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1.  Neural Correlates of Temporal Credit Assignment in the Parietal Lobe 
PLoS ONE  2014;9(2):e88725.
Empirical studies of decision making have typically assumed that value learning is governed by time, such that a reward prediction error arising at a specific time triggers temporally-discounted learning for all preceding actions. However, in natural behavior, goals must be acquired through multiple actions, and each action can have different significance for the final outcome. As is recognized in computational research, carrying out multi-step actions requires the use of credit assignment mechanisms that focus learning on specific steps, but little is known about the neural correlates of these mechanisms. To investigate this question we recorded neurons in the monkey lateral intraparietal area (LIP) during a serial decision task where two consecutive eye movement decisions led to a final reward. The underlying decision trees were structured such that the two decisions had different relationships with the final reward, and the optimal strategy was to learn based on the final reward at one of the steps (the “F” step) but ignore changes in this reward at the remaining step (the “I” step). In two distinct contexts, the F step was either the first or the second in the sequence, controlling for effects of temporal discounting. We show that LIP neurons had the strongest value learning and strongest post-decision responses during the transition after the F step regardless of the serial position of this step. Thus, the neurons encode correlates of temporal credit assignment mechanisms that allocate learning to specific steps independently of temporal discounting.
doi:10.1371/journal.pone.0088725
PMCID: PMC3921206  PMID: 24523935
2.  Characterization of orderly spatiotemporal patterns of clock gene activation in mammalian suprachiasmatic nucleus 
The European journal of neuroscience  2011;33(10):1851-1865.
Because we can observe oscillation within individual cells and in the tissue as a whole, the suprachiasmatic nucleus (SCN) presents a unique system in the mammalian brain for the analysis of individual cells and the networks of which they are a part. While dispersed cells of the SCN sustain circadian oscillations in isolation, they are unstable oscillators that require network interactions for robust cycling. Using cluster analysis to assess bioluminescence in acute brain slices from PERIOD2∷Luciferase (PER2∷LUC) knockin mice, and immunochemistry of SCN from animals harvested at various circadian times, we assessed the spatiotemporal activation patterns of PER2 to explore the emergence of a coherent oscillation at the tissue level. The results indicate that circadian oscillation is characterized by a stable daily cycle of PER2 expression involving orderly serial activation of specific SCN subregions, followed by a silent interval, with substantial symmetry between the left and right side of the SCN. The biological significance of the clusters identified in living slices was confirmed by co-expression of LUC and PER2 in fixed, immunochemically stained brain sections, with the spatiotemporal pattern of LUC expression resembling that revealed in the cluster analysis of bioluminescent slices. We conclude that the precise timing of PER2 expression within individual neurons is dependent on their location within the nucleus, and that small groups of neurons within the SCN give rise to distinctive and identifiable subregions. We propose that serial activation of these subregions is the basis of robustness and resilience of the daily rhythm of the SCN.
doi:10.1111/j.1460-9568.2011.07682.x
PMCID: PMC3423955  PMID: 21488990
circadian rhythms; luciferase; networks; PER2; SCN; synchronization
3.  Two Antiphase Oscillations Occur in Each Suprachiasmatic Nucleus of Behaviorally Split Hamsters 
The Journal of Neuroscience  2005;25(39):9017-9026.
The suprachiasmatic nuclei (SCNs) control circadian rhythms of numerous behavioral and physiological responses. In hamsters, constant light causes “splitting” of circadian rhythms, such that a single daily bout of activity separates into two components, 12 h apart, with antiphase circadian oscillations in the left and right SCN. Given the phenotypic and functional heterogeneity of the SCN, in which ventrolateral but not dorsomedial neurons are retinorecipient, we asked how these two compartments respond to the constant lighting conditions that produce splitting, using three different phase markers of neuronal activity: PER1 (Period 1), c-FOS, and pERK (phosphorylated extracellular signal-regulated kinase). We report the emergence of a coherent novel network in which each side of the SCN exhibits two antiphase oscillating subregions, here termed “core-like” and “shell-like,” in addition to the known antiphase oscillation between the right and left SCN. The novel SCN response entails a coherent rhythm in a core-like region of the SCN, which otherwise is not cycling. A mathematical model is presented, and this model interprets the observed changes in the proportion of in-phase and antiphase populations of SCN oscillators and suggests novel testable hypotheses. Finally, the functional significance of this network was explored by investigating the adjacent hypothalamus. Activation of the paraventricular nucleus is in-phase with the ipsilateral core-like SCN, whereas activation of the lateral subparaventricular zone is in-phase with the ipsilateral shell-like SCN, pointing to a multiplicity of SCN output signals. These results suggest a neural basis for internal coincidence of SCN oscillators, and a novel mechanism of plasticity in SCN neural networks and outputs.
doi:10.1523/JNEUROSCI.2538-05.2005
PMCID: PMC3287349  PMID: 16192393
circadian rhythms; behavioral splitting; SCN; PER1; SCN efferents; oscillator model
4.  Gates and Oscillators II: Zeitgebers and the Network Model of the Brain Clock 
Journal of Biological Rhythms  2007;22(1):14-25.
Circadian rhythms in physiology and behavior are regulated by the SCN. When assessed by expression of clock genes, at least 2 distinct functional cell types are discernible within the SCN: nonrhythmic, light-inducible, retinorecipient cells and rhythmic autonomous oscillator cells that are not directly retinorecipient. To predict the responses of the circadian system, the authors have proposed a model based on these biological properties. In this model, output of rhythmic oscillator cells regulates the activity of the gate cells. The gate cells provide a daily organizing signal that maintains phase coherence among the oscillator cells. In the absence of external stimuli, this arrangement yields a multicomponent system capable of producing a self-sustained consensus rhythm. This follow-up study considers how the system responds when the gate cells are activated by an external stimulus, simulating a response to an entraining (or phase-setting) signal. In this model, the authors find that the system can be entrained to periods within the circadian range, that the free-running system can be phase shifted by timed activation of the gate, and that the phase response curve for activation is similar to that observed when animals are exposed to a light pulse. Finally, exogenous triggering of the gate over a number of days can organize an arrhythmic system, simulating the light-dependent reappearance of rhythmicity in a population of disorganized, independent oscillators. The model demonstrates that a single mechanism (i.e., the output of gate cells) can account for not only free-running and entrained rhythmicity but also other circadian phenomena, including limits of entrainment, a PRC with both delay and advance zones, and the light-dependent reappearance of rhythmicity in an arrhythmic animal.
doi:10.1177/0748730406296319
PMCID: PMC3281756  PMID: 17229921
zeitgeber; zeitnehmer; coupling; oscillator; synchronization
5.  Gates and Oscillators: A Network Model of the Brain Clock 
Journal of Biological Rhythms  2003;18(4):339-350.
The suprachiasmatic nuclei (SCN) control circadian oscillations of physiology and behavior. Measurements of electrical activity and of gene expression indicate that these heterogeneous structures are composed of both rhythmic and nonrhythmic cells. A fundamental question with regard to the organization of the circadian system is how the SCN achieve a coherent output while their constituent independent cellular oscillators express a wide range of periods. Previously, the consensus output of individual oscillators had been attributed to coupling among cells. The authors propose a model that incorporates nonrhythmic “gate” cells and rhythmic oscillator cells with a wide range of periods, that neither requires nor excludes a role for interoscillator coupling. The gate provides daily input to oscillator cells and is in turn regulated (directly or indirectly) by the oscillator cells. In the authors’ model, individual oscillators with initial random phases are able to self-assemble so as to maintain cohesive rhythmic output. In this view, SCN circuits are important for self-sustained oscillation, and their network properties distinguish these nuclei from other tissues that rhythmically express clock genes. The model explains how individual SCN cells oscillate independently and yet work together to produce a coherent rhythm.
PMCID: PMC3271846  PMID: 12932086
zeitgeber; zeitnehmer; coupling; SCN; oscillator; synchronization; amplitude
6.  Microsomal Prostaglandin E Synthase-2 Is Not Essential For In Vivo Prostaglandin E2 Biosynthesis 
Prostaglandin E2 (PGE2) plays an important role in the normal physiology of many organ systems. Increased levels of this lipid mediator are associated with many disease states, and it potently regulates inflammatory responses. Three enzymes capable of in vitro synthesis of PGE2 from the cyclooxygenase metabolite PGH2 have been described. Here, we examine the contribution of one of these enzymes to PGE2 production, mPges-2, which encodes microsomal prostaglandin synthase-2 (mPGES-2), by generating mice homozygous for the null allele of this gene. Loss of mPges-2 expression did not result in a measurable decrease in PGE2 levels in any tissue or cell type examined from healthy mice. Taken together, analysis of the mPGES-2 deficient mouse lines does not substantiate the contention that mPGES-2 is a PGE2 synthase.
doi:10.1016/j.prostaglandins.2008.10.003
PMCID: PMC3182462  PMID: 19010439
Microsomal Prostaglandin E2 Synthase-2; Prostaglandin E2

Results 1-6 (6)