.

neuroscience↗

bioRxiv2021

Remembering the pattern: A longitudinal case study on statistical learning in spatial navigation and memory consolidation

Distinct brain systems are thought to support statistical learning over different timescales. Regularities encountered during online perceptual experience can be acquired rapidly by the hippocampus. Further processing during offline consolidation can establish these regularities gradually in cortical regions, including the medial prefrontal cortex (mPFC). These mechanisms of statistical learning may be critical during spatial navigation, for which knowledge of the structure of an environment can facilitate future behavior. Rapid acquisition and prolonged retention of regularities have been investigated in isolation, but how they interact in the context of spatial navigation is unknown. We had the rare opportunity to study the brain systems underlying both rapid and gradual timescales of statistical learning using intracranial electroencephalography (iEEG) longitudinally in the same patient over a period of three weeks. As hypothesized, spatial patterns were represented in the hippocampus but not mPFC for up to one week after statistical learning and then represented in the mPFC but not hippocampus two and three weeks after statistical learning. Taken together, these findings clarify that the hippocampus may do the initial work of extracting regularities and transfer these integrated memories to cortex, rather than only storing individual experiences and leaving it up to cortex to extract regularities. HighlightsO_LICase study of an epilepsy patient tested longitudinally over three weeks. C_LIO_LIWe tracked time-dependent changes in neural representations of spatial patterns. C_LIO_LIRepresentations reconstructed from hippocampal activity reflected patterns learned within a week. C_LIO_LIRepresentations reconstructed from activity in the mPFC reflected patterns learned 2-3 weeks ago. C_LI

neuroscience↗