Monty Escabi - US grants

[unreadable] DESCRIPTION (provided by applicant): The central nucleus of the inferior colliculus (ICC) plays a key role in the acoustic analysis of spectral, temporal and binaural information. Its laminar organization and highly ordered mosaic of converging afferents suggests a critical role in consolidating this acoustic information in the transition to the auditory thalamus. We hypothesize that the distributed layout of spectro-temporal and binaural receptive field features will be systematically ordered in the central nucleus in a manner that reflects the patterns of innervating afferents and the local collicular circuitry. In this study, we propose to characterize the distributed organization of spectro-temporal receptive field (STRF) preferences within the central nucleus. STRF analysis with structured broadband noise will be used to characterize neuronal preferences and to systematically relate these to measurements from conventional pure-tone stimuli. Single unit recording experiments are proposed to characterize the micro-organization of STRF preferences and to identify the roles of receptive field inheritance / construction in the transformation from the brainstem to the central nucleus. Distributed mapping of the ICC will then allow us to simultaneously characterize the global-organization of spectral, temporal and binaural receptive field preferences within the three dimensional structure. Concurrent anatomic studies will show the topographic position of two parallel recording tracks and their respective projections to medial geniculate body. The expected findings will provide a framework for understanding the acoustic analysis of spectro-temporal and binaural features in complex auditory stimuli, which we can then relate to the organizational hierarchy of brainstem inputs, local circuitry, and modular organization. [unreadable] [unreadable] [unreadable]

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
The hippocampal formation (hippocampus and entorhinal cortex) is essential for making episodic memories in the mammalian brain. The physical substrate of memory resides in the connections among groups of cells (ensembles) and their dynamic activity (electrophysiological) patterns. This project will use a rodent model to address: 1) whether different parts of the hippocampus and entorhinal cortex interact as a single functional group or alternatively work as separate functional groups and 2) the role of specific electrophysiological patterns (theta and gamma rhythms) in defining the activity of a group. Previous studies demonstrated that changes in theta and gamma coherence, a measure of temporal synchrony among neurons, relates to the strength of episodic memory formation and memory retrieval. A key component of the these studies will be how manipulations of the environment (e.g., novelty) alter theta and gamma coherence and whether theta and gamma coherence varies throughout different parts of the hippocampus and entorhinal cortex. This project will provide knowledge about functional interactions between different parts of the hippocampus and entorhinal cortex and how theta and gamma patterns either bring different parts together or segregate them in relation to specific sensory events. This project will also provide; 1) integrative biology training to high school, undergraduate and graduate students interested in psychology, neuroscience and biomedical engineering and 2) a large in vivo electrophysiological database for experimental and computational neuroscientists interested in hippocampal physiology and memory formation.

Human speech and other mammalian vocalizations exhibit extensive variability in their amplitude shape and rhythm. These sound features allow for discriminating and, ultimately, understanding the rich repertoire of vocal communications that characterize the vocal behavior of a large variety of animals including man. This proposal focuses on the roles of various stages of the mammalian forebrain ascending auditory pathway in categorizing and discriminating time variations in natural sounds, an important and open question in auditory physiology.

Neural response timing properties suggest that secondary ventral auditory cortices allow for an extended range of sensory temporal processing not accounted for in primary auditory cortex. The neural response properties of primary and secondary auditory cortices will be examined to determine their functional organization and contribution to sensory discrimination behavior. The approach involves the creation of sophisticated acoustic stimuli that are analytically tractable on the one hand, while mimicking the important aspects of natural sounds on the other. Neural responses will be characterized using electrophysiological, intrinsic-signal imaging, behavioral and computational techniques. Pharmacological manipulations will allow reversible loss-of-function studies to determine how primary and secondary auditory areas interact to promote behavioral discrimination of temporal cues in sound. Further, this project will determine how temporal cues on multiple time scales in sound are discriminated and encoded on a neural and whole organism level for Rattus norvegicus, as this species has an expanded surface area for primary auditory cortex and secondary auditory cortex compared to other mammals commonly used in auditory studies. Another goal of the study is to determine the extent to which sound percepts are built up through parallel and hierarchal processing within auditory forebrain structures. Finally, computer models will be developed to determine what aspects of neural activity patterns are critical for discriminating between the shape and rhythm of communication sounds. This multifaceted approach should allow for an unprecedented understanding of vocal perception that could be broadly applicable. Results from the study will be disseminated through presentations at scientific meetings and through peer-reviewed journal articles.

? DESCRIPTION (provided by applicant): Humans and other animals can discriminate and recognize sounds despite substantial acoustic variability in real-world sounds. This ability depends partly on the auditory system's ability to detect and utilize high-order statistical regularities that are present in the acoustic environment. Despite numerous advances in signal processing, assistive listening devices and speech recognition technologies lack biologically realistic strategies to dynamically deal with such acoustic variability. Thus, a comprehensive theory for how the central nervous system encodes and utilizes statistical structure in sounds is essential to develop processing strategies for sound recognition, coding and compression, and to assist individuals with hearing loss. This proposal presents a novel approach towards addressing the question of how the auditory system deals with and exploits statistical regularities for identification and discrimination of sounds in two critical mammalian auditory structures (inferior colliculus, IC; auditory cortex, AC) Aim 1 is to develop a catalogue of natural and man-made sounds and their associated high-order statistics. Cluster analysis and machine learning will be applied to the sound ensembles to identify salient statistical features that can be used to identify and categorize sounds from a computational perspective. Using information theoretic and correlation based methods, Aim 2 tests the hypothesis that statistical sound regularities are encoded in neural response statistics, including firing rate and spike-timing statistics of IC and AC neurons. Aim 3 will determine neurometric response functions and addresses the hypothesis that high-order statistical regularities in sounds can be discriminated based on temporal pattern and firing rate statistics of single neurons in IC and AC. Aim 4 will employ multi-site recording electrode arrays to tests the hypothesis that neural populations in IC and AC use high-order statistics for sound discrimination and that statistical regularities are encoded by regionally distributed differences n the strength and timing of neural responses or neuron-to-neuron correlations. The study will provide the groundwork for developing a general theory for how the brain encodes and discriminates sounds based on high-order statistical features. A catalogue of neural responses from single cells, neural ensembles, and high-level statistical features that differentiate real world sounds will be developed and deployed as an on-line resource. The role high-order statics play for sound recognition and discrimination will be identified both from a computational and neural coding perspective, including identifying transformations across neural structures, spatial and temporal scales. The project will foster collaborations between psychology, electrical engineering, and biomedical engineering departments at the UConn. Graduate, undergraduate and a post-doctoral student, including women and minorities, will participate in the research and will receive interdisciplinary training in areas of neurophysiology, computation neuroscience, and engineering. Drs. Read and Escabi regularly host summer interns in their labs and expect that 1-2 undergraduate students will be hosted per year. Graduate students will be enrolled in biomedical, electrical engineering, and psychology programs. Project findings will be integrated in graduate computational neuroscience and biomedical engineering coursework. The findings could lead to a host of new sound recognition technologies that make use of high-order statistical regularities to recognize and differentiate amongst sounds. Understanding how high-order statistics are represented in the brain could guide the development of optimal algorithms for detecting a target sound (e.g., speech) in variable/noisy conditions. Such sound recognition systems are also applicable in industrial applications: for instance, identifying fault machine systems from machine generated sounds. Knowledge of the statistical distributions in real world sounds and music will be useful for sound compression (e.g., mpeg coding) and to develop efficient sound processing algorithms. Finally, the findings can be incorporated in auditory prosthetics that mimic normal hearing physiology and make use of high-order sound statistics to remove background noise or enhance intelligibility.

Affect; Auditory; Auditory system; base; Behavior; Behavioral; behavioral study; Carrying Capacities; Cochlea; Code; Complex; Computer Models; Cues; Data; Discrimination; Electrodes; Frequencies; Human; improved; Individual; Inferior Colliculus; Influentials; Masks; Modeling; neural correlate; Neurons; neurophysiology; Noise; Oryctolagus cuniculus; Output; Perception; Population; receptive field; relating to nervous system; response; Role; sound; Source; Speech; Speech Discrimination; speech in noise; speech recognition; statistics; Stimulus; Structure; Study models; Testing; Texture; trend; Variant; Vision; vocalization;