In the past decade, our conception of neural communication has been broadened by the evidence that certain molecules may act as rapid volume signals that diffuse throughout local regions of neural tissue. One such volume signal, nitric oxide (NO), has been implicated in behavioral learning, synaptic plasticity, blood flow control, neurotoxicity, and the ongoing control of synaptic transmission. One of the novelties of using a volume signal like NO is the idea of cross talk through neural tissue in the absence of direct synaptic contacts. We have termed this cross-talk volume signaling and the long-term changes resulting from it volume learning. The long-term goal of this project is to uncover the computational properties of learning and processing mechanisms in the brain that use volume signals. Our previous work on focused on NO and the computational consequences of learning mechanisms that operate in a diffusion-defined domain. We propose to extend our work on nitric oxide signaling to include two other volume signals: (1) the catecholamine dopamine and (2) changes in external calcium concentrations. The movement of dopamine through the interstices of the extracellular space is important because changes in dopamine delivery appear to encode prediction errors about the time and magnitude of future rewarding events. Moreover, dopamine systems are targets of drugs of abuse, so an understanding of computations carried out by fluctuating dopamine delivery is paramount. For external calcium, experiments have long shown that dramatic changes in its levels attend normal neural activity. We have developed a computational framework in which changes in external calcium levels represent information-bearing signals. This new framework is in its infancy, but current results suggest that a new style of computing is being implemented by external calcium fluctuations. Furthermore, the ubiquitous dependence of synaptic transmission on external calcium levels makes crucial an understanding of how and why information processing in the brain would employ fluctuations in this limited, necessary resource.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
2R01MH052797-06A1
Application #
6133563
Study Section
Special Emphasis Panel (ZRG1-IFCN-7 (01))
Program Officer
Glanzman, Dennis L
Project Start
1994-07-01
Project End
2005-04-30
Budget Start
2000-05-01
Budget End
2001-04-30
Support Year
6
Fiscal Year
2000
Total Cost
$261,187
Indirect Cost
Name
Baylor College of Medicine
Department
Neurosciences
Type
Schools of Medicine
DUNS #
074615394
City
Houston
State
TX
Country
United States
Zip Code
77030
King-Casas, Brooks; Sharp, Carla; Lomax-Bream, Laura et al. (2008) The rupture and repair of cooperation in borderline personality disorder. Science 321:806-10
McClure, Samuel M; York, Michele K; Montague, P Read (2004) The neural substrates of reward processing in humans: the modern role of FMRI. Neuroscientist 10:260-8
McClure, Samuel M; Berns, Gregory S; Montague, P Read (2003) Temporal prediction errors in a passive learning task activate human striatum. Neuron 38:339-46
McClure, Samuel M; Daw, Nathaniel D; Montague, P Read (2003) A computational substrate for incentive salience. Trends Neurosci 26:423-8
Montague, P Read; Berns, Gregory S (2002) Neural economics and the biological substrates of valuation. Neuron 36:265-84
Egelman, D M; Montague, P R (1999) Calcium dynamics in the extracellular space of mammalian neural tissue. Biophys J 76:1856-67
Egelman, D M; Person, C; Montague, P R (1998) A computational role for dopamine delivery in human decision-making. J Cogn Neurosci 10:623-30
Egelman, D M; Montague, P R (1998) Computational properties of peri-dendritic calcium fluctuations. J Neurosci 18:8580-9
Montague, P R (1996) The resource consumption principle: attention and memory in volumes of neural tissue. Proc Natl Acad Sci U S A 93:3619-23
Montague, P R; Dayan, P; Sejnowski, T J (1996) A framework for mesencephalic dopamine systems based on predictive Hebbian learning. J Neurosci 16:1936-47

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