Computational Neuroscience
Our research in Computational Neuroscience a spans a wide spectrum, from Bayesian methods and theories of sensory-motor learning and control to neural networks, information encoding and decoding and biophysical modeling of cellular electrophysiology. Some of our faculty in this area are also involved in brain-machine interfaces and systems neuroscience research.
Labs in This Research Area
Charles Heckman LabInvestigating the mechanisms of motor output the spinal cord in both normal and disease states
Investigating the mechanisms of motor output the spinal cord in both normal and disease states
Research Description
Neurons in the spinal cord provide the neural interface for sensation and movement. Our lab focuses on the mechanisms of motor output in both normal and disease states (spinal injury, amyotrophic lateral sclerosis). We use a broad range of techniques including intracellular recordings, array recordings of firing patterns, 2-photon imaging, pharmacological manipulations, and behavioral testing. These techniques are applied in in vitro and in vivo animal preparations. In addition we have extensive collaborations with colleagues who study motor output in human subjects.
For lab information and more, see Dr. Heckman's faculty profile.
Publications
See Dr. Heckhman's publications on PubMed.
Contact
Contact Dr. Heckman at 312-503-2164.
Lab Staff
Research Faculty
Matthieu Chardon, Mingchen Jiang, Michael Johnson, Katharina Quinlan, Thomas Sandercock
Postdoctoral Fellows
Obaid Khurram, Amr Mahrous, Jack Miller, Gregory Pearcey
Graduate Students
Seoan Huh, Edward Kim, Christopher Mullens, Emily Reedich, Theeradej Thaweerattanasinp, Jessica Wilson
Technical Staff
Visiting Scholar
Ann Kennedy LabStudying the structure of animal behavior and the neural mechanisms of flexible and adaptive behavior control, using tools from dynamical systems, statistical modeling, and machine learning
Research Description
The three core goals of our research are:
- To develop new theories for the distributed control of behavior by multiple recurrently connected neural populations.
- To understand computation in heterogeneous neural populations with diverse cell types and signaling molecules, by building and training biologically constrained neural population models, and
- To construct richer descriptions of animal behavior and movement by creating novel pose estimation and supervised/unsupervised machine learning techniques.
By collaborating broadly with experimental labs working in diverse model organisms and neural systems, we aim to develop new theories and models to better understand how neural structure governs function and shapes behavior across the animal kingdom.
For lab information and more, see Dr. Kennedy’s faculty profile and lab website.
Publications
See Dr. Kennedy's publications on Google Scholar.
Contact
Contact Dr. Kennedy.
Lab Staff
Postdoctoral Fellow
Lee E. Miller LabUnderstanding the nature of the somatosensory and motor signals within the brain that are used to control arm movements
Understanding the nature of the somatosensory and motor signals within the brain that are used to control arm movements
Research Description
The primary goal of the research in my lab is to understand the nature of the somatosensory and motor signals within the brain that are used to control arm movements. Most of the experiments in my laboratory rely on multi-electrode arrays that are surgically implanted in the brains of monkeys. These “neural interfaces” allow us to record simultaneously from roughly 100 individual neurons in the somatosensory and motor cortices and thereby study the brain’s own control signals as the monkey makes reaching and grasping movements. We can also pass tiny electrical currents through the electrodes to manipulate the natural neural activity and study their effect on neural activity and the monkey’s behavior.
Current projects seek to understand:
- How motor cortical activity leads to the complex patterns of muscle contractions needed to produce movement
- How movement of the limb and forces exerted by the hand are “encoded” in the activity of neurons in the somatosensory cortex
We also study how these relations are affected by behavioral context: the magnitude and dynamics of exerted forces, the varied requirements for sensory discrimination, and the quality of the visual feedback that is provided to the monkey to guide its movements.
Along with this basic research, we can use these neural interfaces to bypass the peripheral nervous system, in order to connect the monkey’s brain directly to the outside world. We are developing neural interfaces that ultimately will use signals recorded from the brain to allow patients who have lost a limb to operate a prosthetic limb. The interface may also be used to bypass a patient’s injured spinal cord in order to restore voluntary control of their paralyzed muscles. Conversely, electrical stimulation of the brain will restore the sense of touch and limb movement to patients with limb amputation or spinal cord injury. This highly interdisciplinary work is enabled by numerous collaborations at Northwestern University and other institutions.
For lab information and more, see Dr. Miller's faculty profile and lab website.
Publications
See. Dr. Miller's publications on PubMed.
Contact
Contact Dr. Miller at 312-503-8677.
Lab Staff
Postdoctoral Fellows
Kyle Blum, Ali Farshchian, Xuan Ma, Fabio Rizzoglio
Graduate Students
Ege Altan, Min Park, Joseph Sombeck, Chris VerSteeg
Technical Staff
Kevin Bodkin, Eric Gasper, Juliet Heye, Ben Semel, Nikolay Stoykov, Josie Wallner
Undergraduate Student
Temporary Staff
Ferdinando A. Mussa-Ivaldi LabInvestigating the sensory-motor system through a close interaction with artificial systems
Investigating the sensory-motor system through a close interaction with artificial systems
Research Description
Our laboratory (the Robotics Lab at RIC) investigates the sensory-motor system through a close interaction with artificial systems. Specifically, we are interested in determining how the brain acquires, organizes and executes motor behaviors. We use robotic and interface technologies to investigate how humans adapt to radical changes in the environment and in body mechanics.
Consistent evidence indicates that the nervous system is capable of coping with changes in the body and in the environment by developing internal representations of the relationship between movement commands and their sensory consequences. In this sense, motor learning is not only about improving performance. Motor learning is a means by which our brain develops an understanding of the physical and statistical properties of the world. We are studying the basic properties of this learning process and how it may be exploited to facilitate rehabilitation. Other studies within our group are directed at facilitating bidirectional communications between the human body and artificial instruments, such as wheelchairs and computers. We wish to combine the biological mechanisms of learning with machine learning algorithms for reducing the burden that disabled people must currently endure for the efficient operation of systems such as powered wheelchairs and other assistive devices. In a nutshell: we want to create systems that learn and adapt to their users.
Understanding how the brain controls motor behavior is of clinical interest since alterations in neuromotor control due to stroke and other neurological impairments can severely limit motor function. Through our research we wish to create knowledge that can help restore motor functions in individuals with neurological disorders.
For lab information and more, see Dr. Mussa-Ivaldi's faculty profile.
Publications
See Dr. Mussa-Ivaldi's publications on PubMed.
Contact Us
Contact Dr. Mussa-Ivaldi at 312-238-1230 or the Robotics Lab at 312-238-1232.
Postdoctoral Fellows
Dalia De Santis, Ali Farshchiansadegh, Fabio Rizzoglio
Graduate Students
Lucas Pinto LabLarge-scale networks underlying decision making
Large-scale networks underlying decision making
Research Description
We want to understand how neural circuits across many brain areas interact to support decision making. In particular, how are these interactions flexibly reconfigured when animals make decisions that use different underlying computations? To do this we combine high-throughput mouse behavior in virtual reality, optical and genetic tools to measure and manipulate the dynamics of single neurons and neuronal populations, and computational approaches to understand both the behavior and its relationship to neural activity.
Current Projects
Decision making and its different underlying computations
There is much evidence to suggest that decision-making computations happen across widespread brain areas, including many in the cerebral cortex. But how do these areas interact to make a single decision? And how can the brain perform different computations using the same pool of neural circuits? Decisions that require different combinations of underlying computations appear to be associated with distinct patterns of large-scale activity across the cerebral cortex. We want to understand how neuromodulatory mechanisms potentially control these different dynamic configurations of neural activity, and how they map onto different cognitive operations.
Neuromodulatory mechanisms of the reorganization of large-scale cortical dynamics
We study the brain circuits that switch between, and maintain, the different dynamic configurations of large-scale cortical activity that support different types of decisions. A particular focus is on the role of neuromodulators such as acetylcholine. This line of inquiry is also of potential clinical interest, as it may help us understand how neurodegenerative diseases such as Alzheimer’s lead to decision-making deficits.
Functional organization of large-scale cortical dynamics
Another crucial question is whether there is actually a logic to the way large-scale cortical dynamics change according to the behaviors they support. To put it another way, are there core computations performed by each cortical area that explain why activity across the cortex looks the way it does during different tasks? We believe answering this will help us provide parsimonious explanations of cortical function using general computational principles.
For lab information and more, see Dr. Pinto's faculty profile and laboratory website.
Publications
See Dr. Pinto's publications on PubMed.
Contact
Contact Dr. Pinto at 312-503-7928.
Lab Staff
Research Faculty
Graduate Student
Technical Staff
Virginia Rodriguez Sara A. Solla LabUnderstanding the computational implications of neural dynamics
Understanding the computational implications of neural dynamics
Theoretical Neuroscience
The goal of our research is to understand information processing in the brain. We use mathematical models based on specific hypothesis about encoding and decoding aspects of neural activity, and use analytical and numerical techniques to investigate the implications of these hypothesis so that they can be validated, modified, or discarded as dictated by experimental data.
Research Description
Our purpose is to understand the computational implications of neural dynamics. Our work relies on conceptual frameworks and mathematical tools from statistical physics, information theory, nonlinear dynamics, probability theory, and machine learning, and aims at formulating data driven models that illuminate specific aspects of information processing by networks of neurons.
Specific topics of interest include input-output characteristics of single cell and network models, encoding and decoding of information through neural activity, early stages of sensory processing, and the neural control of movement. We work in close collaboration with experimental groups, both at Northwestern University and at other institutions. Recently, we have focused on the interplay between neural connectivity, network dynamics, and computation, and on brain-machine interfaces for the decoding of neural activity in motor cortex and the encoding of sensory information via stimulation of somatosensory cortex. Our work on brain-machine interfaces is funded by NINDS, the National Institute of Neurological Disorders and Strokes within the NIH.
For lab information and more, see Dr. Solla’s faculty profile.
Publications
See Dr. Solla's publications on PubMed.
Contact
Contact Dr. Solla at 312-503-1408 or the lab at 312-503-1408.
D. James Surmeier LabUnderstanding the principles of neuronal dysfunction in disease states
Understanding the principles of neuronal dysfunction in disease states
Research Description
Our group has five research topics. The first topic area is what drives Parkinson’s disease (PD). Using a combination of optical, electrophysiological and molecular approaches, we are examining the factors governing neurodegeneration in PD and its network consequences, primarily in the striatum. This work has led to a Phase III neuroprotection clinical trial for early stage PD and a drug development program targeting a sub-class of calcium channels. The second topic area is network dysfunction in Huntington’s disease (HD). Using the same set of approaches, we are exploring striatal and pallidal dysfunction in genetic models of HD, again with the aim of identifying novel drug targets. The third topic area is striatal dysfunction in schizophrenia, with a particular interest in striatal adaptations to neuroleptic treatment. The fourth topic area is post-traumatic stress disorder and the role played by neurons in the locus ceruleus in its manifestations. The last topic area is chronic pain states and the impact these have on the circuitry of the ventral striatum.
For lab information and more, see Dr. Surmeier's faculty profile.
Publications
See Dr. Surmeier's publications on PubMed.
Contact
Contact Dr. Surmeier at 312-503-4904.
Lab Staff
Research Faculty
Michelle Day, Jaime Guzman-Lucero, Ema Ilijic, Tristano Pancani, David Wokosin, Weixing Shen, Tatiana Tkatch, Zhong Xie
Postdoctoral Fellows
Marziyeh Belal, Yijuan Du, Patricia Gonzalez Rodriguez, Steven Graves, Martin Henrich, Harini Lakshminarasimhan, Austin Lim, Stephen Logan, Curtis Neveu, Tamara Perez-Rosello, DeNard Simmons, Asami Tanimura, Cecilia Tubert, Sasha Ulrich, Yichen Wu, Enrico Zampese, Shenyu Zhai
Graduate Students
Technical Staff
Marisha Alicea, Kang Chen, Yu Chen, Kyle Dombeck, Bonnie Erjavec, Daniel Galtieri, Christine Kamide, Christina Moynihan, Danielle Schowalter