Hebbian Theory

Hebbian theory is a neuroscientific theory claiming that an increase in synaptic efficacy arises from a presynaptic cell's repeated and persistent stimulation of a postsynaptic cell. Find the latest research on Hebbian theory here.

March 17, 2020

Learning of signaling networks: molecular mechanisms

Péter CsermelyPéter Tompa
August 30, 2020
Open Access

Adaptive balancing of exploration and exploitation around the edge of chaos in internal-chaos-based learning

Neural Networks : the Official Journal of the International Neural Network Society
Toshitaka Matsuki, Katsunari Shibata
June 21, 2020

The role of Hebbian learning in human perception: a methodological and theoretical review of the human Visual Long-Term Potentiation paradigm

Neuroscience and Biobehavioral Reviews
Rachael L SumnerIan J Kirk
August 19, 2020
Open Access

Neurally plausible mechanisms for learning selective and invariant representations

Journal of Mathematical Neuroscience
Fabio AnselmiLorenzo Rosasco
December 9, 2020

Spin-Filtering Ferroelectric Tunnel Junctions as Multiferroic Synapses for Neuromorphic Computing

ACS Applied Materials & Interfaces
Yihao YangZheng Wen
November 13, 2020
Open Access

Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning

Proceedings of the National Academy of Sciences of the United States of America
Maxwell GillettNicolas Brunel
October 15, 2020
Open Access

Hebbian learning with elasticity explains how the spontaneous motor tempo affects music performance synchronization

BioRxiv : the Preprint Server for Biology
Iran R RomanEdward W Large
October 22, 2020
Open Access

A Medical Decision Support System to Assess Risk Factors for Gastric Cancer Based on Fuzzy Cognitive Map

Computational and Mathematical Methods in Medicine
Seyed Abbas MahmoodiSeyed Mostafa Mahmoudi
April 8, 2020
Open Access

Donald O. Hebb and the Organization of Behavior: 17 years in the writing

Molecular Brain
Richard E Brown
December 23, 2020
Open Access

Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions

Evolutionary Computation
Anil YamanMykola Pechenizkiy
September 11, 2020
Open Access

Stability by gating plasticity in recurrent neural networks

BioRxiv : the Preprint Server for Biology
Katharina Anna Wilmes, C. Clopath
April 3, 2020
Open Access

Cognitive swarming in complex environments with attractor dynamics and oscillatory computing

Biological cybernetics
Joseph D MonacoKechen Zhang
June 15, 2020

Resonances induced by Spiking Time Dependent Plasticity

Pau Vilimelis Aceituno
January 6, 2021

Ti3 C2 -Based MXene Oxide Nanosheets for Resistive Memory and Synaptic Learning Applications

ACS Applied Materials & Interfaces
Atul C KhotTae Geun Kim
September 29, 2020
Open Access

Optimizing BCPNN Learning Rule for Memory Access

Frontiers in Neuroscience
Yu YangAnders Lansner
September 5, 2020

Semantic memory: A review of methods, models, and current challenges

Psychonomic Bulletin & Review
Abhilasha A Kumar
December 23, 2020
Open Access

Hardware-Based Hopfield Neuromorphic Computing for Fall Detection

Zheqi YuQammer H Abbasi
May 27, 2020
Open Access

Generalized Guerra's interpolation schemes for dense associative neural networks

Neural Networks : the Official Journal of the International Neural Network Society
Elena AgliariAlberto Fachechi

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