Hebb and homeostasis in neuronal plasticity.

By Gina G. Turrigiano, Sacha B. Nelson. Current Opinion in Neurobiology, 2000

733 citations recorded by the source corpus.

Subfields: Cell Biology, Cellular and Molecular Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7f379060bdfed82e704ff8ff81dd8b1ff7b2d6e5

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Citations in the corpus

The synaptic plasticity and memory hypothesis: encoding, storage and persistence (2014, background): “Homeostatic plasticity has been discovered and it may play both a normalizing and protective role in neural circuits that might otherwise be risk at seizure through potentiation of too high a proportion of neural afferents [25].”
Long-term Synaptic Plasticity: Circuit Perturbation and Stabilization (2014, background): “Homeostatic scaling of post synaptic AMPA receptors is considered as a mechanism to protect against saturation beyond the ability of neurons to encode information [20].”
Models of Metaplasticity: A Review of Concepts (2015, positive): “This modulation of the Hebbian plasticity by preactivation of the synaptic pathway is a direct application of the so-called metaplasticity (Abraham and Bear, 1996), i.e., the plasticity of the learning rules themselves.”
A History of Spike-Timing-Dependent Plasticity (2011, background): “…rules seems key to future progress in our understanding of the relationship between intrinsically unstable synaptic plasticity and stability-promoting mechanisms such as synaptic scaling (Turrigiano et al., 1998; Turrigiano and Nelson, 2000; Izhikevich and Desai, 2003; Watt and Desai, 2010).”
Neural remodeling in retinal degeneration. (2003, background): “One common hypothesis is that dendritic arbor remodeling arises from loss of afferent excitatory input (Turrigiano and Nelson, 2000).”
Emergent Dynamical Properties of the BCM Learning Rule (2017, background): “While homeostasis of synapses found in experiments is slow [12, 37], homeostasis of synapses in most theoretical models needs to be rapid and sometimes even instantaneous to achieve stability [33, 38, 39].”
Homeostatic control of neural activity: from phenomenology to molecular design. (2006, background): “…and muscle cells, interface with the mechanisms of neural plasticity to ensure that neural activity remains stable over time (Davis & Goodman 1998ab, Turrigiano et al. 1998, Turrigiano & Nelson 2000, Davis & Bezprozvanny 2001, Marder & Prinz 2002, Burrone & Murthy 2003, Perez-Otano & Ehlers 2005).”
Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics (2007, background): “In the brain, we might hypothesize the existence of other mechanisms (e.g., homeostasis; Turrigiano & Nelson, 2000), which would automatically create the proper balance between the synaptic inputs of the teacher (typically a top-down signal) and the synaptic inputs of the sensory stimuli.”
Neurotrophin secretion: current facts and future prospects. (2003, background): “Furthermore, a number of synaptic functions are modulated by more chronic TrkB signaling (for a recent review see, e.g. Bolton et al., 2000; Turrigiano and Nelson, 2000).”
Neuronal homeostasis: time for a change? (2011, background): “For example, rather that viewing Hebbian and homeostatic processes as exerting ‘opposing’ effects neuronal properties (e.g. Turrigiano & Nelson, 2000), they can be reconciled as distinct features of the same control system.”