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We have derived a class of synaptic plasticity rules for reinforcement learning in a complex neuronal cell model with NMDA-mediated dendritic nonlinearities.
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This paper presents an analytical model, derives a class of MATE algorithms, and proves their convergence.
Therefore, it is the aim of this paper to derive a class of variable-coefficient tempered fractional diffusion models.
The procedure is then applied to derive a class of continuum theories for a two-dimensional square array of particles.
We derive a class of algorithms based on projection to calculate either the localized orbitals or the density matrix.
With the Cohen's class distributions, we can easily derive a class of methods to estimate time-varying spectrum using the Cohen's class distributions by replacing the spectrogram in (12) by any bilinear TFR, (17).
By algebraic manipulation of the gradient formula leading to the basic ZR-rule, we derive a class of learning rules where synaptic plasticity is also modulated by somatic responses, in addition to reward and quantities local to the NMDA-zone.
Then a concrete design procedure is derived for a class of electro-mechanical systems.
An overall effectiveness factor is derived for a class of nonisothermal multiphase catalytic reactions based on a perturbation technique.
A new Kloosterman sum identity over F2m is derived and a class of rational function pairs that satisfy the identity is presented.
In this paper two feedforward/feedback control schemes are derived for a class of nonlinear systems: one is linear and the other is nonlinear.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com