A Neural Correlate of Predicted and Actual Reward-Value Information in Monkey Pedunculopontine Tegmental and Dorsal Raphe Nucleus during Saccade Tasks

Dopamine, acetylcholine, and serotonin, the main modulators of the central nervous system, have been proposed to play important roles in the execution of movement, control of several forms of attentional behavior, and reinforcement learning. While the response pattern of midbrain dopaminergic neurons and its specific role in reinforcement learning have been revealed, the role of the other neuromodulators remains rather elusive. Here, we review our recent studies using extracellular recording from neurons in the pedunculopontine tegmental nucleus, where many cholinergic neurons exist, and the dorsal raphe nucleus, where many serotonergic neurons exist, while monkeys performed eye movement tasks to obtain different reward values. The firing patterns of these neurons are often tonic throughout the task period, while dopaminergic neurons exhibited a phasic activity pattern to the task event. The different modulation patterns, together with the activity of dopaminergic neurons, reveal dynamic information processing between these different neuromodulator systems

Adiabatic quantum computation along quasienergies

The parametric deformations of quasienergies and eigenvectors of unitary operators are applied to the design of quantum adiabatic algorithms. The conventional, standard adiabatic quantum computation proceeds along eigenenergies of parameter-dependent Hamiltonians. By contrast, discrete adiabatic computation utilizes adiabatic passage along the quasienergies of parameter-dependent unitary operators. For example, such computation can be realized by a concatenation of parameterized quantum circuits, with an adiabatic though inevitably discrete change of the parameter. A design principle of adiabatic passage along quasienergy is recently proposed: Cheon's quasienergy and eigenspace anholonomies on unitary operators is available to realize anholonomic adiabatic algorithms [Tanaka and Miyamoto, Phys. Rev. Lett. 98, 160407 (2007)], which compose a nontrivial family of discrete adiabatic algorithms. It is straightforward to port a standard adiabatic algorithm to an anholonomic adiabatic one, except an introduction of a parameter |v>, which is available to adjust the gaps of the quasienergies to control the running time steps. In Grover's database search problem, the costs to prepare |v> for the qualitatively different, i.e., power or exponential, running time steps are shown to be qualitatively different. Curiously, in establishing the equivalence between the standard quantum computation based on the circuit model and the anholonomic adiabatic quantum computation model, it is shown that the cost for |v> to enlarge the gaps of the eigenvalue is qualitatively negligible.Comment: 11 pages, 2 figure

Expression of enhancer of zeste homolog 2 correlates with survival outcome in patients with metastatic breast cancer: exploratory study using primary and paired metastatic lesions

Evaluation of immunostaining of EZH2 and Ki67. (DOC 52 kb

Distinct Functions of the Primate Putamen Direct and Indirect Pathways in Adaptive Outcome-Based Action Selection

Cortico-basal ganglia circuits are critical regulators of reward-based decision making. Reinforcement learning models posit that action reward value is encoded by the firing activity of striatal medium spiny neurons (MSNs) and updated upon changing reinforcement contingencies by dopamine (DA) signaling to these neurons. However, it remains unclear how the anatomically distinct direct and indirect pathways through the basal ganglia are involved in updating action reward value under changing contingencies. MSNs of the direct pathway predominantly express DA D1 receptors and those of the indirect pathway predominantly D2 receptors, so we tested for distinct functions in behavioral adaptation by injecting D1 and D2 receptor antagonists into the putamen of two macaque monkeys performing a free choice task for probabilistic reward. In this task, monkeys turned a handle toward either a left or right target depending on an asymmetrically assigned probability of large reward. Reward probabilities of left and right targets changed after 30-150 trials, so the monkeys were required to learn the higher-value target choice based on action-outcome history. In the control condition, the monkeys showed stable selection of the higher-value target (that more likely to yield large reward) and kept choosing the higher-value target regardless of less frequent small reward outcomes. The monkeys also made flexible changes of selection away from the high-value target when two or three small reward outcomes occurred randomly in succession. DA D1 antagonist injection significantly increased the probability of the monkey switching to the alternate target in response to successive small reward outcomes. Conversely, D2 antagonist injection significantly decreased the switching probability. These results suggest distinct functions of D1 and D2 receptor-mediated signaling processes in action selection based on action-outcome history, with D1 receptor-mediated signaling promoting the stable choice of higher-value targets and D2 receptor-mediated signaling promoting a switch in action away from small reward outcomes. Therefore, direct and indirect pathways appear to have complementary functions in maintaining optimal goal-directed action selection and updating action value, which are dependent on D1 and D2 DA receptor signaling

Toward a multiscale modeling framework for understanding serotonergic function

Despite its importance in regulating emotion and mental wellbeing, the complex structure and function of the serotonergic system present formidable challenges toward understanding its mechanisms. In this paper, we review studies investigating the interactions between serotonergic and related brain systems and their behavior at multiple scales, with a focus on biologically-based computational modeling. We first discuss serotonergic intracellular signaling and neuronal excitability, followed by neuronal circuit and systems levels. At each level of organization, we will discuss the experimental work accompanied by related computational modeling work. We then suggest that a multiscale modeling approach that integrates the various levels of neurobiological organization could potentially transform the way we understand the complex functions associated with serotonin

Genome-wide SNP analysis reveals population structure and demographic history of the ryukyu islanders in the southern part of the Japanese archipelago.

The Ryukyu Islands are located to the southwest of the Japanese archipelago. Archaeological evidence has revealed the existence of prehistoric cultural differentiation between the northern Ryukyu islands of Amami and Okinawa, and the southern Ryukyu islands of Miyako and Yaeyama. To examine a genetic subdivision in the Ryukyu Islands, we conducted genome-wide single nucleotide polymorphism typing of inhabitants from the Okinawa Islands, the Miyako Islands, and the Yaeyama Islands. Principal component and cluster analyses revealed genetic differentiation among the island groups, especially between Okinawa and Miyako. No genetic affinity was observed between aboriginal Taiwanese and any of the Ryukyu populations. The genetic differentiation observed between the inhabitants of the Okinawa Islands and the Miyako Islands is likely to have arisen due to genetic drift rather than admixture with people from neighboring regions. Based on the observed genetic differences, the divergence time between the inhabitants of Okinawa and Miyako islands was dated to the Holocene. These findings suggest that the Pleistocene inhabitants, whose bones have been found on the southern Ryukyu Islands, did not make a major genetic contribution, if any, to the present-day inhabitants of the southern Ryukyu Islands