Synaptic modification by correlated activity: Hebb's postulate revisited.
By Guoqiang Bi, Mu-Ming Poo. 2001
1,544 citations recorded by the source corpus.
Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, 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
- Wulfram Gerstner
- Friedemann Zenke
- Matthieu Gilson
- Xiao-Jing Wang
- D. Feldman
- Gina G. Turrigiano
- Anthony N. Burkitt
- Nicolas Frémaux
In Cognitive Neuroscience
- Wulfram Gerstner
- Friedemann Zenke
- Xiao-Jing Wang
- Matthieu Gilson
- György Buzsáki
- D. Feldman
- Anthony N. Burkitt
- Nicolas Frémaux
In Neuroscience
- Wulfram Gerstner
- Gina G. Turrigiano
- E. D. Di Paolo
- Anthony N. Burkitt
- Matthieu Gilson
- Giacomo Indiveri
- Y. Dan
- Takao K. Hensch
Citations in the corpus
- Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression (2008, neutral): “or tightly timed postsynaptic spikes”
- 2007 Special Issue: Consciousness CLEARS the mind (2007, positive): “Such a match can trigger fast synchronized gamma oscillations (γ, 20–70Hz), whose short period enables synchronized spikes to drive learning via a spike-timing-dependent plasticity (STDP; Levy and Steward, 1983; Markram et al., 1997; Bi and Poo, 2001) learning rule.”
- Gain in sensitivity and loss in temporal contrast of STDP by dopaminergic modulation at hippocampal synapses (2009, background): “For neurons experiencing spiking activity, it has been found that the direction of subsequent synaptic plasticity can be determined by near coincident pre- and postsynaptic firing: LTP is induced when presynaptic firing precedes postsynaptic spike, and paring in the converse order results in LTD (3,”
- On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex (2011, methodology): “The shape of the STDP function ξ can be interpolated from experimental data from Bi and Poo (2001) as shown in Figure 3A .”
- Adaptive Resonance Theory: How a brain learns to consciously attend, learn, and recognize a changing world (2013, background): “(1)), whose short period enables synchronized spikes to drive learning via a spike-timing-dependent plasticity (STDP; Levy and Steward (1983), Markram, Lubke, Frotscher, and Sakmann (1997) and Bi and Poo (2001)) learning rule.”
- Dendritic calcium spikes induce bi-directional synaptic plasticity in the lateral amygdala. (2007, positive): “Similar to what has been described at glutamatergic synapses in the hippocampus and in the cortex (Markram et al., 1997; Debanne et al., 1998; Bi and Poo, 2001) pairing of pre- and postsynaptic APs induces bi-directional, spike timing-dependent plasticity (STDP) in the amygdala (Humeau et al.”
- The neuroscience of mammalian associative learning. (2005, background): “…breathtaking progress not only in detailing those essential circuits but also in identifying the essential processes occurring at the synapses (e.g., Bi & Poo 2001, Martinez & Derrick 1996) and within the neurons (e.g., Malinow & Malenka 2002, Murthy & De Camilli 2003) that make up those circuits.”
- Neural Field Continuum Limits and the Structure–Function Partitioning of Cognitive–Emotional Brain Networks (2023, background): “These kinds of local structural changes, such as those belonging to nonlinear Hebbian [41,50–53], dynamic stochastic synapse [54], spike timing-dependent plasticity [37,39,55], and quantum bidirectional plasticity learning models [10,13], are capable of mediating weak local correlative activity, whi”
- 2005 Special Issue (2005, positive): “Some scaling functions that are widely used with continuous firing rate neuronal representations are listed in Table 1. According to Abbott and Nelson (2000) , the experimental data suggests that f must be positive or negative depending on the postsynaptic rate.”
- Neural syntax: cell assemblies, synapsembles and readers (2010, positive): “Finally, this timescale also corresponds to the temporal window of spike-timing-dependent plasticity (Magee and Johnston, 1997; Markram et al., 1997; cf., Bi and Poo, 2001).”
- Artificial Neural Networks for Neuroscientists: A Primer (2020, background): “Hebbian Plasticity The predominant idea for biological learning is Hebbian plasticity (Hebb, 2005) and its variants (Song et al., 2000; Bi and Poo, 2001).”
- Critical period regulation. (2004, background): “Specific spike timing–dependent windows for synaptic plasticity rely upon physiologically realistic, millisecond-scale changes in the temporal order of pre- and postsynaptic action potentials (Bi & Poo 2001, Froemke & Dan 2002).”