Spike-timing-dependent plasticity (STDP) is a biological process that adjusts the strength of connections between neurons in the brain.
The process adjusts the connection strengths based on the relative timing of a particular neuron's output and input action potentials (or spikes).
The process continues until a subset of the initial set of connections remain, while the influence of all others is reduced to 0.
This proposal apparently passed unnoticed in the neuroscientific community, and subsequent experimentation was conceived independently of these early suggestions.
Steward in 1983[2] and examined the effect of relative timing of pre- and postsynaptic action potentials at millisecond level on plasticity.
In studies on neuromuscular synapses carried out by Y. Dan and Mu-ming Poo in 1992,[3] and on the hippocampus by D. Debanne, B. Gähwiler, and S. Thompson in 1994,[4] showed that asynchronous pairing of postsynaptic and synaptic activity induced long-term synaptic depression.
However, STDP was more definitively demonstrated by Henry Markram in his postdoc period till 1993 in Bert Sakmann's lab (SFN and Phys Soc abstracts in 1994–1995) which was only published in 1997.
Further work, by Guoqiang Bi, Li Zhang, and Huizhong Tao in Mu-Ming Poo's lab in 1998,[6] continued the mapping of the entire time course relating pre- and post-synaptic activity and synaptic change, to show that in their preparation synapses that are activated within 5–20 ms before a postsynaptic spike are strengthened, and those that are activated within a similar time window after the spike are transiently weakened.
[8][9] As suggested by Taylor[1] in 1973, Hebbian learning rules might create informationally efficient coding in bundles of related neurons.
While STDP was first discovered in cultured neurons and brain slice preparations, it has also been demonstrated by sensory stimulation of intact animals.
[10] Postsynaptic NMDA receptors (NMDARs) are highly sensitive to the membrane potential (see coincidence detection in neurobiology).
Due to their high permeability for calcium, they generate a local chemical signal that is largest when the back-propagating action potential in the dendrite arrives shortly after the synapse was active (pre-post spiking), when NMDA and AMPA receptors are still bound to glutamate.
The mechanism for spike-timing-dependent depression is less well understood, but often involves either postsynaptic voltage-dependent calcium entry/mGluR activation, or retrograde endocannabinoids and presynaptic NMDARs.
[13] In addition, it has become evident that the presynaptic neural firing needs to consistently predict the postsynaptic firing for synaptic plasticity to occur robustly,[14] mirroring at a synaptic level what is known about the importance of contingency in classical conditioning, where zero contingency procedures prevent the association between two stimuli.
For the most efficient STDP, the presynaptic and postsynaptic signal has to be separated by approximately a dozen milliseconds.
However, events happening within a couple of minutes can typically be linked together by the hippocampus as episodic memories.
To resolve this contradiction, a mechanism relying on the theta waves and the phase precession has been proposed: Representations of different memory entities (such as a place, face, person etc.)
Expected, ongoing, and completed entities have early, intermediate and late theta phases, respectively.
In the CA3 region of the hippocampus, the recurrent network turns entities with neighboring theta phases into coincident ones thereby allowing STDP to link them together.
Experimentally detectable memory sequences are created this way by reinforcing the connection between subsequent (neighboring) representations.
[15] The principles of STDP can be utilized in the training of artificial spiking neural networks.
Using this approach the weight of a connection between two neurons is increased if the time at which a presynaptic spike (