Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type

By Guoqiang Bi, Mu-Ming Poo. 1998

In cultures of dissociated rat hippocampal neurons, persistent potentiation and depression of glutamatergic synapses were induced by correlated spiking of presynaptic and postsynaptic neurons. The relative timing between the presynaptic and postsynaptic spiking determined the direction and the extent of synaptic changes. Repetitive postsynaptic spiking within a time window of 20 msec after presynaptic activation resulted in long-term potentiation (LTP), whereas postsynaptic spiking within a window of 20 msec before the repetitive presynaptic activation led to long-term depression (LTD). Significant LTP occurred only at synapses with relatively low initial strength, whereas the extent of LTD did not show obvious dependence on the initial synaptic strength. Both LTP and LTD depended on the activation of NMDA receptors and were absent in cases in which the postsynaptic neurons were GABAergic in nature. Blockade of L-type calcium channels with nimodipine abolished the induction of LTD and reduced the extent of LTP. These results underscore the importance of precise spike timing, synaptic strength, and postsynaptic cell type in the activity-induced modification of central synapses and suggest that Hebb’s rule may need to incorporate a quantitative consideration of spike timing that reflects the narrow and asymmetric window for the induction of synaptic modification.

Subfields: Neuroscience

How researchers see this paper

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

In Neuroscience

Citations in the corpus

Using Fast Weights to Attend to the Recent Past (2016, background): “…that can retrieve each bit of a pattern from all the other bits [Gardner, 1988], but for our purposes maximizing the capacity is less important than having a simple, non-iterative storage rule, so we will use an outer product rule to store hidden activity vectors in fast weights that decay rapidly.”
Replay in Deep Learning: Current Approaches and Missing Biological Elements (2021, positive): “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.”
The hardware lottery (2020, background): “Our own intelligence relies on decentralized local updates which surface a global signal in ways that are still not well understood (Lillicrap & Santoro, 2019; Marblestone et al., 2016; Bi & Poo, 1998).”
Scene segmentation by spike synchronization in reciprocally connected visual areas. II. Global assemblies and synchronization on larger space and time scales (2002, background): “Experiments (Markram et al. 1997; Bi and Poo 1998 ) have shown that synaptic connections are potentiated when the postsynaptic spike follows the presynaptic spike within 10–20 ms, but depressed when the postsynaptic spike precedes the presynaptic spike by 10–20 ms. An oscillating regime (at zero ph”
Distinct coincidence detectors govern the corticostriatal spike timing‐dependent plasticity (2010, result): “…(Fino et al. 2005) compared to the classical STDP described in different mammalian brain structures (Markram et al. 1997; Magee & Johnston, 1997; Bi & Poo, 1998; Debanne et al. 1998; but see Bell et al. (1997) and Tzounopoulos et al. (2004, 2007) for anti-Hebbian STDP, respectively, in the…”
Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits (2020, background): “The most common formulation of this is spike-timing-dependent plasticity (STDP), 93 wherein the timing of pre and postsynaptic spikes is what determines whether LTP or LTD occurs [4,68,69].”
A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity (2006, background): “the spike-timing dependent plasticity (STDP) mechanism [16], [17].”
Spike-Timing Dependent Plasticity for Evolved Robots (2002, background): “Directional damping is supported empirically by the observation that spike-driven potentiation is more pronounced than the expected linear variation at synapses of relatively low initial strength in cultured hippocampal cells (Bi & Poo, 1998).”
Computational models of reinforcement learning: the role of dopamine as a reward signal (2010, methodology): “Most of these models use a form of synaptic modification known as spike timing dependent plasticity (STDP), which is based on the temporal relationship between pre-and postsynaptic activation (see Fig. 5b; Bi and Poo 1998; Markram et al. 1997).”