The CCN Group combines biophysical modeling, goal-driven deep learning, and data-driven science, to study how the brain acquires and performs perceptual, cognitive and motor skills. Our research focuses on visual perception and imagery, the coordination of complex hand movements as well as visuomotor integration subserved by the frontoparietal network.
Biophysical modeling is a computational approach used to simulate the behavior of biological systems, including neural systems. In neuroscience, biophysical modeling is used to understand the underlying mechanisms of brain function and behavior by incorporating detailed descriptions of the biophysical properties of neurons and how they interact with each other. The models can range from simulating the behavior of individual neurons to larger-scale simulations of populations of neurons and their interactions.
By using biophysical models, we aim to gain insights into the complex interactions between different neural components and make predictions about how these interactions change in response to different stimuli and task demands. Biophysical modeling can also be combined with other techniques, such as electrophysiology and imaging, to validate the predictions made by the models and gain a more comprehensive understanding of brain function.
The CCN group utilizes biophysical modeling primarily to study whole-brain dynamics during resting and task performance conditions, decision making in visual and visuomotor contexts and the functional role of cortical oscillations.
Goal-driven deep learning
Goal-driven deep learning is a type of machine learning that involves training artificial neural networks to perform tasks in a goal-directed manner, similar to how humans and animals process information. In general, goal-driven deep learning is used in neuroscience to develop computational models that can perform tasks that are representative of human and animal behavior, such as perception, decision-making, and motor control.
In the CCN group, goal-driven deep learning is used in conjunction with biophysical modeling and data-driven model discovery to study the cortical perception-action loop from an interdisciplinary perspective. The goal-driven deep learning models are trained to perform specific tasks based on sensory input and generate predictions about how the brain would perform these tasks. By integrating goal-driven deep learning with biophysical modeling, the CCN group can gain new insights into the neurocomputational mechanisms of the perception-action loop and develop more accurate models of brain function.
The CCN group utilizes goal-driven deep to study human dexterity with a focus on visually-guided in-hand object manipulation.
Data-driven science, also referred to as data-driven model discovery, is a growing field that harnesses vast amounts of data and modern computational techniques to extract meaningful patterns in complex systems, including the brain. It is considered the fourth paradigm of scientific discovery and is transforming the way we model, predict, and control complex systems.
With advancements in low-cost sensors and computational power, as well as virtually unlimited data storage and transfer capabilities, data-driven science is increasingly becoming the go-to approach for addressing complex scientific and engineering problems. By coupling data with machine learning algorithms, researchers are able to uncover underlying principles and enhance the ability to predict, estimate, and control complex systems.
In the field of neuroscience, data-driven science has tremendous potential for gaining insight into the brain's functioning. By analyzing high-fidelity measurements from time-series recordings and experiments, researchers can extract spatiotemporal patterns that dominate neural activity. This leads to a deeper understanding of brain processes and the development of more effective methods for predicting, estimating, and controlling brain activity.
The CCN Group utilizes data-driven science in the study of the cortical perception-action loop with a focus on identifying governing equations that capture dynamics in visual and motor cortices.