Temporal asymmetry in spike timing-dependent synaptic plasticity

Bi, Guo-Qiang; Wang, Huai Xing

doi:10.1016/s0031-9384(02)00933-2

Cited by 55 publications

(43 citation statements)

References 30 publications

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“…Asymmetric learning windows have repeatedly been found experimentally (e.g. see [11][12][13]). This asymmetry is a prerequisite, at least in phase oscillator networks, to observe the coexistence of states characterized by different levels of synchrony [3,3].…”

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confidence: 65%

Emergence of Slow Collective Oscillations in Neural Networks with Spike-Timing Dependent Plasticity

2013

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The collective dynamics of excitatory pulse coupled neurons with spike timing dependent plasticity (STDP) is studied. The introduction of STDP induces persistent irregular oscillations between strongly and weakly synchronized states, reminiscent of brain activity during slow-wave sleep. We explain the oscillations by a mechanism, the Sisyphus Effect, caused by a continuous feedback between the synaptic adjustments and the coherence in the neural firing. Due to this effect, the synaptic weights have oscillating equilibrium values, and this prevents the system from relaxing into a stationary macroscopic state.

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confidence: 65%

Emergence of Slow Collective Oscillations in Neural Networks with Spike-Timing Dependent Plasticity

2013

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“…Studies in different brain regions and under varying experimental conditions have revealed a plethora of different types of STDP (7). Experimental protocols using a diversity of spike patterns have furthermore highlighted the complexity and nonlinearity of plasticity rules in different systems (6,(8)(9)(10)(11). However, how the diversity and nonlinearity of plasticity results emerge from the interplay between the underlying biochemical synaptic machinery and activity patterns remains elusive.…”

mentioning

confidence: 99%

Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location

Graupner

Brunel

2012

Proc. Natl. Acad. Sci. U.S.A.

338

553

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Multiple stimulation protocols have been found to be effective in changing synaptic efficacy by inducing long-term potentiation or depression. In many of those protocols, increases in postsynaptic calcium concentration have been shown to play a crucial role. However, it is still unclear whether and how the dynamics of the postsynaptic calcium alone determine the outcome of synaptic plasticity. Here, we propose a calcium-based model of a synapse in which potentiation and depression are activated above calcium thresholds. We show that this model gives rise to a large diversity of spike timing-dependent plasticity curves, most of which have been observed experimentally in different systems. It accounts quantitatively for plasticity outcomes evoked by protocols involving patterns with variable spike timing and firing rate in hippocampus and neocortex. Furthermore, it allows us to predict that differences in plasticity outcomes in different studies are due to differences in parameters defining the calcium dynamics. The model provides a mechanistic understanding of how various stimulation protocols provoke specific synaptic changes through the dynamics of calcium concentration and thresholds implementing in simplified fashion protein signaling cascades, leading to long-term potentiation and long-term depression. The combination of biophysical realism and analytical tractability makes it the ideal candidate to study plasticity at the synapse, neuron, and network levels.

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“…Given the highly nonlinear involvement of postsynaptic spike times and PSP in the optimal synaptic update rule, a simple interpretation in terms of pairs or pre-and postsynaptic spikes as in many standard models of synaptic plasticity (30,31) can only capture a small portion of synaptic plasticity phenomena. The optimal learning rule suggests that nonlinear phenomena (32)(33)(34)(35) are potentially highly relevant.…”

Section: Discussionmentioning

confidence: 99%

Generalized Bienenstock–Cooper–Munro rule for spiking neurons that maximizes information transmission

Toyoizumi

Pfister

Aihara

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

113

154

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Maximization of information transmission by a spiking-neuron model predicts changes of synaptic connections that depend on timing of pre-and postsynaptic spikes and on the postsynaptic membrane potential. Under the assumption of Poisson firing statistics, the synaptic update rule exhibits all of the features of the Bienenstock-Cooper-Munro rule, in particular, regimes of synaptic potentiation and depression separated by a sliding threshold. Moreover, the learning rule is also applicable to the more realistic case of neuron models with refractoriness, and is sensitive to correlations between input spikes, even in the absence of presynaptic rate modulation. The learning rule is found by maximizing the mutual information between presynaptic and postsynaptic spike trains under the constraint that the postsynaptic firing rate stays close to some target firing rate. An interpretation of the synaptic update rule in terms of homeostatic synaptic processes and spiketiming-dependent plasticity is discussed.computational neuroscience ͉ information theory ͉ learning ͉ spiking-neuron model ͉ synaptic plasticity T he efficacy of synaptic connections between neurons in the brain is not fixed, but it varies, depending on the firing frequency of presynaptic neurons (1, 2), the membrane potential of the postsynaptic neuron (3), spike timing (4-6), and intracellular parameters such as the calcium concentration; for a review see ref. 7. During the last decades, a large number of theoretical concepts and mathematical models have emerged that have helped to understand the functional consequences of synaptic modifications, in particular, long-term potentiation (LTP) and long-term depression (LTD) during development, learning, and memory; for reviews see (8)(9)(10). Apart from the work of Hebb (11), one of the most influential theoretical concepts has been the Bienenstock-Cooper-Munro (BCM) model originally developed to account for cortical organization and receptive field properties during development (12). The model predicted (i) regimes of both LTD and LTP, depending on the state of the postsynaptic neuron, and (ii) a sliding threshold that separates the two regimes. Both predictions i and ii have subsequently been confirmed experimentally (2,13,14).In this paper, we construct a bridge between the BCM model and a seemingly unconnected line of research in theoretical neuroscience centered around the concept of optimality.There are indications that several components of neural systems show close to optimal performance (15-17). Instead of looking at a specific implementation of synaptic changes, defined by a rule such as in the BCM model, we therefore ask what would be the optimal synaptic update rule so as to guarantee that a spiking neuron transmits as much information as possible? Information theoretic concepts have been used by several researchers because they allow to compare performance of neural systems with a fundamental theoretical limit (16, 17), but optimal synaptic update rules have so far been mostly restricted to a pure r...

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Temporal asymmetry in spike timing-dependent synaptic plasticity

Cited by 55 publications

References 30 publications

Emergence of Slow Collective Oscillations in Neural Networks with Spike-Timing Dependent Plasticity

Emergence of Slow Collective Oscillations in Neural Networks with Spike-Timing Dependent Plasticity

Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location

Generalized Bienenstock–Cooper–Munro rule for spiking neurons that maximizes information transmission

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