Author Summary | We investigated the underlying mechanisms of these two neural representations using a computational model of a cortical neuron. |
Comparison of model-based predictions and real neuronal responses | 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 | Impact of spontaneous rate on computational model |
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). |
Methods). | According to our computational model (Fig. |
Methods). | 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). |
Model parameters underlying rate and temporal representations | In our computational model , roughly equal proportions of synchronized, non-synchronized, and mixed response neurons were generated (Fig. |
Responses to pulse trains in real and simulated cortical neurons | 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). |
Responses to pulse trains in real and simulated cortical neurons | 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. |
Responses to pulse trains in real and simulated cortical neurons | Thus, the general features of temporal and rate representations produced by synchronized and non-synchronized neurons, respectively, were preserved in our computational model . |
Results | 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). |
Abstract | In this study we connect for the first time microscale insulin signaling activity with macroscale glioblastoma growth through the use of computational modeling . |
Author Summary | 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 | Development of a computational model |
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]. |
Development of a computational model | The computational model revealed how inhibition of specific molecular interactions in the insulin signaling pathway could lead to significant reduction of glioblastoma growth. |
Discussion | 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 . |
Introduction | To that end, we developed a computational model that captures the dynamics of insulin signaling. |
Abstract | 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. |
Author Summary | 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. |
Discussion | 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. |
Introduction | 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 | Confirmation of node degree/directionality relationship in a computational model of human brain networks |
Discussion | 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. |
Introduction | 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]. |
Introduction | 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]. |
Results | Here we incorporate these assumptions in four computational models and test them in a simulation of a simple perceptual choice task involving action. |
Results | In the first study, we simulate the decision trajectories for four computational models with increasingly sophisticated interaction between the decision making and action components. |
Study 1: Decision trajectories during embodied choice | 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. |