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Topological bridges between circuits, models, and behavior

电路、模型和行为之间的拓扑桥梁

基本信息

批准号：
10208403
负责人：
Marlene Rochelle Cohen
金额：
$ 281.4万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-05-01 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10208403
关键词：
Affect Area Attention Behavior Behavioral Biology Biophysics Brain Cognition Cognitive Complex Data Data Analyses Decision Making Diagnosis Dimensions Electrophysiology (science)Esthesia Goals Image Information Theory Interneurons Link Macaca mulatta Mathematics Measurement Measures Metals Methods Modeling Monkeys Motivation Mus Neurons Oceans Parietal Parietal Lobe Perception Performance Physiological Population Primates Process Psychophysics Recurrence Rewards Role Sensory Sensory Process Stimulus Structure System Testing Time Visual Visual Cortex Work algebraic topology association cortex cell type cognitive process cognitive system detector flexibility information processing inhibitory neuron insight nervous system disorder neural network neuronal circuitry neurophysiology neuropsychiatric disorder novel novel strategies optogenetics predictive modeling relating to nervous system response sensory system success tool two-photon

项目摘要

Project summary The plight of the neuroscientist trying to understand the brain using linear analysis methods is akin to studying the makeup of the ocean using the bits you find with a metal detector. Everything we know about the neural basis of decision making, from biology to computation to behavior, makes it clear that the relationship between neurons and behavior is profoundly nonlinear. However, for good mathematical reasons, our attempts to understand that relationship typically rely only on linear measures. These measures have an especially hard time dealing with the reality that neural networks are far from static. Indeed, the flexibility of interactions between neurons, while adding an additional nonlinearity, is a critically important clue about underlying mechanisms and computations. The goal of the proposed project is to test the hypothesis that nonlinear measures of correlated variability in a population of neurons will 1) establish a strong link between neurons and perceptual decisions, 2) constrain models of the circuit mechanisms by which cognition affects perception, and 3) predict the effects of causally manipulating different subtypes of inhibitory interneurons on population activity. We will use and develop methods from algebraic topology to characterize the activity of neuronal populations in a holistic, nonlinear way. Our project leverages the complementary strengths between three highly interactive approaches: primate neurophysiology and psychophysics, modeling neuronal circuits, and two photon imaging and optogenetic manipulation of subtypes of inhibitory interneurons in mice. In Aim 1, we will test the hypothesis that sensory and cognitive processes including contrast, adaptation, attention, and motivation affect performance on visual tasks exactly when they change the topological signatures of the correlated variability in visual or parietal cortex. In Aim 2, we will use a biophysically realistic model to understand which changes in a cortical circuit would or would not change the topological signatures of neuronal population activity. In Aim 3, we will test the predictions of our model to understand how manipulating the activity of different subtypes of inhibitory interneurons affects topological summaries of neuronal activity and information processing in the network. This work uses novel mathematical ideas to bridge different levels of the study of cortical circuits. It will have implications for our understanding of the relationship between neuronal circuits and behavior across species, systems, and theoretical approaches.

项目总结