2019 — 2022 |
Ro, Tony Emmanouil, Tatiana Aloi |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Af: Small: Collaborative Research: a Computational Theory of Brain Function
This project seeks to identify, explore, render rigorous, and validate one piece of the solution to the puzzle "how does the brain work?" - one of the truly fundamental and most challenging frontiers in all of science. Computation in the brain will be approached at an intermediate level of scale, far larger than that of individual neurons and synapses yet significantly smaller than that of the whole brain. The core hypothesis is that "assemblies," large and highly interconnected sets of neurons, are the engine of brain computation. Studying computation at this level is crucial for understanding higher cognitive functions, especially in humans, such as reasoning, planning, and language; and this formulation of brain computation is particularly amenable to the methodology and point of view of the theory of computation, and will further its reach. This project is quintessentially interdisciplinary, and will provide multi-faceted training to graduate and undergraduate students in Computer Science Theory, Machine Learning, and Cognitive Neuroscience and Psychology. It will develop interdisciplinary graduate courses in this particular scientific interface. The results of the project will be disseminated broadly via conferences and journals in all these disciplines, but also in colloquia and public lectures, while students of a great variety of backgrounds will participate in a cutting-edge research experience. Assemblies can be the basis of a powerful computational system involving a repertoire of operations including project, associate, and merge. These operations can be shown, through theorems and simulations, to be plausible (that is, they can be "compiled down" to the level of neurons and synapses) and useful (in the sense that they can help explain extant experimental results). The project will pursue this assembly hypothesis through: (1) expanding our modeling and our mathematical techniques of analysis for the study of assembly computation; (2) developing more accurate and efficient simulation methodology; (3) embarking on a multi-pronged exploration of the computational power of assemblies in novel modalities beyond formal computation, in particular (a) probabilistic and dynamical systems-like computation through pattern completion and (b) learning and prediction; (4) mathematical modeling and algorithmic investigation of the ways in which the dynamics and biases of synaptic connectivity, as well as assembly overlap, affect the various modes of brain computation; and, importantly, (5) functional magnetic resonance imaging (fMRI) experiments, and the analysis of the results of these experiments and extant electrocorticography (ECoG) data through novel algorithmic and machine learning techniques for the purpose of identifying evidence of assembly computation in the human brain.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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0.958 |
2020 |
Emmanouil, Tatiana Aloi |
R15Activity Code Description: Supports small-scale research projects at educational institutions that provide baccalaureate or advanced degrees for a significant number of the Nation’s research scientists but that have not been major recipients of NIH support. The goals of the program are to (1) support meritorious research, (2) expose students to research, and (3) strengthen the research environment of the institution. Awards provide limited Direct Costs, plus applicable F&A costs, for periods not to exceed 36 months. This activity code uses multi-year funding authority; however, OER approval is NOT needed prior to an IC using this activity code. |
Neural Correlates of Ensemble Perception @ Bernard M. Baruch College
Project summary The capacity of the visual system is limited: several studies have shown that we can only extract detailed information about a handful of objects at a time. Despite these limitations, people subjectively report rich perceptual experiences and they demonstrate a sophisticated ability to navigate the visual environment. One mechanism that potentially accounts for this discrepancy is ensemble perception: the ability to summarize large amounts of information that exceed the limits of attention. A growing body of research now suggests that people extract the statistical mean from large groups of objects, for example they can report the average size and speed of objects in the visual environment, as well as average expression in a crowd of faces. Although there are several behavioral experiments investigating the speed, efficiency and automaticity of ensemble coding, the neural substrates of this mechanism remains largely unexplored. In particular, the areas involved in ensemble coding, as well as the timing by which ensemble properties are computed remain unclear. The goal of this proposal is to test the hypothesis that ensemble properties are represented in early visual areas (Aim 1), and are computed at early stages of visual processing (Aim 2). One set of experiments (Aim 1) will use functional Magnetic Resonance Imaging (fMRI) and Multivariate Pattern Analysis (MVPA) to decode whether early visual areas contain information about the specific mean value that participants perceive at a given time. Another set of experiments (Aim 2) will use Electroencephalography (EEG) to investigate the timing of ensemble perception, and to test the hypothesis that ensemble perception occurs before the processing of individual object details. Overall, the results will shed light on the neural representation of ensemble perception, and will advance our understanding of the mechanisms by which the visual system extracts large amounts of information from complex scenes without focused attention.
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0.958 |