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

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.

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 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

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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