Transient hippocampal CA1 lesions in humans impair pattern separation performance

Hippocampus ◽  
2019 ◽  
Vol 29 (8) ◽  
pp. 736-747 ◽  
Author(s):  
Annika Hanert ◽  
Anya Pedersen ◽  
Thorsten Bartsch
Keyword(s):  
Pattern Separation ◽  
Separation Performance ◽  
Hippocampal Ca1

scholarly journals Hippocampal spatial and sequential representations of event structure scaffold precise episodic temporal order memory

2020 ◽  
Author(s):  
Chuqi Liu ◽  
Zhifang Ye ◽  
Chuansheng Chen ◽  
Nikolai Axmacher ◽  
Gui Xue
Keyword(s):  
Temporal Order ◽  
Event Structure ◽  
Pattern Separation ◽  
Activation Patterns ◽  
Mnemonic Strategy ◽  
Method Of Loci ◽  
Hippocampal Ca1 ◽  
Temporal Order Memory ◽  
Sequence Boundaries ◽  
Spatial Locations

Abstract The hippocampus plays an important role in representing spatial locations and sequences and for transforming representations via pattern separation and completion. How these representational structures and operations support memory for the temporal order of random items is still poorly understood. We addressed this question by leveraging the method of loci (MOL), a powerful mnemonic strategy for temporal order memory that particularly recruits hippocampus-dependent computations of spatial locations and associations. Applying representational similarity analysis to fMRI activation patterns revealed that hippocampal subfields contained representations of both temporal context and multiple features of sequence structure, including location identity, distance, and sequence boundaries. Critically, the hippocampal CA1 and CA23DG exhibited spatial and sequential pattern separation, respectively, enabling the encoding of multiple items in the same location and reducing swap errors across adjacent locations. Our results suggest that the hippocampus can flexibly reconfigure multiplexed event structure representations to support accurate temporal order memory.

Aging affects spatial reconstruction more than spatial pattern separation performance even after extended practice

Hippocampus ◽  
2017 ◽  
Vol 27 (6) ◽  
pp. 716-725 ◽  
Author(s):  
Rachel Clark ◽  
Asli C. Tahan ◽  
Patrick D. Watson ◽  
Joan Severson ◽  
Neal J. Cohen ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Pattern Separation ◽  
Separation Performance ◽  
Spatial Reconstruction ◽  
Extended Practice ◽  
Spatial Pattern Separation

scholarly journals Sparse synaptic connectivity is required for decorrelation and pattern separation in feedforward networks

2017 ◽  
Author(s):  
N. Alex Cayco-Gajic ◽  
Claudia Clopath ◽  
R. Angus Silver
Keyword(s):  
Mossy Fiber ◽  
Activity Patterns ◽  
Optimal Solution ◽  
Pattern Separation ◽  
Synaptic Connectivity ◽  
Separation Performance ◽  
Feedforward Networks ◽  
Beneficial Effects ◽  
Learning Speed ◽  
Input Layer

AbstractPattern separation is a fundamental function of the brain. Divergent feedforward networks separate overlapping activity patterns by mapping them onto larger numbers of neurons, aiding learning in downstream circuits. However, the relationship between the synaptic connectivity within these circuits and their ability to separate patterns is poorly understood. To investigate this we built simplified and biologically detailed models of the cerebellar input layer and systematically varied the spatial correlation of their inputs and their synaptic connectivity. Performance was quantified by the learning speed of a classifier trained on either the mossy fiber input or granule cell output patterns. Our results establish that the extent of synaptic connectivity governs the pattern separation performance of feedforward networks by counteracting the beneficial effects of expanding coding space and threshold-mediated decorrelation. The sparse synaptic connectivity in the cerebellar input layer provides an optimal solution to this trade-off, enabling efficient pattern separation and faster learning.

scholarly journals Limbic Tract Integrity Contributes to Pattern Separation Performance Across the Lifespan

2014 ◽  
Vol 25 (9) ◽  
pp. 2988-2999 ◽  
Author(s):  
Ilana J. Bennett ◽  
Derek J. Huffman ◽  
Craig E.L. Stark
Keyword(s):  
Pattern Separation ◽  
Separation Performance

P58. Sleep in humans stabilizes hippocampal pattern separation performance

2018 ◽  
Vol 129 (8) ◽  
pp. e91
Author(s):  
A. Hanert ◽  
F.D. Weber ◽  
A. Pedersen ◽  
J. Born ◽  
T. Bartsch
Keyword(s):  
Pattern Separation ◽  
Separation Performance

scholarly journals Pattern separation performance is decreased in patients with early multiple sclerosis

2017 ◽  
Vol 7 (8) ◽  
pp. e00739 ◽  
Author(s):  
Vincent Planche ◽  
Aurélie Ruet ◽  
Julie Charré-Morin ◽  
Mathilde Deloire ◽  
Bruno Brochet ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Pattern Separation ◽  
Separation Performance

P.2.015 Chronic effects of ‘biased’ 5-HT1A receptor agonists on object pattern separation performance and hippocampal plasticity

2016 ◽  
Vol 26 ◽  
pp. S36-S37 ◽  
Author(s):  
B. Van Hagen ◽  
N.P. Van Goethem ◽  
R. Schreiber ◽  
A. Newman-Tancredi ◽  
M. Varney ◽  
...  
Keyword(s):  
Pattern Separation ◽  
Separation Performance ◽  
Hippocampal Plasticity ◽  
Receptor Agonists ◽  
Chronic Effects
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