We investigated the underlying mechanisms of these two neural representations using a computational model of a cortical neuron.

Using our computational model , we could make several predictions concerning how stimulus-evoked responses may differ between synchronized, non-synchronized, and mixed-response neurons, which were testable in our dataset of real neurons.

Impact of spontaneous rate on computational model

Our computational model operated with a fixed spontaneous rate (~4 spk/s), close to the median spontaneous rate encountered in our real neuronal population (3.8 spk/s).

According to our computational model (Fig.

In our computational model , the time-varying conductance used to simulate the neuron’s synaptic input was simplified to only approximate the AMPA and GABA-A currents evoked by the acoustic pulse train, with a time-constant of 5 ms [24] (see Methods).

In our computational model , roughly equal proportions of synchronized, non-synchronized, and mixed response neurons were generated (Fig.

In order to directly compare our computational model with real data, we reanalyzed a previously published dataset [15, 18, 26], composed of single-unit responses to acoustic pulse trains from the auditory cortex of four awake marmosets (Callithrix jacchus) (see Methods).

Using the same criteria as in our computational model , 70% of units responding to our acoustic pulse train stimuli (147/210 units) could be classified as having synchronized, non-synchronized or a mixed response (Fig.

Thus, the general features of temporal and rate representations produced by synchronized and non-synchronized neurons, respectively, were preserved in our computational model .

We developed an integrate-and-fire computational model of an auditory cortical neuron [23], based on previously reported data obtained using in-Vivo, Whole-cell recordings from rodent primary auditory corteX [24] (see Methods).

In this study we connect for the first time microscale insulin signaling activity with macroscale glioblastoma growth through the use of computational modeling .

Here, we developed a computational model of insulin signaling in glio-blastoma in order to study this pathway’s role in tumor progression.

Development of a computational model

Conversely, computational models of insulin signaling exist [42, 43], but have only been applied to other applications, including articular cartilage [44], ovarian cancer [45] , and human skeletal muscle [46], and exclude molecules of interest for brain cancer cells [44, 47].

The computational model revealed how inhibition of specific molecular interactions in the insulin signaling pathway could lead to significant reduction of glioblastoma growth.

In conclusion, we have been able to achieve a deeper understanding of the interactions between key factors in the insulin signaling pathway through our computational model .

To that end, we developed a computational model that captures the dynamics of insulin signaling.

With regard to the continuous autocrine signal, our findings are in line with a previous study of our group, where we used a simplified computational model to provide evidence for the self-induced autocrine/paracrine WNT signaling in hNPCs [44].

In addition we provide the first stochastic computational model of WNT/B-catenin signaling that combines membrane-related and intracellular processes, including lipid rafts/receptor dynamics as well as WNT- and ROS-dependent B-catenin activation.

The stochastic multilevel computational model we derive from our experimental measurements adds to the family of existing WNT models, addressing major biochemical and spatial aspects of WNT/beta-catenin signaling that have not been considered in existing models so far.

To summarize, based on our computational model , we demonstrated, that DVL may either act as amplifier or as direct inducer of canonical WNT signaling.

To explore the signaling mechanisms of both, the continuous activation pattern in untreated and in particular the early immediate response in raft-deficient cells, we perform a number of simulation studies based on a validated computational model of WNT signaling we will present in the following.

Computational modeling is increasingly applied to derive or test hypotheses, that in most cases arise from experimental data.

To evaluate, whether an interplay between ROS-induced and lipid raft dependent WNT/fi-catenin signaling can explain our experimental results we apply computational modeling .

Using computational models we show that neither channel noise nor a realistic cell morphology are responsible for the rate dependent shift in the phase response curve.

Furthermore, we address potential explanations for the observed transition using computational modeling .

Active conductances are thought to modulate the shape of the PRC, and therefore computational modeling [7, 47, 48] could be a powerful tool to dissect the ionic bases of the PRC.

PRCs computed for a highly detailed computational model of a Purkinje cell with synaptic activation in the dendritic tree.

To investigate these phenomena at a level currently inaccessible by direct observation, we developed a computational model of a nascent metastatic tumor capturing salient features of known tumor-immune interactions that faithfully recapitulates key features of existing experimental observations.

To address this need we developed a computational model capturing the current understanding of how individual metastatic tumor cells and immune cells sense and contribute to the tumor environment, which in turn enabled us to investigate the complex, collective behavior of these systems.

Given the challenges associated with investigating such systems in vivo, particularly at early stages of tumor development, we developed a computational model serving as a both a conceptual tool and an in silico test bed for building understanding of such phenomena.

Given the aforementioned challenges associated with investigating TME dynamics experimentally, especially at the early stages of cancer initiation and progression, a complementary strategy is the use of computational modeling .

Confirmation of node degree/directionality relationship in a computational model of human brain networks

However, despite these recent empirical and computational model studies, there has been no general explanatory mechanism linking global topology, local node dynamics and directionality between interacting nodes based on mathematical derivation.

Emerging empirical data and computational models suggest that the relative location of neuronal populations in large-scale brain networks might shape the neural dynamics and the directional interactions between nodes, which implies a significant influence of global topology on local dynamics and information flow [16—2 1].

In addition, computational models and simulation studies of global brain networks have revealed that hub nodes (i.e., nodes with extensive connections) have a significant influence on the local node dynamics and the direction of information flow in normal and pathological brains [19—2 1].

The computational model .

(a) Diagram of the computational model colored by section name.

(c) The computational model parameters.

Here we incorporate these assumptions in four computational models and test them in a simulation of a simple perceptual choice task involving action.

In the first study, we simulate the decision trajectories for four computational models with increasingly sophisticated interaction between the decision making and action components.

All computational models that we compare below are built on the drift-diffusion model [1], which is a model of the cognitive processes involved in making simple two-choice decisions.