2012 — 2016 |
Fischer, Brian J Pena, Jose L [⬀] Takahashi, Terry T (co-PI) [⬀] |
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. |
Crcns: Coding For Optimal Performances in Natural Environments @ Albert Einstein College of Medicine
DESCRIPTION (provided by applicant): Capturing nature's statistical structure in the neural coding is essential for optimal adaptation to the environment. This proposal investigates this issue by asking how the brain can approach statistical optimality in the sound localization system of barn owls. A Bayesian theoretical framework will be used to describe how sensory and a priori information can be combined optimally to guide orienting behavior. Specifically, we seek to demonstrate that sensory reliability and a priori information are represented in the response properties and topography of the neural population that represents auditory space. The first aim studies how sensory cue reliability is represented in the brain. Optimal use of sensory information requires that the statistical reliability of sensory cues is accessible from neural responses. Previous theories have suggested that cue reliability is encoded in the gain of neural responses or alternatively the selectivity of neural responses but how reliability is represented is not known. In the owl, changes in the statistical reliability of spatial cues resultin changes in sound localization behavior consistent with a Bayesian model. Our model predicts that the reliability is encoded in the tuning curve widths of space-specific neurons located in the owl's midbrain. We will manipulate tuning-curve widths and firing rates independently to test this hypothesis and test the model with behavior. The second aim will study whether the integration of spatial cues for sound localization follows the rules of statistical optimality. Perception in natural environments often depends on the integration of multiple cues, both within modalities and across modalities. Here, whether the integration is linear or nonlinear is crucial, as extending a Bayesian model from one to two dimensions indicates that optimal combination of conditionally independent sensory cues should be nonlinear. In the owl's brain, the spatial cues used to determine elevation and azimuth are processed independently and combined nonlinearly in the midbrain to form spatial receptive fields. However, whether or not sound localization cues are conditionally independent is unknown. This aim will demonstrate why nonlinear operations are essential for optimal cue combination and how they arise. We will perform in vivo intracellular recording and behavioral tests to address these questions. This will provide an experimental test of the prediction that optimal combination of conditionally independent cues is nonlinear. The third aim will extend the model to coding dynamic auditory scenes; the time dimension will be incorporated into the Bayesian model of sound localization. We will use a population vector model to determine how a neural system can achieve predictive power in auditory space through Bayesian inference. We will measure receptive fields of midbrain neurons in space and time to test the hypothesis that the owl has a bias for sources moving toward the center of gaze. We will use behavioral tests to measure detection thresholds for moving sound sources. Finally, we will study whether a dynamic gain control in a non-uniform network can account for Bayesian predictive coding of sound motion with a bias for sources moving toward the center of gaze. Broader Impacts: Outstanding open questions of how statistics of natural scenes are captured by neural coding include how reliability of sensory information is represented and combined with prior probabilistic knowledge, and how sensory cues are integrated to optimally guide behavior. This project addresses these questions in the heterogeneous representation of space of the owl's auditory midbrain. Whether non-uniform representations can be decoded using a population vector to perform Bayesian inference and that this mechanism works in multiple dimensions transcends sound localization in barn owls, becoming of general interest to neural coding. The PIs involved in this project, one of them a junior researcher, gather complementary expertise in modeling, physiology and behavioral approaches allowing for a truly interdisciplinary approach. This project will thus consolidate a powerful collaboration while providing groundbreaking information on outstanding questions in Neuroscience. The three institutions involved are committed to the training of underrepresented groups. The location of the Albert Einstein College of Medicine in the Bronx, makes it a pole of development in one of the most diverse and poor counties in the country and provides the potential for direct access to translational research. The inclusion of the Department of Mathematics at Seattle University, ranked among the top ten universities in the West for undergraduate programs, and the University of Oregon will ensure that this project will enhance training from the undergraduate to postdoctoral levels.
|
0.901 |
2017 — 2021 |
Debello, Wiliam Mcintyre Ellisman, Mark H (co-PI) [⬀] Fischer, Brian J Pena, Jose L [⬀] |
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. |
From Microscale Structure to Population Coding of Normal and Learned Behavior @ Albert Einstein College of Medicine
Abstract This study aims to understand how the ensemble activity and network architecture of a neuronal population guides natural and learned behavior. The model system is the midbrain localization pathway of the owl. Ensemble recordings, microcircuit analysis, behavioral measurements and computational modeling will be used to analyze the neural representation of auditory space and the head-orienting movement driven by it. The compact volume of tissue commanding this behavior makes a complete understanding of information processing tractable with high-throughput electrophysiological and microanatomical methods. How information about sound location is readout to guide orienting behaviors has not been demonstrated in any species. This project has the potential to fill this gap. Aim 1 will investigate the relationship between orienting behavior and activity in the neuronal population representing auditory space, in which frontal space is overrepresented. The hypothesis is based on recent work showing that sound localization can be explained by statistical inference, computed by integrating activity across the entire population. Microelectrode arrays (MEAs) will be used to map the activity of the population upon presentation of sounds. Population decoders will be constructed to determine how the population activity is readout to drive behavior. In Aim 2, the network architecture supporting the activity pattern will be studied with light and electron microscopy. Network models will combine the data to explain how connectivity and cellular computations result in the population activity and correlated firing that drives behavior. When auditory-visual cues are modified, the midbrain representation of auditory space adapts over time, and consequently drives a learned behavior. Aim 3 will directly examine this link. MEA recordings, microcircuit analysis and behavioral measurements will be made in owls adapted to prismatic spectacles. Population decoders will be used to test the hypothesis that population activity in the learned condition maintains a non-uniform population code with an overrepresentation of frontal space. Network models will be used to examine how local re-wiring may explain changes in the distribution of activity across the population. This would be the first time that neural activity and network architecture underlying sound localization are approached from the complete-population down to single-cell level, before and after learning. This integrative approach holds potential for understanding principles of population coding, plasticity and learning that operate across species and brain circuits.
|
0.901 |