scholarly journals Task Demands Predict a Dynamic Switch in the Content of Awake Hippocampal Replay

2017 ◽  
Author(s):  
H. Freyja Olafsdottir ◽  
Francis Carpenter ◽  
Caswell Barry
Keyword(s):  
Entorhinal Cortex ◽  
Memory Consolidation ◽  
Place Cell ◽  
Spatial Task ◽  
Task Demands ◽  
Grid Cells ◽  
Operational Mode ◽  
Deep Layers ◽  
Hippocampal Place Cell ◽  
Dynamic Switch

Reactivation of hippocampal place cell sequences during behavioural immobility and rest has been linked with both memory consolidation and navigational planning. Yet it remains to be investigated whether these functions are temporally segregated; occurring during different behavioural states. During a self-paced spatial task, awake hippocampal replay occurring immediately before movement towards a reward location, or just after arrival at a reward location, preferentially involved cells consistent with the current trajectory. In contrast, during periods of extended immobility, no such biases were evident. Notably, the occurrence of task-focused reactivations predicted the accuracy of subsequent spatial decisions. Additionally, during immobility but not periods preceding or succeeding movement, grid cells in deep layers of entorhinal cortex replayed coherently with the hippocampus. Thus, hippocampal reactivations dynamically and abruptly switch operational mode in response to task demands; plausibly moving from a state favouring navigational planning to one geared towards memory consolidation.

scholarly journals Telencephalic outputs from the medial entorhinal cortex are copied directly to the hippocampus

2021 ◽  
Author(s):  
Sau Yee Tsoi ◽  
Merve Öncül ◽  
Ella Svahn ◽  
Mark Robertson ◽  
Zuzanna Bogdanowicz ◽  
...  
Keyword(s):  
Entorhinal Cortex ◽  
Memory Consolidation ◽  
Memory Storage ◽  
Medial Entorhinal Cortex ◽  
Ca1 Region ◽  
Deep Layers ◽  
Standard Models

AbstractStandard models for memory storage assume that signals reach the hippocampus from superficial layers of the entorhinal cortex (EC) and are returned to the telencephalon by projections from deep layers of the EC. Here we show that telencephalon-projecting cells in Layer 5a of the medial EC send a copy of their outputs back to the CA1 region of the hippocampus. Our results suggest that rather than serving as a relay, deep EC may coordinate hippocampal-neocortical interactions in memory consolidation.

scholarly journals Coordinated learning of grid cell and place cell spatial and temporal properties: multiple scales, attention and oscillations

2014 ◽  
Vol 369 (1635) ◽  
pp. 20120524 ◽  
Author(s):  
Stephen Grossberg ◽  
Praveen K. Pilly
Keyword(s):  
Entorhinal Cortex ◽  
Multiple Scales ◽  
Spatial Scales ◽  
Grid Cell ◽  
Neural Model ◽  
Gamma Oscillations ◽  
Place Cell ◽  
Place Cells ◽  
Grid Cells ◽  
Map Learning

A neural model proposes how entorhinal grid cells and hippocampal place cells may develop as spatial categories in a hierarchy of self-organizing maps (SOMs). The model responds to realistic rat navigational trajectories by learning both grid cells with hexagonal grid firing fields of multiple spatial scales, and place cells with one or more firing fields, that match neurophysiological data about their development in juvenile rats. Both grid and place cells can develop by detecting, learning and remembering the most frequent and energetic co-occurrences of their inputs. The model's parsimonious properties include: similar ring attractor mechanisms process linear and angular path integration inputs that drive map learning; the same SOM mechanisms can learn grid cell and place cell receptive fields; and the learning of the dorsoventral organization of multiple spatial scale modules through medial entorhinal cortex to hippocampus (HC) may use mechanisms homologous to those for temporal learning through lateral entorhinal cortex to HC (‘neural relativity’). The model clarifies how top-down HC-to-entorhinal attentional mechanisms may stabilize map learning, simulates how hippocampal inactivation may disrupt grid cells, and explains data about theta, beta and gamma oscillations. The article also compares the three main types of grid cell models in the light of recent data.

Performance, workload, and stress correlates of temporal and spatial task demands

2003 ◽  
Author(s):  
Jennifer M. Ross ◽  
James L. Szalma ◽  
Jennifer Thropp ◽  
Peter A. Hancock
Keyword(s):  
Spatial Task ◽  
Task Demands ◽  
Temporal And Spatial

scholarly journals Impaired hippocampal place cell dynamics in a mouse model of the 22q11.2 deletion

2017 ◽  
Vol 20 (11) ◽  
pp. 1612-1623 ◽  
Author(s):  
Jeffrey D Zaremba ◽  
Anastasia Diamantopoulou ◽  
Nathan B Danielson ◽  
Andres D Grosmark ◽  
Patrick W Kaifosh ◽  
...  
Keyword(s):  
Mouse Model ◽  
Place Cell ◽  
22Q11.2 Deletion ◽  
Cell Dynamics ◽  
Hippocampal Place Cell

scholarly journals Abnormal Locus Coeruleus Sleep Activity Alters Sleep Signatures of Memory Consolidation and Impairs Place Cell Stability and Spatial Memory

2018 ◽  
Vol 28 (22) ◽  
pp. 3599-3609.e4 ◽  
Author(s):  
Kevin M. Swift ◽  
Brooks A. Gross ◽  
Michelle A. Frazer ◽  
David S. Bauer ◽  
Kyle J.D. Clark ◽  
...  
Keyword(s):  
Spatial Memory ◽  
Locus Coeruleus ◽  
Memory Consolidation ◽  
Place Cell ◽  
Cell Stability

scholarly journals Contribution of hippocampal place cell activity to learning and formation of goal-directed navigation in rats

Neuroscience ◽  
2003 ◽  
Vol 117 (4) ◽  
pp. 1025-1035 ◽  
Author(s):  
T Kobayashi ◽  
A.H Tran ◽  
H Nishijo ◽  
T Ono ◽  
G Matsumoto
Keyword(s):  
Cell Activity ◽  
Place Cell ◽  
Hippocampal Place Cell

scholarly journals A geometric attractor mechanism for self-organization of entorhinal grid modules

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Louis Kang ◽  
Vijay Balasubramanian
Keyword(s):  
Entorhinal Cortex ◽  
Network Models ◽  
Recurrent Inhibition ◽  
Modular Structure ◽  
Self Organization ◽  
Periodic Lattice ◽  
Grid Cells ◽  
Medial Entorhinal Cortex ◽  
Attractor Network ◽  
Discrete Values

Grid cells in the medial entorhinal cortex (MEC) respond when an animal occupies a periodic lattice of ‘grid fields’ in the environment. The grids are organized in modules with spatial periods, or scales, clustered around discrete values separated on average by ratios in the range 1.4–1.7. We propose a mechanism that produces this modular structure through dynamical self-organization in the MEC. In attractor network models of grid formation, the grid scale of a single module is set by the distance of recurrent inhibition between neurons. We show that the MEC forms a hierarchy of discrete modules if a smooth increase in inhibition distance along its dorso-ventral axis is accompanied by excitatory interactions along this axis. Moreover, constant scale ratios between successive modules arise through geometric relationships between triangular grids and have values that fall within the observed range. We discuss how interactions required by our model might be tested experimentally.

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