The integration of behavioral phenomena into computational models of brain function is a fundamental staple of empirical research in neuroscience and psychology. Yet, researchers are beginning to accumulate increasing amounts of data without having the temporal, computational or monetary resources to integrate these data into scientific theories, and/or to test resulting theories in follow-up experiments.

Major Depressive Disorder (MDD) is a common and debilitating illness. One novel intervention is transcranial magnetic stimulation (TMS), which has been cleared by the Food and Drug Administration for pharmacoresistant MDD. TMS uses rapidly fluctuating magnetic fields to induce electrical activity and modify pathological neural networks in MDD. Despite its promise, TMS is effective in approximately 60% of patients, and requires daily treatments for up to six weeks.

Advances in neurotechnology have enabled concurrent electrical neuromodulation and recording of neural signals at adjacent locations in the central nervous system. However, stimulation artifacts obscure the sensed underlying neural activity. Researchers have developed a novel method, termed Period-based Artifact Reconstruction and Removal Method (PARRM), to remove stimulation artifacts from neural recordings by leveraging the exact period of stimulation to construct and subtract a high-fidelity template of the artifact.

Researchers are developing biomedical devices with embedded closed-loop algorithms for providing advanced adaptive therapies. As these devices become more capable and algorithms become more complex, tasked with integrating and interpreting multi-channel, multi-modal electrophysiological signals, there is a need for flexible bench-top testing and prototyping. NeuroDAC uses off-the-shelf audio equipment to construct a biosignal waveform generator capable of streaming pre-recorded biosignals from a host computer.

In cognitive neuroscience, computational modeling can formally adjudicate between theories and affords quantitative fits to behavioral/brain data. Candidate models are often defined as generative: models from which we can simulate data. However, typically cognitive neuroscience studies require going the other way around, from data to inferring models and their parameters. Common software tools for doing this inference typically depend on having access to a likelihood function which returns the likelihood of data observation for a model parameterization.

Psychophysiological tasks are a useful tool for studying human behavior driven by mental processes such as cognitive control, reward evaluation and learning. Neural mechanisms during behavioral tasks are often studied via simultaneous electrophysiological recordings. Popular online platforms such as Amazon Mechanical Turk (MTurk) and Prolific enable deployment of tasks to numerous participants simultaneously. However, there is currently no task-creation framework available for flexibly deploying tasks online and during simultaneous electrophysiology.

Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution, providing reliable markers of healthy and disease states. A downside of these rapidly advancing technologies is a lack of understanding the underlying neural generators, which limits their use in developing new theories of information processing or new methods for treating neuropathology. Human Neocortical Neurosolver (HNN) is a user-friendly software tool designed to bridge these macro-scale signals to the underlying cellular and circuit level activity.