cached image

Sara A. Solla - US grants

Affiliations:

Northwestern University, Evanston, IL

Area:

neural networks

Website:

http://www.physics.northwestern.edu/people/personalpages/ssolla.html

Tree Info Publications Similar researchers PubMed Report error

We are testing a new system for linking grants to scientists.

The funding information displayed below comes from the NIH Research Portfolio Online Reporting Tools and the NSF Award Database.
The grant data on this page is limited to grants awarded in the United States and is thus partial. It can nonetheless be used to understand how funding patterns influence mentorship networks and vice-versa, which has deep implications on how research is done.
You can help! If you notice any innacuracies, please sign in and mark grants as correct or incorrect matches.

Sign in to see low-probability grants and correct any errors in linkage between grants and researchers.

High-probability grants

According to our matching algorithm, Sara A. Solla is the likely recipient of the following grants.

Filter high-probability grants:

Years	Recipients	Code	Title / Keywords	Matching score
2000 — 2006	Silber, Mary (co-PI) [⬀] Solla, Sara Umbanhowar, Paul (co-PI) [⬀] Davis, Stephen [⬀] Riecke, Hermann (co-PI) [⬀]	N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information	Igert: Dynamics of Complex Systems in Science and Engineering @ Northwestern University 9987577 Stephen Davis - Northwestern University IGERT: Dynamics of Complex Systems in Science and Engineering This Integrative Graduate Education and Research Training (IGERT) award supports the establishment of a multidisciplinary graduate training program of education and research on the dynamics of complex systems. Nonlinear science has increasing impact on many disciplines in the natural sciences, engineering, and medicine, and requires training that bridges traditional departmental and school boundaries. This graduate training program emphasizes the unity of fundamental concepts underlying a broad range of scientific research areas: nonlinear optics, computational neuroscience, pattern formation, chaos, ergodic theory; and applications to engineering and materials science problems: interface motion, combustion, and mixing. The goal is to prepare the students for today's rapidly evolving professional environment. Students will be equipped with the tools and intuition needed to tackle complex nonlinear problems arising in various guises and technical fields. Cross-disciplinary research and communication skills will be developed through an intensive year-long course, in which small graduate-student teams will investigate a topic guided by two faculty members with complementary perspectives. This early research experience will be followed by a thesis on a different topic; in the case of IGERT fellows the thesis project will be co-advised and cross-disciplinary. Internships will provide additional training experience. Cross-departmental research seminars and student-run seminars, regional workshops, yearly retreats, and an active visitor program will foster a highly cooperative, diverse, cross-disciplinary training environment. IGERT is an NSF-wide program intended to meet the challenges of educating Ph.D. scientists and engineers with the multidisciplinary backgrounds and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing new, innovative models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries. In the third year of the program, awards are being made to nineteen institutions for programs that collectively span all areas of science and engineering supported by NSF. The intellectual foci of this specific award reside in the Directorates for Mathematical and Physical Sciences; Engineering; Computer and Information Science and Engineering; and Education and Human Resources. Show summary Hide summary	0.915
2016 — 2019	Solla, Sara Hartmann, Mitra [⬀]	N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information	The Vibrissotactile Natural Scene @ Northwestern University In humans, the sense of touch is closely linked with hand movements. An open question in neuroscience is how the brain combines touch signals (sensory) with hand movements (motor) to create a unified tactile perception of an object. Because of difficulties in studying how the human brain combines these cues, researchers study rats, which use ~60 whiskers to tactually explore the environment. The whisker system is an excellent model to investigate how neurons represent and unify touch and movement, but neuroscientists currently struggle to stimulate the whiskers in a way that imitates the signals obtained during natural exploratory behavior. In this proposal, the investigators will characterize naturalistic patterns of tactile input that the rat's brain evolved to process, and will develop new mathematical tools to describe the environmental features that the rat experiences. The proposed work is scientifically important for two reasons. First, because rodents are the most commonly used animals in neuroscience, these experiments will aid researchers studying many parts of the brain involved with touch and movement. Second, these new mathematical tools will help quantify the sense of touch across species, including humans. The proposed work will have significant broader impacts on science and mathematics education and public outreach. Undergraduate students will contribute to the work, and the investigators will lead Northwestern's Robotics Club to explore engineering applications of whisker-based tactile sensing. The investigators will also continue outreach efforts through the Society of Hispanic Professional Engineers, the Society of Women Engineers, and Chicago's Museum of Science and Industry. In the fields of vision and audition, the receptive fields of central neurons are tuned to the statistics of the "natural scenes,- to those properties of the stimuli that the animal is likely to encounter in its natural environment. In the field of somatosensation it is challenging to quantify the natural tactile scene, in part because somatosensory signals are tightly linked to the animal's movements. The proposed work aims to begin to quantify the natural tactile scene for the rat vibrissal system by combining careful behavioral monitoring and simulations of rat head and whisker movements. The project has two major goals. The first is to characterize the statistics of the environments that the rat naturally inhabits. In Bayesian terms, this statistical distribution is called the "prior," because it describes the environment's geometrical features, unbiased by the tactile sampling choices of the animal. The second is to quantify the statistics of the environment sampled by the rat, given its choices of head motions and whisk cycle. In Bayesian terms, this statistical distribution is called the "posterior," because it incorporates the bias of the rat when preferentially sampling the tactile scene. This work is one of the first attempts to quantify the statistics of active touch, and it aims to make specific predictions for the receptive field properties that enable spatiotemporal integration. Equally important, this work will develop an appropriate mathematical framework for characterizing the geometry of natural scenes, an essential step towards describing active somatosensation using information theoretic measures. Show summary Hide summary	0.915
2017 — 2021	Miller, Lee E [⬀] Mussa-Ivaldi, Ferdinando Alessandro (co-PI) [⬀] Perreault, Eric J. (co-PI) [⬀] Solla, Sara A	R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies.	A Primate Model of An Intra-Cortically Controlled Fes Prosthesis For Grasp @ Northwestern University At Chicago Project Summary In the space of barely over ten years, Brain Computer Interfaces (BCIs) used to restore movement have developed from the stuff of science fiction to clinically relevant devices. However, most existing BCIs, while technically remarkable, require the user to be wired to stationary equipment, and allow only intermittent control of a computer cursor or disembodied robotic limb. They require that the algorithm linking brain activity to the restored movement be frequently recalibrated. We have developed a wireless BCI that will operate 24 hours a day, restoring voluntarily movement to monkeys despite paralysis of their hand, for a broad range of their normal motor behaviors, such as foraging, feeding, or playing with enrichment toys. By using ?autoencoding neural networks? we will be able to greatly extend the period over which the BCI will work without recalibration. We have developed a unique model of spinal cord injury (SCI) using a chronically implanted infusion pump that delivers a potent drug (tetrodotoxin) to cuffs placed around two key nerves in the arm. The drug causes a nerve block that produces the acute effects of spinal cord injury for indefinite periods of time, yet with full recovery within a day of stopping the drug. Prior to the nerve block, we will record wirelessly not only neural signals from the brain, but also electromyograms (EMGs) from a large number of muscles in the arm and hand. We will make these recordings not only during typical, constrained motor behaviors in the lab, but also during completely unconstrained behaviors while the monkey is in its home cage. We will use the data to develop algorithms (?decoders?) that transform the neural signals into predicted EMG signals. Following the onset of paralysis, our BCI will use these EMG predictions as control signals for Functional Electrical Stimulation (FES), causing contractions of the paralyzed muscles that the monkey can control voluntarily through the computer interface. We will study the gradually changing brain activity as the monkeys learn to use this FES BMI. In addition, we will attempt to augment the monkey's performance by developing ?adaptive? decoders that improve their performance in parallel with the monkey's own adaptation, as well as ?teacher? decoders that coach the monkeys, pushing them toward desired control strategies and away from counterproductive ones. This technology gives us the ability to study the brain's representation of movement across a range of motor behaviors that has never been possible before. During paralysis, it will allow us to study motor learning and adaption without the limitations imposed by the intermittent availability of current BCIs. Finally, it provides a platform close to that necessary for clinical translation, with which we will be able to study the limits of current decoders and to develop nonlinear and adaptive decoders designed to assist the monkey's own adaptive processes. While this application is focused on restoration of grasp, its general principles will extend to the control of reaching, lower limb function, and even prosthetic limbs. Ultimately, this work will develop the interface, decoder, and control technology that will be necessary to move BCIs from the lab to the clinic. Show summary Hide summary	1