Humans and other animals possess neural integrators, brain modules specialized for performing the mathematical operation of integrating a time-varying signal. This computation is important for certain behaviors such as motor control, navigation, and decision making. Transient stimuli to neural integrators produce sustained changes in rate of action potential discharge that persist for up to tens of seconds. Our past research suggests that this persistent neural activity, a correlate of the integrator's memory, is supported by both cellular and circuit mechanisms acting in concert. Our long-range goal is to understand the exact nature of these mechanisms through a collaborative research program combining experimental and theoretical studies of the goldfish oculomotor integrator. To more precisely localize the integrator, intracellular electrodes will be used in vivo to precisely stimulate and inhibit single neurons while extracellular recording methods are used to monitor the effects on other neurons in the circuit. The possibility that vestibular nuclei are part of the integrator will be tested using local pharmacological inactivation. Serial section electron microscopy, and paired recording in a novel in vitro preparation, will be used to improve our understanding of the synaptic connectivity of integrator neurons. Specific hypothesized cellular mechanisms of persistence will be tested using two-photon calcium imaging of dendrites. This information will be used to construct improved hybrid models of the integrator, incorporating dendritic biophysics as well as realistic synaptic connectivity. The role of the cerebellum in a recently discovered form of integrator plasticity will be tested by extracellular recording methods. Models of integrator plasticity based on synaptic learning rules will be developed. The proposed research should have broad significance for neuroscience. Persistent neural activity has been observed in many brain areas, not just in neural integrators, and therefore its mechanisms are of very general interest. Integration can be regarded as the simplest form of working memory, the ability to store information and actively manipulate it. Therefore, understanding how neurons integrate could shed light on how working memory is implemented by the brain. Many of the hypotheses in this proposal are generic to hypothesized circuit and cellular mechanisms of persistence in other brain areas; hence the results of testing them may be relevant to persistent neural activity in general. Health Relatedness: Persistent neural activity has consistently been observed in brain areas important in working memory, a central component of many cognitive abilities. Some mental disorders, such as schizophrenia, may involve deficits in working memory and neural integrators. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
2R01MH060651-06A1
Application #
7096849
Study Section
Cognitive Neuroscience Study Section (COG)
Program Officer
Glanzman, Dennis L
Project Start
1999-12-01
Project End
2011-05-31
Budget Start
2006-06-01
Budget End
2007-05-31
Support Year
6
Fiscal Year
2006
Total Cost
$454,999
Indirect Cost
Name
Princeton University
Department
Biochemistry
Type
Schools of Arts and Sciences
DUNS #
002484665
City
Princeton
State
NJ
Country
United States
Zip Code
08544
Kim, Christina K; Miri, Andrew; Leung, Louis C et al. (2014) Prolonged, brain-wide expression of nuclear-localized GCaMP3 for functional circuit mapping. Front Neural Circuits 8:138
Miri, Andrew; Daie, Kayvon; Arrenberg, Aristides B et al. (2011) Spatial gradients and multidimensional dynamics in a neural integrator circuit. Nat Neurosci 14:1150-9
Miri, Andrew; Daie, Kayvon; Burdine, Rebecca D et al. (2011) Regression-based identification of behavior-encoding neurons during large-scale optical imaging of neural activity at cellular resolution. J Neurophysiol 105:964-80
Harvey, Christopher D; Collman, Forrest; Dombeck, Daniel A et al. (2009) Intracellular dynamics of hippocampal place cells during virtual navigation. Nature 461:941-6
Werfel, Justin; Xie, Xiaohui; Seung, H Sebastian (2005) Learning curves for stochastic gradient descent in linear feedforward networks. Neural Comput 17:2699-718
Goldman, Mark S (2004) Enhancement of information transmission efficiency by synaptic failures. Neural Comput 16:1137-62