scholarly journals A spike-timing-dependent plasticity rule for single, clustered and distributed dendritic spines

2018 ◽  
Author(s):  
Sabrina Tazerart ◽  
Diana E. Mitchell ◽  
Soledad Miranda-Rottmann ◽  
Roberto Araya
Keyword(s):  
Dendritic Spines ◽  
Actin Polymerization ◽  
Pyramidal Neurons ◽  
Glutamate Release ◽  
Synaptic Strength ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Spine Shape ◽  
Stdp Rule

SUMMARYSpike-timing-dependent plasticity (STDP) has been extensively studied in cortical pyramidal neurons, however, the precise structural organization of excitatory inputs that supports STDP, as well as the structural, molecular and functional properties of dendritic spines during STDP remain unknown. Here we performed a spine STDP protocol using two-photon glutamate uncaging to mimic presynaptic glutamate release (pre) paired with somatically generated postsynaptic spikes (post). We found that the induction of STDP in single spines follows a classical Hebbian STDP rule, where pre-post pairings at timings that trigger LTP (t-LTP) produce shrinkage of the activated spine neck and a concomitant increase in its synaptic strength; and post-pre pairings that trigger LTD (t-LTD) decrease synaptic strength without affecting the activated spine shape. Furthermore, we tested whether the single spine-Hebbian STDP rule could be affected by the activation of neighboring (clustered) or distant (distributed) spines. Our results show that the induction of t-LTP in two clustered spines (<5 μm apart) enhances LTP through a mechanism dependent on local spine calcium accumulation and actin polymerization-dependent neck shrinkage, whereas t-LTD was disrupted by the activation of two clustered spines but recovered when spines were separated by >40 μm. These results indicate that synaptic cooperativity, induced by the co-activation of only two clustered spines, provides local dendritic depolarization and local calcium signals sufficient to disrupt t-LTD and extend the temporal window for the induction of t-LTP, leading to STDP only encompassing LTP.

A Non-Markovian Random Walk Underlies a Stochastic Model of Spike-Timing-Dependent Plasticity

2010 ◽  
Vol 22 (5) ◽  
pp. 1180-1230 ◽  
Author(s):  
Terry Elliott
Keyword(s):  
Random Walk ◽  
Stochastic Model ◽  
Synaptic Strength ◽  
Spike Timing ◽  
Spatial Average ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Strength Change ◽  
Single Spike ◽  
Switch Mechanism

A stochastic model of spike-timing-dependent plasticity (STDP) proposes that spike timing influences the probability but not the amplitude of synaptic strength change at single synapses. The classic, biphasic STDP profile emerges as a spatial average over many synapses presented with a single spike pair or as a temporal average over a single synapse presented with many spike pairs. We have previously shown that the model accounts for a variety of experimental data, including spike triplet results, and has a number of desirable theoretical properties, including being entirely self-stabilizing in all regions of parameter space. Our earlier analyses of the model have employed cumbersome spike-to-spike averaging arguments to derive results. Here, we show that the model can be reformulated as a non-Markovian random walk in synaptic strength, the step sizes being fixed as postulated. This change of perspective greatly simplifies earlier calculations by integrating out the proposed switch mechanism by which changes in strength are driven and instead concentrating on the changes in strength themselves. Moreover, this change of viewpoint is generative, facilitating further calculations that would be intractable, if not impossible, with earlier approaches. We prepare the machinery here for these later calculations but also briefly indicate how this machinery may be used by considering two particular applications.

Taming Fluctuations in a Stochastic Model of Spike-Timing-Dependent Plasticity

2009 ◽  
Vol 21 (12) ◽  
pp. 3363-3407 ◽  
Author(s):  
Terry Elliott ◽  
Konstantinos Lagogiannis
Keyword(s):  
Stochastic Model ◽  
Neuronal Development ◽  
Synaptic Strength ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Large Numbers ◽  
Large Fluctuations ◽  
Low Pass ◽  
The Mean

A stochastic model of spike-timing-dependent plasticity proposes that single synapses express fixed-amplitude jumps in strength, the amplitudes being independent of the spike time difference. However, the probability that a jump in strength occurs does depend on spike timing. Although the model has a number of desirable features, the stochasticity of response of a synapse introduces potentially large fluctuations into changes in synaptic strength. These can destabilize the segregated patterns of afferent connectivity characteristic of neuronal development. Previously we have taken these jumps to be small relative to overall synaptic strengths to control fluctuations, but doing so increases developmental timescales unacceptably. Here, we explore three alternative ways of taming fluctuations. First, a calculation of the variance for the change in synaptic strength shows that the mean change eventually dominates fluctuations, but on timescales that are too long. Second, it is possible that fluctuations in strength may cancel between synapses, but we show that correlations between synapses emasculate the law of large numbers. Finally, by separating plasticity induction and expression, we introduce a temporal window during which induction signals are low-pass-filtered before expression. In this way, fluctuations in strength are tamed, stabilizing segregated states of afferent connectivity.

Temporal Dynamics of Rate-Based Synaptic Plasticity Rules in a Stochastic Model of Spike-Timing-Dependent Plasticity

2008 ◽  
Vol 20 (9) ◽  
pp. 2253-2307 ◽  
Author(s):  
Terry Elliott
Keyword(s):  
Synaptic Plasticity ◽  
Stochastic Model ◽  
Parameter Space ◽  
Synaptic Strength ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Firing Rates ◽  
Typical Sequence ◽  
Synaptic Dynamics

In a recently proposed, stochastic model of spike-timing-dependent plasticity, we derived general expressions for the expected change in synaptic strength, ΔSn, induced by a typical sequence of precisely n spikes. We found that the rules ΔSn, n ≥ 3, exhibit regions of parameter space in which stable, competitive interactions between afferents are present, leading to the activity-dependent segregation of afferents on their targets. The rules ΔSn, however, allow an indefinite period of time to elapse for the occurrence of precisely n spikes, while most measurements of changes in synaptic strength are conducted over definite periods of time during which a potentially unknown number of spikes may occur. Here, therefore, we derive an expression, ΔS(t), for the expected change in synaptic strength of a synapse experiencing an average sequence of spikes of typical length occurring during a fixed period of time, t. We find that the resulting synaptic plasticity rule Δ S(t) exhibits a number of remarkable properties. It is an entirely self-stabilizing learning rule in all regions of parameter space. Further, its parameter space is carved up into three distinct, contiguous regions in which the exhibited synaptic interactions undergo different transitions as the time t is increased. In one region, the synaptic dynamics change from noncompetitive to competitive to entirely depressing. In a second region, the dynamics change from noncompetitive to competitive without the second transition to entirely depressing dynamics. In a third region, the dynamics are always noncompetitive. The locations of these regions are not fixed in parameter space but may be modified by changing the mean presynaptic firing rates. Thus, neurons may be moved among these three different regions and so exhibit different sets of synaptic dynamics depending on their mean firing rates.

Role of AMPA and NMDA Receptors and Back-Propagating Action Potentials in Spike Timing–Dependent Plasticity

2010 ◽  
Vol 103 (1) ◽  
pp. 47-54 ◽  
Author(s):  
Marco Fuenzalida ◽  
David Fernández de Sevilla ◽  
Alejandro Couve ◽  
Washington Buño
Keyword(s):  
Nmda Receptors ◽  
Pyramidal Neurons ◽  
Long Term Potentiation ◽  
Receptor Activation ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Cellular Mechanisms ◽  
Dependent Plasticity ◽  
Term Potentiation

The cellular mechanisms that mediate spike timing–dependent plasticity (STDP) are largely unknown. We studied in vitro in CA1 pyramidal neurons the contribution of AMPA and N-methyl-d-aspartate (NMDA) components of Schaffer collateral (SC) excitatory postsynaptic potentials (EPSPs; EPSPAMPA and EPSPNMDA) and of the back-propagating action potential (BAP) to the long-term potentiation (LTP) induced by a STDP protocol that consisted in pairing an EPSP and a BAP. Transient blockade of EPSPAMPA with 7-nitro-2,3-dioxo-1,4-dihydroquinoxaline-6-carbonitrile (CNQX) during the STDP protocol prevented LTP. Contrastingly LTP was induced under transient inhibition of EPSPAMPA by combining SC stimulation, an imposed EPSPAMPA-like depolarization, and BAP or by coupling the EPSPNMDA evoked under sustained depolarization (approximately −40 mV) and BAP. In Mg2+-free solution EPSPNMDA and BAP also produced LTP. Suppression of EPSPNMDA or BAP always prevented LTP. Thus activation of NMDA receptors and BAPs are needed but not sufficient because AMPA receptor activation is also obligatory for STDP. However, a transient depolarization of another origin that unblocks NMDA receptors and a BAP may also trigger LTP.

scholarly journals STDP and the distribution of preferred phases in the whisker system

2021 ◽  
Vol 17 (9) ◽  
pp. e1009353
Author(s):  
Nimrod Sherf ◽  
Maoz Shamir
Keyword(s):  
Drift Velocity ◽  
Spike Timing ◽  
Neural Population ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Wide Range ◽  
Stdp Rule ◽  
Natural Test ◽  
Rats And Mice ◽  
Whisker System

Rats and mice use their whiskers to probe the environment. By rhythmically swiping their whiskers back and forth they can detect the existence of an object, locate it, and identify its texture. Localization can be accomplished by inferring the whisker’s position. Rhythmic neurons that track the phase of the whisking cycle encode information about the azimuthal location of the whisker. These neurons are characterized by preferred phases of firing that are narrowly distributed. Consequently, pooling the rhythmic signal from several upstream neurons is expected to result in a much narrower distribution of preferred phases in the downstream population, which however has not been observed empirically. Here, we show how spike timing dependent plasticity (STDP) can provide a solution to this conundrum. We investigated the effect of STDP on the utility of a neural population to transmit rhythmic information downstream using the framework of a modeling study. We found that under a wide range of parameters, STDP facilitated the transfer of rhythmic information despite the fact that all the synaptic weights remained dynamic. As a result, the preferred phase of the downstream neuron was not fixed, but rather drifted in time at a drift velocity that depended on the preferred phase, thus inducing a distribution of preferred phases. We further analyzed how the STDP rule governs the distribution of preferred phases in the downstream population. This link between the STDP rule and the distribution of preferred phases constitutes a natural test for our theory.

Inhibition of the slow afterhyperpolarization restores the classical spike timing-dependent plasticity rule obeyed in layer 2/3 pyramidal cells of the prefrontal cortex

2012 ◽  
Vol 107 (1) ◽  
pp. 205-215 ◽  
Author(s):  
Aleksey V. Zaitsev ◽  
Roger Anwyl
Keyword(s):  
Prefrontal Cortex ◽  
Pyramidal Cells ◽  
Long Term Potentiation ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Layer 2 ◽  
Stdp Rule ◽  
Stimulation Of

The induction of long-term potentiation (LTP) and long-term depression (LTD) of excitatory postsynaptic currents was investigated in proximal synapses of layer 2/3 pyramidal cells of the rat medial prefrontal cortex. The spike timing-dependent plasticity (STDP) induction protocol of negative timing, with postsynaptic leading presynaptic stimulation of action potentials (APs), induced LTD as expected from the classical STDP rule. However, the positive STDP protocol of presynaptic leading postsynaptic stimulation of APs predominantly induced a presynaptically expressed LTD rather than the expected postsynaptically expressed LTP. Thus the induction of plasticity in layer 2/3 pyramidal cells does not obey the classical STDP rule for positive timing. This unusual STDP switched to a classical timing rule if the slow Ca2+-dependent, K+-mediated afterhyperpolarization (sAHP) was inhibited by the selective blocker N-trityl-3-pyridinemethanamine (UCL2077), by the β-adrenergic receptor agonist isoproterenol, or by the cholinergic agonist carbachol. Thus we demonstrate that neuromodulators can affect synaptic plasticity by inhibition of the sAHP. These findings shed light on a fundamental question in the field of memory research regarding how environmental and behavioral stimuli influence LTP, thereby contributing to the modulation of memory.

scholarly journals Emergence of oscillations via spike timing dependent plasticity

2018 ◽  
Author(s):  
Sarit Soloduchin ◽  
Maoz Shamir
Keyword(s):  
Phase Diagram ◽  
Neuronal Network ◽  
Learning Process ◽  
Learning Rule ◽  
Spike Timing ◽  
Oscillatory Activity ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Wide Range ◽  
Stdp Rule

AbstractNeuronal oscillatory activity has been reported in relation to a wide range of cognitive processes. In certain cases changes in oscillatory activity has been associated with pathological states. Although the specific role of these oscillations has yet to be determined, it is clear that neuronal oscillations are abundant in the central nervous system. These observations raise the question of the origin of these oscillations; and specifically whether the mechanisms responsible for the generation and stabilization of these oscillations are genetically hard-wired or whether they can be acquired via a learning process.Here we focus on spike timing dependent plasticity (STDP) to investigate whether oscillatory activity can emerge in a neuronal network via an unsupervised learning process of STDP dynamics, and if so, what features of the STDP learning rule govern and stabilize the resultant oscillatory activity?Here, the STDP dynamics of the effective coupling between two competing neuronal populations with reciprocal inhibitory connections was analyzed using the phase-diagram of the system that depicts the possible dynamical states of the network as a function of the effective inhibitory couplings. This phase diagram yields a rich repertoire of possible dynamical behaviors including regions of different fixed point solutions, bi-stability and a region in which the system exhibits oscillatory activity. STDP introduces dynamics for the inhibitory couplings themselves and hence induces a flow in the phase diagram. We investigate the conditions for the flow to converge to an oscillatory state of the neuronal network and then characterize how the features of the STDP rule govern and stabilize these oscillations.

scholarly journals Altered spike timing-dependent plasticity rules in physiological calcium

2020 ◽  
Author(s):  
Yanis Inglebert ◽  
Johnatan Aljadeff ◽  
Nicolas Brunel ◽  
Dominique Debanne
Keyword(s):  
Specific Activity ◽  
Spike Timing ◽  
Plasticity Model ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Hippocampal Synapses ◽  
Stdp Rule ◽  
Intracellular Ca

AbstractLike many forms of long-term synaptic plasticity, spike-timing-dependent plasticity (STDP) depends on intracellular Ca2+ signaling for its induction. Yet, all in vitro studies devoted to STDP used abnormally high external Ca2+ concentration. We measured STDP at the CA3-CA1 hippocampal synapses under different extracellular Ca2+ concentrations and found that the sign, shape and magnitude of plasticity strongly depend on Ca2+. A pre-post protocol that results in robust LTP in high Ca2+, yielded only LTD or no plasticity in the physiological Ca2+ range. LTP could be restored by either increasing the number of post-synaptic spikes or increasing the pairing frequency. A calcium-based plasticity model in which depression and potentiation depend on post-synaptic Ca2+ transients was found to fit quantitatively all the data, provided NMDA receptor-mediated non-linearities were implemented. In conclusion, STDP rule is profoundly altered in physiological Ca2+ but specific activity regimes restore a classical STDP profile.

scholarly journals STDP and the distribution of preferred phases in the whisker system

2021 ◽  
Author(s):  
Nimrod Sherf ◽  
Maoz Shamir
Keyword(s):  
Drift Velocity ◽  
Spike Timing ◽  
Neural Population ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Wide Range ◽  
Stdp Rule ◽  
Natural Test ◽  
Rats And Mice ◽  
Whisker System

Rats and mice use their whiskers to probe the environment. By rhythmically swiping their whiskers back and forth they can detect the existence of an object, locate it, and identify its texture. Localization can be accomplished by inferring the position of the whisker. Rhythmic neurons that track the phase of the whisking cycle encode information about the azimuthal location of the whisker. These neurons are characterized by preferred phases of firing that are narrowly distributed. Consequently, pooling the rhythmic signal from several upstream neurons is expected to result in a much narrower distribution of preferred phases in the downstream population, which however has not been observed empirically. Here, we show how spike timing dependent plasticity (STDP) can provide a solution to this conundrum. We investigated the effect of STDP on the utility of a neural population to transmit rhythmic information downstream using the framework of a modeling study. We found that under a wide range of parameters, STDP facilitated the transfer of rhythmic information despite the fact that all the synaptic weights remained dynamic. As a result, the preferred phase of the downstream neuron was not fixed, but rather drifted in time at a drift velocity that depended on the preferred phase, thus inducing a distribution of preferred phases. We further analyzed how the STDP rule governs the distribution of preferred phases in the downstream population. This link between the STDP rule and the distribution of preferred phases constitutes a natural test for our theory.

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