Computational Neuroscience

Computational neuroscience employs mathematical models, theoretical analysis, and abstractions of the brain to understand the principles that govern its development, physiology, cognitive abilities, and contributions to behavior. Theoretical models aim to capture the essential features of the nervous system at multiple spatial and temporal scales to enable the development of hypotheses that can be directly tested by biological, clinical, or psychological experiments.

Computational researchers at The Friedman Brain Institute, organized under the aegis of the Center for Computational Neuroscience with generous philanthropic support from Adam and Jana Shapiro, work closely with experimental data on various scales to uncover novel insights and generate new experimental predictions. Our computational team leverages several different approaches to understand the brain, and includes the Nash Family Center for Advanced Circuit Therapeutics and the Center for Computational Psychiatry.

Areas of Research and Clinical Focus

Our researchers are bringing together the fields of brain research and artificial intelligence/machine learning to figure out how the brain works using mathematical and computational models. When models are combined with data collected from neuroscience experiments, artificial systems can be designed that are capable of performing realistic behaviors using only the machinery the biological nervous system has access to (i.e., neurons and synapses operating at a fast timescale). Building such systems enables researchers to “reverse engineer” them to reveal the operating principles of the real brain. The resulting integrative theories and models have the potential to transform the study of the brain, by making specific, quantifiable predictions that lead to new experiments and drive new hypotheses about how the brain works—in health and disease.

Scientists in this research area include:

Computational psychiatry is a nascent research area seeking to characterize mental disorders in terms of aberrant computations at multiple scales. Recent progress in human neuroscience has also highlighted the need for computational models that can bridge the explanatory gap between pathophysiology and psychopathology. The computational expertise and tools required to address this gap exist only across disciplines, combining skills and knowledge from investigators and clinicians that are jointly interested in solving problems of mental health.

Leveraging on the rich clinical resources and computational expertise across departments, the Icahn School of Medicine at Mount Sinai’s Center for Computational Psychiatry is dedicated to deepening our understanding of how both algorithms and biology of the brain contribute to dysfunction like addiction, eating disorder, autism, and personality disorders. The Center is especially interested in a transdiagnostic approach towards mental dysfunction; for example, by examining how aberrant social cognition might manifest itself similarly or differently across a range of distinct diagnostic labels. 

Scientists in this research area include:

The Nash Family Center for Advanced Circuit Therapeutics is an interdisciplinary Center that develops and tests new brain-tuning strategies to accelerate the delivery of state-of-the art individualized surgical treatments for patients with advanced neuropsychiatric disorders. Whether it is those where brain stimulation therapies are already clinically available (e.g., deep brain stimulation (DBS) for Parkinson’s disease, epilepsy, obsessive compulsive disorder, etc.) or more experimental DBS applications, including depression, dementia, eating disorders, and addiction.

The Center further catalyzes integrative research activities involving surgical patients using multimodal imaging, invasive and noninvasive electrophysiology, quantitative performance and behavioral metrics (wearable sensors, motion capture, face/speech analyses), and computational models of behavior and disease mechanisms, all supported by a centralized clinical and research bioinformatics infrastructure. The genesis and evaluation of next-generation implantable devices, computation-based algorithms for treatment delivery, and clinical piloting of novel applications complement ongoing personalized, evidence-based, multidisciplinary care.

Scientists in this research area include: