2012 — 2014 |
Churchland, Mark Montgomery |
DP2Activity Code Description: To support highly innovative research projects by new investigators in all areas of biomedical and behavioral research. |
A Dynamical Systems Approach to Fundamental Questions in Neuroscience @ Columbia University Health Sciences
DESCRIPTION (Provided by the applicant) Abstract: The brain is not only a remarkable computational organ - capable of feats that stymie the best computers and robots - it is the generator of our thoughts and actions. Yet modern systems neuroscience has principally asked how the brain transforms inputs into outputs. This approach has deep historical roots: both Descartes and Sherrington saw the nervous system as a massively elaborated reflex. The approach also produced critical early successes: the descriptions by Mountcastle, Hubel, and Wiesel, of how sensory stimuli drive single- neuron responses. Yet the brain is clearly more than a glorified input-output device. The neural networks within it do not just respond reflexively to external stimuli, they also generate their ow activity. In doing so they produce thoughts, plans, decisions and actions. As the study of such processes becomes increasingly central to systems neuroscience, we will need to become increasingly concerned with internal neural dynamics: how neural circuitry shapes and generates the responses that allow us to act upon the world. We will become less interested in how individual neurons reflect external stimuli. We will become much more interested in the dynamics of how neural activity sustains and shapes itself over time. I believe this rising interes in internal neural dynamics will drive large changes in the conceptual, analytical, and experimental paradigms employed by systems neuroscience. The first changes will focus on collecting, visualizing, and analyzing data that can reveal underlying dynamics: how the state of the neural circuit at one point in time leads lawfully to the state of the neural circuit at the net point in time. The focus will then shift to designing experiments that most effectively probe dynamics. Such experiments will borrow techniques from the physical sciences and from engineering, but will initially be based on the traditional behavioral paradigm of systems neuroscience in which animals are trained to produce tightly-controlled behavior. However, I believe the traditional experimental framework will give way to a new one. Instead of indirectly influencing neural activity by operantly conditioning behavior, we will directly monitor and operantly condition the internally generated neural activity itself. This methodology will be built upon the technical platform recently developed in the service of neuro-motor prostheses, but will serve a basic scientific purpose: it will give the experimenter unprecedented control over the system they are trying to understand, and allow stringent tests of hypotheses regarding dynamics. My goal is to help build this emerging paradigm. A subsequent but equal goal is to leverage our growing understanding of neural dynamics. I believe that we should be able to develop a new class of neural prosthetic device that uses the dynamic patterns of motor cortex activity to drive artificial locomotion. I believe this is both the best way to demonstrate that ou hard-won knowledge of dynamics is meaningful, and that it may be one of the most effective ways to develop a neuro-motor prosthesis that will help significant numbers of people. Public Health Relevance: The proposed research aims to improve our understanding of how the brain generates patterns of activity, including those patterns of activity that allow us to mov our limbs and to walk. We propose to leverage that knowledge to build a proof-of-concept neural prosthetic that allows direct neural control of locomotion, something that could greatly improve the live the hundreds of thousands of tetra- and quadriplegics. The proposed research is also of relevance to the many diseases where the ability to generate normally patterned neural activity is lost: most obviously motor disorders such as Parkinson's disease, and potentially cognitive disorders as well.
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0.954 |
2017 — 2021 |
Churchland, Mark Montgomery |
U19Activity Code Description: To support a research program of multiple projects directed toward a specific major objective, basic theme or program goal, requiring a broadly based, multidisciplinary and often long-term approach. A cooperative agreement research program generally involves the organized efforts of large groups, members of which are conducting research projects designed to elucidate the various aspects of a specific objective. Substantial Federal programmatic staff involvement is intended to assist investigators during performance of the research activities, as defined in the terms and conditions of award. The investigators have primary authorities and responsibilities to define research objectives and approaches, and to plan, conduct, analyze, and publish results, interpretations and conclusions of their studies. Each research project is usually under the leadership of an established investigator in an area representing his/her special interest and competencies. Each project supported through this mechanism should contribute to or be directly related to the common theme of the total research effort. The award can provide support for certain basic shared resources, including clinical components, which facilitate the total research effort. These scientifically meritorious projects should demonstrate an essential element of unity and interdependence. |
Extracting Computational Principles Governing the Relation Between Brain Activity and Muscle Activity That Are Conserved Between Rodents and Primates @ Columbia University Health Sciences
Abstract Since the late 1960?s, a large literature has attempted to characterize the properties of neural responses in motor areas of the brain, and to relate those responses to externally measured variables such as muscle activity or reach direction. In some ways this field has been very successful: early studies revealed robust movement-related modulation of neural firing rates, across a broad network of reciprocally connected areas. Such activity is broadly tuned, in the sense that most neurons respond during most movements, and it was thus appreciated that the relevant computations must be understood at the population level, rather than via the properties of a small subset of responsive neurons. Yet the nature of that population-level computation has remained controversial. This is true even of primary motor cortex, which has made it harder still to characterize and contrast the different computations made by different cortical areas. In general, there remains vigorous disagreement regarding the relationship of cortical activity to the ultimate of the motor system: complex, intricate, temporally rich patterns of activity across a large population of muscles. A fundamental conundrum has been that neural responses in motor cortex (and elsewhere) resemble the responses of muscles in some ways but not others. We will attempt to resolve this apparent paradox through two means. First, we will use emerging methods in rodent that allow recordings from subpopulations of motor cortex neurons, identified via the populations of spinal interneurons to which they project. This will allow us to ask whether the logic of motor cortex responses becomes clearer when subpopulations, with potentially very different roles, are segregated rather than lumped together. Second, we will use analysis methods motivated by network-theory to characterize computationally relevant aspects of the population response. Such methods, many of which exploit machine-learning techniques, hold the promise of explaining otherwise confusing aspects of the population response. Such methods can seek structure predicted by models, determine if it is present, and if so whether it is differentially present across different populations (i.e., subpopulations within motor cortex and populations in other cortical areas). We will also use network modeling both to produce hypotheses, and to explore the computational relevance of novel structure uncovered by our methods. Preliminary data indicate that different populations can appear very similar when analyzed via traditional means, yet show very different population-level structure when approached via our novel methods. Our believe is that a combination of network modeling, analyses inspired by computational-level theories, and a variety of novel classes of data, will allow progress in defining what motor cortex shares with the downstream muscles, what additional computationally relevant properties motor cortex has that the muscles do not, how various computationally relevant properties are / aren?t shared among motor cortex subpopulations, and how the population response in different cortical areas varies in computationally relevant dimensions.
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0.954 |