Statistical implications of clipped Hebbian learning of cell assemblies

2005 ◽  
Vol 65-66 ◽  
pp. 647-652 ◽  
Author(s):  
Andreas Knoblauch
Keyword(s):  
Hebbian Learning ◽  
Cell Assemblies

scholarly journals Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network

2009 ◽  
Vol 1 (2) ◽  
pp. 160-176 ◽  
Author(s):  
Max Garagnani ◽  
Thomas Wennekers ◽  
Friedemann Pulvermüller
Keyword(s):  
Neural Network ◽  
Hebbian Learning ◽  
Cell Assemblies

A Basis Set of Elementary Operations Captures Recombination of Neocortical Cell Assemblies During Basal Conditions and Learning

2019 ◽  
Author(s):  
Dominik F. Aschauer ◽  
Jens-Bastian Eppler ◽  
Luke Ewig ◽  
Anna Chambers ◽  
Christoph Pokorny ◽  
...  
Keyword(s):  
Basis Set ◽  
Cell Assemblies

Disentangling collective motion and local rearrangements in 2D and 3D cell assemblies

Soft Matter ◽  
2021 ◽  
Author(s):  
Roberto Cerbino ◽  
Stefano Villa ◽  
Andrea Palamidessi ◽  
Emanuela Frittoli ◽  
Giorgio Scita ◽  
...  
Keyword(s):  
Collective Motion ◽  
Cell Assemblies ◽  
Multiscale Dynamics ◽  
2D And 3D

We propose a new tracking-free method for the quantification of multiscale dynamics in 2D and 3D cell collectives.

scholarly journals Built to Last: Functional and Structural Mechanisms in the Moth Olfactory Network Mitigate Effects of Neural Injury

2021 ◽  
Vol 11 (4) ◽  
pp. 462
Author(s):  
Charles B. Delahunt ◽  
Pedro D. Maia ◽  
J. Nathan Kutz
Keyword(s):  
Brain Injuries ◽  
Hebbian Learning ◽  
Neuronal Damage ◽  
Neural Systems ◽  
Network Motifs ◽  
Neural Firing ◽  
Neural Injury ◽  
Axonal Pathology ◽  
Structural Mechanisms ◽  
Axonal Swellings

Most organisms suffer neuronal damage throughout their lives, which can impair performance of core behaviors. Their neural circuits need to maintain function despite injury, which in particular requires preserving key system outputs. In this work, we explore whether and how certain structural and functional neuronal network motifs act as injury mitigation mechanisms. Specifically, we examine how (i) Hebbian learning, (ii) high levels of noise, and (iii) parallel inhibitory and excitatory connections contribute to the robustness of the olfactory system in the Manduca sexta moth. We simulate injuries on a detailed computational model of the moth olfactory network calibrated to data. The injuries are modeled on focal axonal swellings, a ubiquitous form of axonal pathology observed in traumatic brain injuries and other brain disorders. Axonal swellings effectively compromise spike train propagation along the axon, reducing the effective neural firing rate delivered to downstream neurons. All three of the network motifs examined significantly mitigate the effects of injury on readout neurons, either by reducing injury’s impact on readout neuron responses or by restoring these responses to pre-injury levels. These motifs may thus be partially explained by their value as adaptive mechanisms to minimize the functional effects of neural injury. More generally, robustness to injury is a vital design principle to consider when analyzing neural systems.

Stereo matching using Hebbian learning

1999 ◽  
Vol 29 (4) ◽  
pp. 553-559 ◽  
Author(s):  
G. Pajares ◽  
J.M. Cruz ◽  
J.A. Lopez-Orozco
Keyword(s):  
Stereo Matching ◽  
Hebbian Learning

Dynamical cell assemblies in the rat auditory cortex in a reaction-time task

Biosystems ◽  
1998 ◽  
Vol 48 (1-3) ◽  
pp. 269-277 ◽  
Author(s):  
Alessandro E.P Villa ◽  
Brian Hyland ◽  
Igor V Tetko ◽  
Abdellatif Najem
Keyword(s):  
Reaction Time ◽  
Auditory Cortex ◽  
Reaction Time Task ◽  
Cell Assemblies ◽  
Time Task
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