Here, we propose to realise the sum-over-trips framework by a length-priority method.
In this paper, we introduced a number of efficient algorithms for the computational realisation of the sum-over-trips framework and assessed their convergence.
This complexity in neuronal morphologies across different types of neurons is expected to affect the convergence of computational implementations of the sum-over-trips framework.
In this paper, we introduce a number of efficient algorithms for realisation of the sum-over-trips framework and investigate the convergence of these algorithms on different dendritic geometries.
This sum-over-trips framework is built on a path integral formulation and enables the calculation of the Green's function on an arbitrary dendritic geometry as a convergent infinite series solution.
Our results clearly indicate that the convergence of the realisation of the sum-over-trips framework by either the four-classes or the length-priority method strongly depends on a dendritic geometry.
Earlier work of Coombes et al. [17] demonstrated that the Green's function for a single cell with resonant membrane can be constructed by generalising the "sum-over-trips" framework of Abbott et al. [15, 16] for passive dendrites.
The relative error ε of the approximation of G i j ( x, y, t ) is shown as a function of the number of trips in the sum-over-trips framework for injection at y and measurement at x on the dendritic trees in Fig. 4.
We assume that this network receives an input at the location y 0. To study the dynamics of this network, we use the "sum-over-trips" framework and construct the Green's functions G ˆ 1 ( x, y 0, ω ) and G ˆ 2 ( x, y 0, ω ) for Cell 1 and Cell 2, respectively.
We introduce the following table that associates individual coefficients in the "sum-over-trips" framework with the syllables: As the cells are identical in this network, the parameter p GJ is defined by (18) and p s / r a N γ / r a + γ s , (32).
In this study, we address this limitation by proposing a hybrid trip generation model framework where demographic groups are treated as latent or unobserved.
