Neural network optimization algorithms greatly enhance our ability to construct large-scale, dynamical models of highly interconnected networks. Until now, optimization has only been applied to networks of simplistic processing units, ignoring the integrative and temporal response properties of single neurons, thus limiting the predictive power of the models. The long-term goal of this project is to develop a hybrid modeling strategy in which optimization methods are applied to networks of realistic,multicompartmental model neurons. To accomplish this goal, we will construct a hybrid model of an actual distributed processing network composed of repeatably identifiable sensory, motor, and interneurons that computes a well-defined behavioral input-output function. Optimization will be used to predict the connectivity of as-yet-unidentified interneurons in the actual network and the predictions will be tested by identifying the interneurons by physiological and morphological means. Performance of the hybrid model will be assessed by comparing it to the performance of an a priori model in which all connection strengths are determined physiologically. The final model will be used to predict the loci of synaptic plasticity underlying nonassociative conditioning of the reflex by incorporating local learning rules and by optimization methods. The predictions will be tested by determining the actual plastic sites physiologically. This project will have the combined effect of enhancing the predictive power of optimized network models and illuminating the relation between computations at the single-neuron and network levels.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
First Independent Research Support & Transition (FIRST) Awards (R29)
Project #
5R29MH051383-05
Application #
2675147
Study Section
Cognitive Functional Neuroscience Review Committee (CFN)
Project Start
1994-05-01
Project End
1999-04-30
Budget Start
1998-05-01
Budget End
1999-04-30
Support Year
5
Fiscal Year
1998
Total Cost
Indirect Cost
Name
University of Oregon
Department
Neurosciences
Type
Schools of Arts and Sciences
DUNS #
948117312
City
Eugene
State
OR
Country
United States
Zip Code
97403
Roberts, William M; Augustine, Steven B; Lawton, Kristy J et al. (2016) A stochastic neuronal model predicts random search behaviors at multiple spatial scales inC. elegans. Elife 5:
Heckscher, Ellie S; Zarin, Aref Arzan; Faumont, Serge et al. (2015) Even-Skipped(+) Interneurons Are Core Components of a Sensorimotor Circuit that Maintains Left-Right Symmetric Muscle Contraction Amplitude. Neuron 88:314-29
Faumont, S; Lindsay, T H; Lockery, S R (2012) Neuronal microcircuits for decision making in C. elegans. Curr Opin Neurobiol 22:580-91
Faumont, Serge; Rondeau, Gary; Thiele, Tod R et al. (2011) An image-free opto-mechanical system for creating virtual environments and imaging neuronal activity in freely moving Caenorhabditis elegans. PLoS One 6:e24666
McCormick, Kathryn E; Gaertner, Bryn E; Sottile, Matthew et al. (2011) Microfluidic devices for analysis of spatial orientation behaviors in semi-restrained Caenorhabditis elegans. PLoS One 6:e25710
Lindsay, Theodore H; Thiele, Tod R; Lockery, Shawn R (2011) Optogenetic analysis of synaptic transmission in the central nervous system of the nematode Caenorhabditis elegans. Nat Commun 2:306
Singh, Komudi; Chao, Michael Y; Somers, Gerard A et al. (2011) C. elegans Notch signaling regulates adult chemosensory response and larval molting quiescence. Curr Biol 21:825-34
Izquierdo, Eduardo J; Lockery, Shawn R (2010) Evolution and analysis of minimal neural circuits for klinotaxis in Caenorhabditis elegans. J Neurosci 30:12908-17
Thiele, Tod R; Faumont, Serge; Lockery, Shawn R (2009) The neural network for chemotaxis to tastants in Caenorhabditis elegans is specialized for temporal differentiation. J Neurosci 29:11904-11