Computational neuroscience

Animacy is a robust organizing principle among object category representations in the human brain. Using multivariate pattern analysis methods, it has been shown that distance to the decision boundary of a classifier trained to discriminate neural activation patterns for animate and inanimate objects correlates with observer RTs for the same animacy categorization task [Ritchie, J. B., Tovar, D. A., & Carlson, T. A. Emerging object representations in the visual system predict reaction times for categorization...

Real-time bioimaging of infectious disease processes may aid countermeasure development and lead to an improved understanding of pathogenesis. However, few studies have identified biomarkers for monitoring infections using in vivo imaging. Previously, we demonstrated that positron emission tomography/computed tomography (PET/CT) imaging with [(18)F]-fluorodeoxyglucose (FDG) can monitor monkeypox disease progression in vivo in nonhuman primates (NHPs). In this study, we investigated [(18)F]-FDG-PET/CT imaging of immune processes in lymphoid tissues to identify patterns of inflammation in the monkepox NHP model and to determine the value of [(18)F]-FDG-PET/CT as a biomarker for disease and treatment outcomes...

Modeling and implementation of the nonlinear neural system with physiologically plausible dynamic behaviors are considerably meaningful in the field of computational neuroscience. This study introduces a novel hardware platform to investigate the dynamical behaviors within the nonlinear subthalamic nucleus-external globus pallidus system. In order to reduce the implementation complexities, a hardware-oriented conductance-based subthalamic nucleus (STN) model is presented, which can reproduce accurately the dynamical characteristics of biological conductance-based STN cells...

In recent years, the source localization technique of magnetoencephalography (MEG) has played a prominent role in cognitive neuroscience and in the diagnosis and treatment of neurological and psychological disorders. However, locating deep brain activities such as in the mesial temporal structures, especially in preoperative evaluation of epilepsy patients, may be more challenging. In this work we have proposed a modified beamforming approach for finding deep sources. First, an iterative spatiotemporal signal decomposition was employed for reconstructing the sensor arrays, which could characterize the intrinsic discriminant features for interpreting sensor signals...

The application of machine learning methods to neuroimaging data has fundamentally altered the field of cognitive neuroscience. Future progress in understanding brain function using these methods will require addressing a number of key methodological and interpretive challenges. Because these challenges often remain unseen and metaphorically "haunt" our efforts to use these methods to understand the brain, we refer to them as "ghosts". In this paper, we describe three such ghosts, situate them within a more general framework from philosophy of science, and then describe steps to address them...

How are neural circuits organized and tuned to achieve stable function and produce robust behavior? The organization process begins early in development and involves a diversity of mechanisms unique to this period. We summarize recent progress in theoretical neuroscience that has substantially contributed to our understanding of development at the single neuron, synaptic and network level. We go beyond classical models of topographic map formation, and focus on the generation of complex spatiotemporal activity patterns, their role in refinements of particular circuit features, and the emergence of functional computations...

We developed software tools to download, extract features, and organize the Cell Types Database from the Allen Brain Institute (ABI) in order to integrate its whole cell patch clamp characterization data into the automated modeling/data analysis cycle. To expand the potential user base we employed both Python and MATLAB. The basic set of tools downloads selected raw data and extracts cell, sweep, and spike features, using ABI's feature extraction code. To facilitate data manipulation we added a tool to build a local specialized database of raw data plus extracted features...

Importance: Although borderline personality disorder (BPD)-one of the most common, burdensome, and costly psychiatric conditions-is characterized by repeated interpersonal conflict and instable relationships, the neurobiological mechanism of social interactive deficits remains poorly understood. Objective: To apply recent advancements in the investigation of 2-person human social interaction to investigate interaction difficulties among people with BPD. Design, Setting, and Participants: Cross-brain information flow in BPD was examined from May 25, 2012, to December 4, 2015, in pairs of participants studied in 2 linked functional magnetic resonance imaging scanners in a university setting...

Hearing relies on a series of coupled electrical, acoustical, and mechanical interactions inside the cochlea that enable sound processing. The local structural and electrical properties of the organ of Corti (OoC) and basilar membrane give rise to the global, coupled behavior of the cochlea. However, it is difficult to determine the root causes of important behavior, such as the mediator of active processes, in the fully coupled in vivo setting. An alternative experimental approach is to use an excised segment of the cochlea under controlled electrical and mechanical conditions...

A long-standing goal of neuroscience has been to understand how computations are implemented across large-scale brain networks. By correlating spontaneous activity during "resting states" [1], studies of intrinsic brain networks in humans have demonstrated a correspondence with task-related activation patterns [2], relationships to behavior [3], and alterations in processes such as aging [4] and brain disorders [5], highlighting the importance of resting-state measurements for understanding brain function...

Neuroscience research has become increasingly reliant upon quantitative and computational data analysis and modeling techniques. However, the vast majority of neuroscientists are still trained within the traditional biology curriculum, in which computational and quantitative approaches beyond elementary statistics may be given little emphasis. Here we provide the results of an informal poll of computational and other neuroscientists that sought to identify critical needs, areas for improvement, and educational resources for computational neuroscience training...

Computational neuroscience is, to first order, dominated by two approaches: the 'bottom-up' approach, which searches for statistical patterns in large-scale neural recordings, and the 'top-down' approach, which begins with a theory of computation and considers plausible neural implementations. While this division is not clear-cut, we argue that these approaches should be much more intimately linked. From a Bayesian perspective, computational theories provide constrained prior distributions on neural data-albeit highly sophisticated ones...

Functional magnetic resonance imaging (fMRI) measures changes in blood-oxygenation-level-dependent (BOLD) signals to detect brain activities. It has been recently reported that the spatial correlation patterns of resting-state BOLD signals in the white matter (WM) also give WM information often measured by diffusion tensor imaging (DTI). These correlation patterns can be captured using functional correlation tensor (FCT), which is analogous to the diffusion tensor (DT) obtained from DTI. In this paper, we propose a noise-robust FCT method aiming at further improving its quality, and making it eligible for further neuroscience study...

The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent times, however, communication and collaboration between the two fields has become less commonplace. In this article, we argue that better understanding biological brains could play a vital role in building intelligent machines. We survey historical interactions between the AI and neuroscience fields and emphasize current advances in AI that have been inspired by the study of neural computation in humans and other animals...

In this piece, I first celebrate the growing contribution of psychology to the understanding and solution of pressing social issues. Then, despite these exciting developments, I worry about whether we have created a field that our students want to spend their lives in, and I suggest concerns that might fruitfully be addressed. Finally, I worry about the potential fragmentation of psychology and applaud programs of research that have shown the unique and important contributions to be made when the methods and perspectives of neuroscience, cognitive science, and computational modeling are integrated with those of social, personality, and developmental psychology...

The potential impact of brain training methods for enhancing human cognition in healthy and clinical populations has motivated increasing public interest and scientific scrutiny. At issue is the merits of intervention modalities, such as computer-based cognitive training, physical exercise training, and non-invasive brain stimulation, and whether such interventions synergistically enhance cognition. To investigate this issue, we conducted a comprehensive 4-month randomized controlled trial in which 318 healthy, young adults were enrolled in one of five interventions: (1) Computer-based cognitive training on six adaptive tests of executive function; (2) Cognitive and physical exercise training; (3) Cognitive training combined with non-invasive brain stimulation and physical exercise training; (4) Active control training in adaptive visual search and change detection tasks; and (5) Passive control...

Small, genetically tractable species such as larval zebrafish, Drosophila, or Caenorhabditis elegans have become key model organisms in modern neuroscience. In addition to their low maintenance costs and easy sharing of strains across labs, one key appeal is the possibility to monitor single or groups of animals in a behavioural arena while controlling the activity of select neurons using optogenetic or thermogenetic tools. However, the purchase of a commercial solution for these types of experiments, including an appropriate camera system as well as a controlled behavioural arena, can be costly...

We present a method for solving population density equations (PDEs)--a mean-field technique describing homogeneous populations of uncoupled neurons-where the populations can be subject to non-Markov noise for arbitrary distributions of jump sizes. The method combines recent developments in two different disciplines that traditionally have had limited interaction: computational neuroscience and the theory of random networks. The method uses a geometric binning scheme, based on the method of characteristics, to capture the deterministic neurodynamics of the population, separating the deterministic and stochastic process cleanly...

OBJECTIVE: The advent of High-Performance Computing (HPC) in recent years has led to its increasing use in brain study through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a homogeneous acceleration platform to effectively address the complete array of modeling requirements...

As the complex structure of nervous tissue cannot be mimicked in two-dimensional (2D) cultures, the development of three-dimensional (3D) neuronal cell culture platforms is a topical issue in the field of neuroscience and neural tissue engineering. Computer-assisted laser-based fabrication techniques such as direct laser writing by two-photon polymerization (2PP-DLW) offer a versatile tool to fabricate 3D cell culture platforms with highly ordered geometries in the size scale of natural 3D cell environments...