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Spike-timing-dependent plasticity

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Spike-timing-dependent plasticity (STDP) is a biological process that adjusts the strength of synaptic connections between neurons based on the relative timing of their action potentials (or spikes). It is a temporally sensitive form of synaptic plasticity, meaning that the efficiency of synaptic transmission is modified by the timing of neural activity. When a presynaptic neuron consistently fires just before a postsynaptic neuron, the connection is typically strengthened—a process known as long-term potentiation (LTP). If the timing is reversed and the presynaptic neuron fires after the postsynaptic neuron, the connection is weakened through long-term depression (LTD). [1][2]

STDP is considered a key mechanism in learning and memory formation and helps explain activity-dependent development of neural circuits. It has been observed in multiple brain regions, including the hippocampus, neocortex, and visual system, and has been widely implemented in computational models of biologically inspired learning algorithms and network dynamics[3][4].

Mechanism

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Under the STDP process, if an input spike to a neuron tends, on average, to occur immediately before that neuron's output spike, then that particular input is made somewhat stronger. If an input spike tends, on average, to occur immediately after an output spike, then that particular input is made somewhat weaker hence: "spike-timing-dependent plasticity". Thus, inputs that might be the cause of the post-synaptic neuron's excitation are made even more likely to contribute in the future, whereas inputs that are not the cause of the post-synaptic spike are made less likely to contribute in the future. The process continues until a subset of the initial set of connections remain, while the influence of all others is reduced to 0. Since a neuron produces an output spike when many of its inputs occur within a brief period, the subset of inputs that remain are those that tended to be correlated in time. In addition, since the inputs that occur before the output are strengthened, the inputs that provide the earliest indication of correlation will eventually become the final input to the neuron.

Spike-timing-dependent plasticity (STDP) depends on the precise timing of action potentials (spikes) between presynaptic and postsynaptic neurons. If a presynaptic spike occurs shortly before a postsynaptic spike—typically within a window of 10 to 20 milliseconds—the synapse is strengthened, a process known as long-term potentiation (LTP). If the presynaptic spike follows the postsynaptic spike, the synapse is weakened, resulting in long-term depression (LTD)[1][2]. This timing-dependent adjustment of synaptic strength enables neurons to reinforce inputs that are likely to have contributed to their activation while weakening those that were not causally involved [3].

The effect of STDP is cumulative: repeated pairings of causally timed spikes strengthen the relevant synapses, while others weaken over time. Eventually, this leads to a subset of inputs being selectively retained, particularly those that tend to fire together within narrow temporal windows. As a result, the neuron becomes tuned to detect and respond preferentially to input patterns that consistently precede its own activation, which may reflect meaningful or predictive features of the environment [4].

At the molecular level, STDP is primarily mediated by N-methyl-D-aspartate receptors (NMDA receptors) located on the postsynaptic membrane. These receptors function as coincidence detectors: they require both the release of glutamate from the presynaptic terminal and sufficient depolarization of the postsynaptic membrane to become fully activated. When these conditions are met—such as when a back-propagating action potential follows synaptic input—the NMDA receptor channel opens, allowing calcium ions to enter the postsynaptic cell [2][5].

The amplitude and duration of calcium influx determine the direction of synaptic change. High-amplitude, rapid calcium transients typically trigger LTP via the activation of calcium-sensitive kinases, while lower, prolonged calcium levels are associated with LTD, in part due to the activation of phosphatases [2][6]. The spike-timing rule is therefore shaped by intracellular signaling cascades that translate calcium signals into long-term structural or biochemical changes at the synapse.

Although the mechanisms of LTP are relatively well understood, the pathways underlying spike-timing-dependent LTD can vary. LTD may involve voltage-dependent calcium entry through other channels, activation of metabotropic glutamate receptors, or the release of retrograde messengers such as endocannabinoids. In some synapses, presynaptic NMDA receptors also contribute to LTD by modulating neurotransmitter release[6].

The specific shape of the STDP learning window—the curve that relates spike timing difference to synaptic change—differs across brain regions and cell types. Many synapses exhibit an asymmetric window favoring LTP for pre-before-post timing and LTD for post-before-pre. However, other synapses display symmetric, anti-Hebbian, or frequency-dependent patterns, particularly under different neuromodulatory conditions or in inhibitory circuits [1][4][7].

History

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In 1973, M. M. Taylor[8] suggested that if synapses were strengthened for which a presynaptic spike occurred just before a postsynaptic spike more often than the reverse (Hebbian learning), while with the opposite timing or in the absence of a closely timed presynaptic spike, synapses were weakened (anti-Hebbian learning), the result would be an informationally efficient recoding of input patterns. This proposal apparently passed unnoticed in the neuroscientific community, and subsequent experimentation was conceived independently of these early suggestions.

Early experiments on associative plasticity were carried out by W. B. Levy and O. Steward in 1983[9] and examined the effect of relative timing of pre- and postsynaptic action potentials at millisecond level on plasticity. Bruce McNaughton contributed much to this area, too. In studies on neuromuscular synapses carried out by Y. Dan and Mu-ming Poo in 1992,[10] and on the hippocampus by D. Debanne, B. Gähwiler, and S. Thompson in 1994,[11] 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.[12] C. Bell and co-workers also found a form of STDP in the cerebellum. Henry Markram used dual patch clamping techniques to repetitively activate pre-synaptic neurons 10 milliseconds before activating the post-synaptic target neurons, and found the strength of the synapse increased. When the activation order was reversed so that the pre-synaptic neuron was activated 10 milliseconds after its post-synaptic target neuron, the strength of the pre-to-post synaptic connection decreased. Further work, by Guoqiang Bi, Li Zhang, and Huizhong Tao in Mu-Ming Poo's lab in 1998,[13] 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. It has since been shown that the initially highly asymmetric STDP window turns into a more symmetric "LTP only" window three days after induction.[14] Spike-timing-dependent plasticity is thought to be a substrate for Hebbian learning during development.[15][16] As suggested by Taylor[8] 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.[17]

From Hebbian rule to STDP

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According to the Hebbian rule, synapses increase their efficiency if the synapse persistently takes part in firing the postsynaptic target neuron. Similarly, the efficiency of synapses decreases when the firing of their presynaptic targets is persistently independent of firing their postsynaptic ones. These principles are often simplified in the mnemonics: those who fire together, wire together; and those who fire out of sync, lose their link. However, if two neurons fire exactly at the same time, then one cannot have caused, or taken part in firing the other. Instead, to take part in firing the postsynaptic neuron, the presynaptic neuron needs to fire just before the postsynaptic neuron. Experiments that stimulated two connected neurons with varying interstimulus asynchrony confirmed the importance of temporal relation implicit in Hebb's principle: for the synapse to be potentiated or depressed, the presynaptic neuron has to fire just before or just after the postsynaptic neuron, respectively.[18] In addition, it has become evident that the presynaptic neural firing needs to consistently predict the postsynaptic firing for synaptic plasticity to occur robustly,[19] 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.

Role in hippocampal learning

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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.) are repeated on each theta cycle at a given theta phase during the episode to be remembered. 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.[20]

Computational models and applications

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Training spiking neural networks

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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 () occurs is shortly before the time of a post synaptic spike(), ie. and . The size of the weight increase is dependent on the value of and decreases exponentially as the value of increases given by the equation:

where is the maximum possible change and is the time constant.

If the opposite scenario occurs ie a post synaptic spike occurs before a presynaptic spike then the weight is instead reduced according to the equation:

Where and serve the same function of defining the maximum possible change and time constant as before respectively.

The parameters that define the decay profile (,, etc.) do not necessarily have to be fixed across the entire network and different synapses may have different shapes associated with them.

Biological evidence suggests that this pairwise STDP approach cannot give a complete description of a biological neuron and more advanced approaches which look at symmetric triplets of spikes (pre-post-pre, post-pre-post) have been developed and these are believed to be more biologically plausible. [21]

See also

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References

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  1. ^ a b c Bi, Guo-qiang; Poo, Mu-ming (1998-12-15). "Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type". The Journal of Neuroscience. 18 (24): 10464–10472. doi:10.1523/JNEUROSCI.18-24-10464.1998. ISSN 0270-6474. PMC 6793365. PMID 9852584.
  2. ^ a b c d Caporale, Natalia; Dan, Yang (2008-07-01). "Spike Timing–Dependent Plasticity: A Hebbian Learning Rule". Annual Review of Neuroscience. 31 (1): 25–46. doi:10.1146/annurev.neuro.31.060407.125639. ISSN 0147-006X. PMID 18275283.
  3. ^ a b Dan, Yang; Poo, Mu-Ming (July 2006). "Spike Timing-Dependent Plasticity: From Synapse to Perception". Physiological Reviews. 86 (3): 1033–1048. doi:10.1152/physrev.00030.2005. ISSN 0031-9333. PMID 16816145.
  4. ^ a b c Feldman, Daniel E. (August 2012). "The Spike-Timing Dependence of Plasticity". Neuron. 75 (4): 556–571. doi:10.1016/j.neuron.2012.08.001. PMC 3431193. PMID 22920249.
  5. ^ Magee, Jeffrey C.; Grienberger, Christine (2020-07-08). "Synaptic Plasticity Forms and Functions". Annual Review of Neuroscience. 43: 95–117. doi:10.1146/annurev-neuro-090919-022842. ISSN 0147-006X.
  6. ^ a b Lisman, John; Cooper, Katherine; Sehgal, Megha; Silva, Alcino J. (March 2018). "Memory formation depends on both synapse-specific modifications of synaptic strength and cell-specific increases in excitability". Nature Neuroscience. 21 (3): 309–314. doi:10.1038/s41593-018-0076-6. ISSN 1546-1726. PMC 5915620. PMID 29434376.
  7. ^ Morrison, Abigail; Diesmann, Markus; Gerstner, Wulfram (2008-06-01). "Phenomenological models of synaptic plasticity based on spike timing". Biological Cybernetics. 98 (6): 459–478. doi:10.1007/s00422-008-0233-1. ISSN 1432-0770. PMC 2799003. PMID 18491160.
  8. ^ a b Taylor MM (1973). "The Problem of Stimulus Structure in the Behavioural Theory of Perception". South African Journal of Psychology. 3: 23–45.
  9. ^ Levy WB, Steward O (April 1983). "Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus". Neuroscience. 8 (4): 791–7. CiteSeerX 10.1.1.365.5814. doi:10.1016/0306-4522(83)90010-6. PMID 6306504. S2CID 16184572. [1] Archived 2020-11-11 at the Wayback Machine
  10. ^ Dan Y, Poo MM (1992). "Hebbian depression of isolated neuromuscular synapses in vitro". Science. 256 (5063): 1570–73. Bibcode:1992Sci...256.1570D. doi:10.1126/science.1317971. PMID 1317971.
  11. ^ Debanne D, Gähwiler B, Thompson S (1994). "Asynchronous pre- and postsynaptic activity induces associative long-term depression in area CA1 of the rat hippocampus in vitro". Proceedings of the National Academy of Sciences of the United States of America. 91 (3): 1148–52. Bibcode:1994PNAS...91.1148D. doi:10.1073/pnas.91.3.1148. PMC 521471. PMID 7905631.
  12. ^ Markram H, Lübke J, Frotscher M, Sakmann B (January 1997). "Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs" (PDF). Science. 275 (5297): 213–5. doi:10.1126/science.275.5297.213. PMID 8985014. S2CID 46640132.
  13. ^ Bi GQ, Poo MM (15 December 1998). "Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type". Journal of Neuroscience. 18 (24): 10464–72. doi:10.1523/JNEUROSCI.18-24-10464.1998. PMC 6793365. PMID 9852584.
  14. ^ Anisimova, Margarita; van Bommel, Bas; Mikhaylova, Marina; Wiegert, J. Simon; Oertner, Thomas G.; Gee, Christine E. (2019-12-03). "Spike-timing-dependent plasticity rewards synchrony rather than causality". doi:10.1101/863365. Retrieved 2024-08-29. {{cite journal}}: Cite journal requires |journal= (help)
  15. ^ Gerstner W, Kempter R, van Hemmen JL, Wagner H (September 1996). "A neuronal learning rule for sub-millisecond temporal coding". Nature. 383 (6595): 76–78. Bibcode:1996Natur.383...76G. doi:10.1038/383076a0. PMID 8779718. S2CID 4319500.
  16. ^ Song S, Miller KD, Abbott LF (September 2000). "Competitive Hebbian learning through spike-timing-dependent synaptic plasticity". Nature Neuroscience. 3 (9): 919–26. doi:10.1038/78829. PMID 10966623. S2CID 9530143.
  17. ^ Meliza CD, Dan Y (2006), "Receptive-field modification in rat visual cortex induced by paired visual stimulation and single-cell spiking", Neuron, 49 (2): 183–189, doi:10.1016/j.neuron.2005.12.009, PMID 16423693
  18. ^ Caporale N.; Dan Y. (2008). "Spike timing-dependent plasticity: a Hebbian learning rule". Annual Review of Neuroscience. 31: 25–46. doi:10.1146/annurev.neuro.31.060407.125639. PMID 18275283.
  19. ^ Bauer E. P.; LeDoux J. E.; Nader K. (2001). "Fear conditioning and LTP in the lateral amygdala are sensitive to the same stimulus contingencies". Nature Neuroscience. 4 (7): 687–688. doi:10.1038/89465. PMID 11426221. S2CID 33130204.
  20. ^ Kovács KA (September 2020). "Episodic Memories: How do the Hippocampus and the Entorhinal Ring Attractors Cooperate to Create Them?". Frontiers in Systems Neuroscience. 14: 68. doi:10.3389/fnsys.2020.559186. PMC 7511719. PMID 33013334.
  21. ^ Taherkhani, Aboozar; Belatreche, Ammar; Li, Yuhua; Cosma, Georgina; Maguire, Liam P.; McGinnity, T. M. (2020-02-01). "A review of learning in biologically plausible spiking neural networks". Neural Networks. 122: 253–272. doi:10.1016/j.neunet.2019.09.036. ISSN 0893-6080. PMID 31726331.

Further reading

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