Spike timing dependent synaptic plasticity in biological systems

Roberts, Patrick D.; Bell, Charles E.

doi:10.1007/s00422-002-0361-y

Cited by 138 publications

(80 citation statements)

References 79 publications

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“…Theoretical studies of the dynamics of neural networks have contributed to our understanding of how these networks might function [1,2]. Recent progress in the study of synaptic plasticity is opening up new opportunities for understanding how these networks can form [3,4,5]. One of the most important aspects of a functional neural network is that the strength of its connections can change in response to the history of its activities, that is, it can learn from experience [6,7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Mean-field theory of a plastic network of integrate-and-fire neurons

Chen

Jasnow

2010

Phys. Rev. E

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We consider a noise driven network of integrate-and-fire neurons. The network evolves as result of the activities of the neurons following spike-timing-dependent plasticity rules. We apply a self-consistent mean-field theory to the system to obtain the mean activity level for the system as a function of the mean synaptic weight, which predicts a first-order transition and hysteresis between a noise-dominated regime and a regime of persistent neural activity. Assuming Poisson firing statistics for the neurons, the plasticity dynamics of a synapse under the influence of the mean-field environment can be mapped to the dynamics of an asymmetric random walk in synapticweight space. Using a master-equation for small steps, we predict a narrow distribution of synaptic weights that scales with the square root of the plasticity rate for the stationary state of the system given plausible physiological parameter values describing neural transmission and plasticity. The dependence of the distribution on the synaptic weight of the mean-field environment allows us to determine the mean synaptic weight self-consistently. The effect of fluctuations in the total synaptic conductance and plasticity step sizes are also considered. Such fluctuations result in a smoothing of the first-order transition for low number of afferent synapses per neuron and a broadening of the synaptic weight distribution, respectively.

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Section: Introductionmentioning

confidence: 99%

Mean-field theory of a plastic network of integrate-and-fire neurons

Chen

Jasnow

2010

Phys. Rev. E

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“…Evolution of synaptic weights versus time as well as the final synaptic weight distributions after learning may again provide key insights. In particular, it is not clear from the results whether synaptic learning is additive (weight change does not depend on actual synaptic weight), or multiplicative (in which learning is a function of the weight) [9], [39], [40]. The latter seems more plausible based on the operation principle of these circuits, in that the conductance of the reverse current path through the memristor certainly depends on the instantaneous weight.…”

Section: Discussionmentioning

confidence: 99%

Spatio-temporal pattern recognition in neural circuits with memory-transistor-driven memristive synapses

Cantley

Ivans

Subramaniam

et al. 2017

2017 International Joint Conference on Neural Networks (IJCNN)

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“…17,18 Various modifications as a function of pulse timing have been reported for different synapses. [19][20][21] For example, in hippocampal neurons, potentiation (increase) of the synaptic strength is observed when the post-follows the presynaptic pulse, while depression (decrease) occurs when the pre-follows the postsynaptic pulse (asymmetric Hebbian learning). 16 This functionality can be successfully emulated with memristors 4,[22][23][24][25][26] and, empirically, it is described with exponential functions.…”

Section: Introductionmentioning

confidence: 99%

Mimicking of pulse shape-dependent learning rules with a quantum dot memristor

Maier

Hartmann

Dias

et al. 2016

Journal of Applied Physics

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We present the realization of four different learning rules with a quantum dot memristor by tuning the shape, the magnitude, the polarity and the timing of voltage pulses. The memristor displays a large maximum to minimum conductance ratio of about 57000 at zero bias voltage. The high and low conductances correspond to different amounts of electrons localized in quantum dots, which can be successively raised or lowered by the timing and shapes of incoming voltage pulses. Modifications of the pulse shapes allow altering the conductance change in dependence on the time difference. Hence, we are able to mimic different learning processes in neural networks with a single device. In addition, the device performance under pulsed excitation is emulated combining the Landauer-Büttiker formalism with a dynamic model for the quantum dot charging, which allows explaining the whole spectrum of learning responses in terms of structural parameters that can be adjusted during fabrication such as gating efficiencies and tunneling rates. The presented memristor may pave the way for future artificial synapses with a stimulus-dependent capability of learning.

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Spike timing dependent synaptic plasticity in biological systems

Cited by 138 publications

References 79 publications

Mean-field theory of a plastic network of integrate-and-fire neurons

Mean-field theory of a plastic network of integrate-and-fire neurons

Spatio-temporal pattern recognition in neural circuits with memory-transistor-driven memristive synapses

Mimicking of pulse shape-dependent learning rules with a quantum dot memristor

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