A number of recent efforts have used large-scale, biologically realistic, neural models to help understand the neural basis for the patterns of activity observed in both resting state and task-related functional neural imaging data. An example of the former is The Virtual Brain (TVB) software platform, which allows one to apply large-scale neural modeling in a whole brain framework. TVB provides a set of structural connectomes of the human cerebral cortex, a collection of neural processing units for each connectome node, and various forward models that can convert simulated neural activity into a variety of functional brain imaging signals. In this paper, we demonstrate how to embed a previously or newly constructed task-based large-scale neural model into the TVB platform. We tested our method on a previously constructed large-scale neural model (LSNM) of visual object processing that consisted of interconnected neural populations that represent, primary and secondary visual, inferotemporal, and prefrontal cortex. Some neural elements in the original model were “non-task-specific” (NS) neurons that served as noise generators to “task-specific” neurons that processed shapes during a delayed match-to-sample (DMS) task. We replaced the NS neurons with an anatomical TVB connectome model of the cerebral cortex comprising 998 regions of interest interconnected by white matter fiber tract weights. We embedded our LSNM of visual object processing into corresponding nodes within the TVB connectome. Reciprocal connections between TVB nodes and our task-based modules were included in this framework. We ran visual object processing simulations and showed that the TVB simulator successfully replaced the noise generation originally provided by NS neurons; i.e., the DMS tasks performed with the hybrid LSNM/TVB simulator generated equivalent neural and fMRI activity to that of the original task-based models. Additionally, we found partial agreement between the functional connectivities using the hybrid LSNM/TVB model and the original LSNM. Our framework thus presents a way to embed task-based neural models into the TVB platform, enabling a better comparison between empirical and computational data, which in turn can lead to a better understanding of how interacting neural populations give rise to human cognitive behaviors.

In this study, we investigated one type of auditory perceptual grouping phenomena--the auditory continuity illusion (also called temporal induction). We employed a previously developed, neurobiologically realistic, large-scale neural network model of the auditory processing pathway in the cortex, ranging from the primary auditory cortex to the prefrontal cortex, and simulated temporal induction without changing any model parameters. The model processes tonal contour stimuli, composed of combinations of upward and downward FM sweeps and tones, in a delayed match-to-sample task. The local electrical activities of the neuronal units of the model simulated accurately the experimentally observed electrophysiological data, where available, and the model's simulated BOLD-fMRI data were quantitatively matched with experimental fMRI data. In the present simulations, intact stimuli were matched with fragmented versions (i.e., with inserted silent gaps). The ability of the model to match fragmented stimuli declined as the duration of the gaps increased. However, when simulated broadband noise was inserted into these gaps, the matching response was restored, indicating that a continuous stimulus was perceived. The electrical activities of the neuronal units of the model agreed with electrophysiological data, and the behavioral activity of the model matched human behavioral data. In the model, the predominant mechanism implementing temporal induction is the divergence of the feedforward connections along the auditory processing pathway in the temporal cortex. These simulation results not only attest to the robustness of the model, but further predict the primary role of the anatomical connectivity of the auditory processing areas in mediating the continuity illusion.

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Many cognitive and computational models have been proposed to help understand working memory. In this article, we present a simulation study of cortical processing of visual objects during several working memory tasks using an extended version of a previously constructed large-scale neural model [Tagamets, M. A., & Horwitz, B. Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study. Cerebral Cortex, 8, 310-320, 1998]. The original model consisted of arrays of Wilson-Cowan type of neuronal populations representing primary and secondary visual cortices, inferotemporal (IT) cortex, and pFC. We added a module representing entorhinal cortex, which functions as a gating module. We successfully implemented multiple working memory tasks using the same model and produced neuronal patterns in visual cortex, IT cortex, and pFC that match experimental findings. These working memory tasks can include distractor stimuli or can require that multiple items be retained in mind during a delay period (Sternberg's task). Besides electrophysiology data and behavioral data, we also generated fMRI BOLD time series from our simulation. Our results support the involvement of IT cortex in working memory maintenance and suggest the cortical architecture underlying the neural mechanisms mediating particular working memory tasks. Furthermore, we noticed that, during simulations of memorizing a list of objects, the first and last items in the sequence were recalled best, which may implicate the neural mechanism behind this important psychological effect (i.e., the primacy and recency effect).