Understanding sensory systems relies on characterizing the stimulus-response properties of neurons at each stage of processing. This characterization can then be used to investigate the neural mechanisms underlying sensory maps and to derive computational models of sensory processing. Such knowledge will enable us to better understand the computations performed by the human brain in general and in particular will provide insights on how to develop better sensory prosthetics. We propose to develop a novel suite of quantitative methods for objectively characterizing the nonlinear responses of sensory neurons. A unique feature of our methods is that they can be used with complex, naturalistic stimuli as well as with conventional simple stimuli commonly used in sensory neurophysiology. The powerful combination of nonlinear analysis and complex stimuli potentially enables us to characterize sensory neurons even at relatively high levels of sensory processing. Much of our proposal focuses on developing the quantitative computational tools necessary for our analyses. First, we propose to develop algorithms for estimating nonlinear receptive field profiles of sensory neurons from responses to arbitrary stimuli. Second, we propose to develop tools for evaluating the quality of the resulting receptive field models. Third, we will develop analysis tools that will facilitate the biological interpretation of the derived models. Finally, we propose to develop a comprehensive software package that will include both linear and nonlinear estimation techniques as well as the evaluation and analysis tools. The package will consist of a stand alone documented function library for the most experienced users, a turn-key package with an integrated graphical user interface for general users and extensive online documentation. The tools and analyses will be validated using computational models and data collected in our laboratories and in other laboratories that have agreed to aid us in software testing and evaluation.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH066990-03
Application #
6735720
Study Section
Special Emphasis Panel (ZAG1-FAS-7 (J3))
Program Officer
Hirsch, Michael D
Project Start
2002-05-08
Project End
2006-04-30
Budget Start
2004-05-01
Budget End
2005-04-30
Support Year
3
Fiscal Year
2004
Total Cost
$339,666
Indirect Cost
Name
University of California Berkeley
Department
Psychology
Type
Schools of Arts and Sciences
DUNS #
124726725
City
Berkeley
State
CA
Country
United States
Zip Code
94704
Stansbury, Dustin E; Naselaris, Thomas; Gallant, Jack L (2013) Natural scene statistics account for the representation of scene categories in human visual cortex. Neuron 79:1025-34
Woolley, Sarah M N; Hauber, Mark E; Theunissen, Frederic E (2010) Developmental experience alters information coding in auditory midbrain and forebrain neurons. Dev Neurobiol 70:235-52
Woolley, Sarah M N; Gill, Patrick R; Fremouw, Thane et al. (2009) Functional groups in the avian auditory system. J Neurosci 29:2780-93
Gill, Patrick; Woolley, Sarah M N; Fremouw, Thane et al. (2008) What's that sound? Auditory area CLM encodes stimulus surprise, not intensity or intensity changes. J Neurophysiol 99:2809-20
Woolley, Sarah M N; Gill, Patrick R; Theunissen, Frederic E (2006) Stimulus-dependent auditory tuning results in synchronous population coding of vocalizations in the songbird midbrain. J Neurosci 26:2499-512
Hsu, Anne; Borst, Alexander; Theunissen, Frederic E (2004) Quantifying variability in neural responses and its application for the validation of model predictions. Network 15:91-109
Singh, Nandini C; Theunissen, Frederic E (2003) Modulation spectra of natural sounds and ethological theories of auditory processing. J Acoust Soc Am 114:3394-411