Theoretical and computational methods are used to define neuronal mechanisms underlying cognitive tasks in humans and animals, decipher the neuronal code, and relate natural systems with artificial neural networks. Specifically, Researchers use theoretical techniques to investigate sensory and motor systems, learning and memory, and abnormal brain activities.
The following labs use different Mathematical theory and computational approaches to model behavior and the function of the nervous system:
Theoretical and Computational Neuroscience Researchers
The Motor Control and Rehabilitation of Walking Lab (MCRW) is a basic and clinical neuroscience lab dedicated to research and development of novel treatments and technologies in the field of walking rehabilitation.
We investigate mechanisms that control the function of walking in persons with typical walking patterns and those with disabilities due to brain damage.
Currently, the MCRW has several active studies examining topics such as integrative strategies to increase the role of somatosensation in the control of walking, optimal incorporation of the visual system to improve walking stability and the capacity to cope with perturbation while walking.
In Cerebral Palsy we investigate muscle oxygenation and hemodynamics as well as low-grade inflammation and additional health biomarkers following endurance versus strength training.
The interdisciplinary Computational Vision Lab
(iCVL) studies biological (and in particular, human) vision and machine vision
from both theoretical and applied perspectives. We bring together these fields
to (1) develop algorithmic solutions to challenges in computer vision and image
understanding, (2) devise computational explanations of biological visual
function, and (2) employ insights from studying vision for exploration of other
types of information processing, both sensory and cognitive. To meet these goals,
research is highly interdisciplinary, involving various combinations of
computational and mathematical work, machine learning techniques, behavioral
exploration and visual psychophysics (with both humans and animals), and
inquiry into visual neuroscience
My recent research scope in brain science includes holographic memory coding in the brain. Quantum effects in the brain and machine learning based avatar person representation and the interaction among such avatars.
Studies motor control with behavioral, electrophysiology and neuroimaging techniques with a particular interest in understanding the function of the cerebellum.
I work in the fields of theoretical and computational neuroscience and neurophysics, and study the dynamics of large neuronal networks. My research has focused on information processing in the whisker somatosensory-motor system, where active sensing is crucial for perception. Using theoretical and computational methods, I study how cortical circuits with several types of interneurons process thalamic input. In another project, I investigate the generation and synchronization of whisking and sniffing rhythms in the brainstem, the generation of neuronal signals in response to whisker contact, and the computational role of somatosensory-motor loops in the brainstem.
The maidenbaum lab studies the interaction between humans and their surrounding environment - how do we represent our spatial surroundings in our brain? How are these representations modulated by different sensory input channels and by memory? How are real, virtual and augmented environments coded? And how can we use insights from this basic science in order to rehabilitate, assist and augment human spatial skills?
We use naturalistic gamified paradigms in order to test human spatial memory and navigation in healthy participants and in patients, and computational models in order to decode environmental features such as directions, locations, and targets.
The lab is also interested in non-physical spaces, aiming to extend findings from spatial cognition to other dimensions such as time, social and abstract concept spaces.
Develops novel methods for multivariate temporal data analytics, including frequent temporal patterns discovery and their use for temporal knowledge discovery, classification and prediction. Specifically in neuroscience his lab works on classifying epileptic patients, based on their history data, and on classification of mice brain cells based on their electrical activities.
My students and I apply neuroscience theories about the human sensorimotor control, perception, adaptation, learning, and skill acquisition in the development of human-operated medical and surgical robotic systems. We also use robots, haptic devices, and other mechatronic devices as a platform to understand the human sensorimotor system in real-life tasks like surgery, and in virtual tasks like virtual reality games or surgical simulation. We hope that this research will improve the quality of treatment for patients, will facilitate better training of surgeons, advance the technology of teleoperation and haptics, and advance our understanding of the brain.
My research deals with the design of statistical tools that are appropriate for the complicated fMRI data structure, and researchers' hypotheses. Some of my contributions are simple adaptations of existing tools from fields such as multivariate statistics literature, supervised machine learning, multiple testing, etc. The other part of my contributions go back to the philosophy of science and rethink the notion of "brain activation" and "signal" in the context of groups of human brains. My philosophical musings lead to new statistical methodology.
With the vast advancement of empirical techniques for recording, imaging and manipulating neural responses, the quantitative aspect of brain research is becoming increasingly more important. Theoretical physics offers a wide range of theoretical tools and concepts that were successfully applied in other fields of natural sciences.
In my lab we apply tools and concepts from: Statistical Mechanics, Nonlinear Dynamics, Theory of Disordered Systems and Information Theory to the investigation of the central nervous system. Specifically my lab focuses on two central challenges: the neural code and neural learning theory.
Research in the lab of Dr. Oren Shriki uses mathematical analyses of brain activity and machine learning techniques to develop novel diagnostic tools for neurological and psychiatric disorders. The lab also develops computational models of neuronal networks to gain insights into how changes in neural dynamics lead to brain disorders and how neural plasticity may assist in restoring healthy neural dynamics. A major focus of the lab is on translational neuroscience and neurotechnologies, such as brain-computer interfaces, a system for real-time epileptic seizure prediction and a novel pilot helmet which monitor's the pilot's brain.