Synaptic plasticity: taming the beast

By L. F. Abbott, Sacha B. Nelson. 2000

2,337 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering, Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

In Neuroscience

Citations in the corpus

Continual Lifelong Learning with Neural Networks: A Review (2018, background): “However, Hebbian plasticity alone is unstable and leads to runaway neural activity, thus requiring compensatory mechanisms to stabilize the learning process (Abbott and Nelson, 2000; Bienenstock et al., 1982).”
The temporal paradox of Hebbian learning and homeostatic plasticity (2017, background): “separation of timescales poses a temporal paradox as it renders most data-driven STDP models unstable [3, 81].”
Replay in Deep Learning: Current Approaches and Missing Biological Elements (2021, neutral): “Since synaptic plasticity mechanisms that result in long-term changes are activity dependent (Bi & Poo, 1998; Markram, Lübke, Frotscher, & Sakmann, 1997; Abbott & Nelson, 2000), replay during sleep plays a critical role in long-term memory consolidation.”
Introduction to spiking neural networks: Information processing, learning and applications. (2011, background): “…scale: some processes, e.g. pulse paired facilitation, decay on the order of about 10–100 ms; other processes, such as long-term potentiation (LTP) or long-term depression (LTD), persist for hours, days, or longer (Lømo 1966, Magleby and Zengel 1976, Abbott and Nelson 2000, Citri and Malenka 2008).”
State-dependent computations: spatiotemporal processing in cortical networks (2009, background): “However, given the universal presence of synaptic plasticit”
Gain in sensitivity and loss in temporal contrast of STDP by dopaminergic modulation at hippocampal synapses (2009, methodology): “Because of its requirement for naturally occurring spiking activity of neurons, STDP is likely to represent a physiologically relevant form of synaptic plasticity (3, 5).”
The''echo state''approach to analysing and training recurrent neural networks (2001, background): “While much is known about temporal learning phenomena at the synapse level (overview: [1]), not much is clear concerning the learning dynamics at the network level.”
Mechanism for the learning deficits in a mouse model of neurofibromatosis type 1 (2002, background): “Learning is thought to occur through activity-dependent synaptic modifications in neuronal network”