Interactions of glutamate and dopamine in a computational model of the striatum

A network model of simplified striatal principal neurons with mutual inhibition was used to investigate possible interactions between cortical glutamatergic and nigral dopaminergic afferents in the neostriatum. Glutamatergic and dopaminergic inputs were represented by an excitatory synaptic conductance and a slow membrane potassium conductance, respectively. Neuronal activity in the model was characterized by episodes of increased action potential firing rates of variable duration and frequency. Autocorrelation histograms constructed from the action potential activity of striatal model neurons showed that reducing peak excitatory conductance had the effect of increasing interspike intervals. On the other hand, the maximum value of the dopamine-sensitive potassium conductance was inversely related to the duration of firing episodes and the maximal firing rates. A smaller potassium conductance restored normal firing rates in the most active neurons at the expense of a larger proportion of neurons showing reduced activity. Thus, a homogeneous network with mutual inhibition can produce equally complex dynamics as have been proposed to occur in a striatal network with two neuron populations that are oppositely regulated by dopamine. Even without mutual inhibition it appears that increased dopamine concentrations could partially compensate for the effects of reduced glutamatergic input in individual neurons.     

Efficient reinforcement learning: computational theories, neuroscience and robotics

Reinforcement learning algorithms have provided some of the most influential computational theories for behavioral learning that depends on reward and penalty. After briefly reviewing supporting experimental data, this paper tackles three difficult theoretical issues that remain to be explored. First, plain reinforcement learning is much too slow to be considered a plausible brain model. Second, although the temporal-difference error has an important role both in theory and in experiments, how to compute it remains an enigma. Third, function of all brain areas, including the cerebral cortex, cerebellum, brainstem and basal ganglia, seems to necessitate a new computational framework. Computational studies that emphasize meta-parameters, hierarchy, modularity and supervised learning to resolve these issues are reviewed here, together with the related experimental data.     

Role of brain dopamine in food reward and reinforcement

The ability of food to establish and maintain response habits and conditioned preferences depends largely on the function of brain dopamine systems. While dopaminergic transmission in the nucleus accumbens appears sufficient for some forms of reward, the role of dopamine in food reward does not appear to be restricted to this region. Dopamine plays an important role in both the ability to energize feeding and to reinforce food-seeking behaviour; the role in energizing feeding is secondary to the prerequisite role in reinforcement. Dopaminergic activation is triggered by the auditory and visual as well as the tactile, olfactory, and gustatory stimuli of foods. While dopamine plays a central role in the feeding and food-seeking of normal animals, some food rewarded learning can be seen in genetically engineered dopamine-deficient mice.     

Latent learning,shortcuts and detours: a computational model

Voicu and Schmajuk (Rob. Auto. Syst. 35 (2001a) 23) described a model of spatial navigation and exploration that includes an action system capable of guiding, with the help of a cognitive system, the search for specific goals as determined by a motivation system. Whereas in the original model the cognitive map stores information about the connectivity between places in the environment, in the present version the cognitive map also stores information about the paths traversed by the agent. Computer simulations show that the network correctly describes experimental results including latent learning in a maze, detours in a maze, and shortcuts in an open field. In addition, the model generates novel predictions about detours and shortcuts in an open field.     

Computational models of reinforcement learning: the role of dopamine as a reward signal

Reinforcement learning is ubiquitous. Unlike other forms of learning, it involves the processing of fast yet content-poor feedback information to correct assumptions about the nature of a task or of a set of stimuli. This feedback information is often delivered as generic rewards or punishments, and has little to do with the stimulus features to be learned. How can such low-content feedback lead to such an efficient learning paradigm? Through a review of existing neuro-computational models of reinforcement learning, we suggest that the efficiency of this type of learning resides in the dynamic and synergistic cooperation of brain systems that use different levels of computations. The implementation of reward signals at the synaptic, cellular, network and system levels give the organism the necessary robustness, adaptability and processing speed required for evolutionary and behavioral success.     

The role of the striatum in adaptation learning: a computational model

To investigate the functional role of the striatum in visuo-motor adaptation, we extend the DIRECT-model for visuo-motor reaching movements formulated by Bullock et al.(J Cogn Neurosci 5:408–435,1993) through two parallel loops, each modeling a distinct contribution of the cortico–cerebellar–thalamo–cortical and the cortico–striato–thalamo–cortical networks to visuo-motor adaptation. Based on evidence of Robertson and Miall(Neuroreport 10(5): 1029–1034, 1999), we implement the function of the cortico–cerebellar–thalamo–cortical loop as a module that gradually adapts to small changes in sensorimotor relationships. The cortico–striato–thalamo–cortical loop on the other hand is hypothesized to act as an adaptive search element, guessing new sensorimotor-transformations and reinforcing successful guesses while punishing unsuccessful ones. In a first step, we show that the model reproduces trajectories and error curves of healthy subjects in a two dimensional center-out reaching task with rotated screen cursor visual feedback. In a second step, we disable learning processes in the cortico–striato– thalamo–cortical loop to simulate subjects with Parkinson’s disease (PD), and show that this leads to error curves typical of subjects with PD. We conclude that the results support our hypothesis, i.e., that the role of the cortico–striato–thalamo–cortical loop in visuo-motor adaptation is that of an adaptive search element.     

Role of miR-107 and its signaling pathways in diseases

Abstract

MicroRNAs exert their biologic effects by targeting specific mRNAs for degradation or translational inhibition. MicroRNA-mediated regulation is complex, potentially affecting expression of the host gene, related enzymes within the same pathway or apparently distinct targets. miR-107 is found to be implicated in the pathogenesis of some diseases. This review was performed to sum up the role of miR-107 and its signaling pathways in renal diseases.     

Models of heterogeneous dopamine signaling in an insect learning and memory center

The Drosophila mushroom body exhibits dopamine dependent synaptic plasticity that underlies the acquisition of associative memories. Recordings of dopamine neurons in this system have identified signals related to external reinforcement such as reward and punishment. However, other factors including locomotion, novelty, reward expectation, and internal state have also recently been shown to modulate dopamine neurons. This heterogeneity is at odds with typical modeling approaches in which these neurons are assumed to encode a global, scalar error signal. How is dopamine dependent plasticity coordinated in the presence of such heterogeneity? We develop a modeling approach that infers a pattern of dopamine activity sufficient to solve defined behavioral tasks, given architectural constraints informed by knowledge of mushroom body circuitry. Model dopamine neurons exhibit diverse tuning to task parameters while nonetheless producing coherent learned behaviors. Notably, reward prediction error emerges as a mode of population activity distributed across these neurons. Our results provide a mechanistic framework that accounts for the heterogeneity of dopamine activity during learning and behavior.     

Proteomic analysis of the maternal protein restriction rat model for schizophrenia: Identification of translational changes in hormonal signaling pathways and glutamate neurotransmission

Previous studies have found that some first onset schizophrenia patients show signs of impaired insulin signaling. Also, epidemiological studies have shown that periods of suboptimal nutrition including protein deficiencies during pregnancy can lead to increased incidence of metabolic conditions and psychiatric disorders in the offspring. For these reasons, we have carried out a molecular profiling analysis of blood serum and brain tissues from adult offspring produced by the maternal low protein (LP) rat model. The results showed similar changes to those seen in schizophrenia. Multiplex immunoassay profiling identified changes in the levels of insulin, adiponectin, and leptin along with alterations in inflammatory and vascular system‐related proteins such as osteopontin, macrophage colony‐stimulating factor 1, and vascular cell adhesion molecule 1. LC‐MS^E proteomic profiling showed that glutamatergic pathways were altered in frontal cortex, while signaling pathways and cytoskeletal proteins involved in hormonal secretion and synaptic remodeling were altered in the hypothalamus. Taken together, these studies indicate that the LP rat model recapitulates several pathophysiological attributes seen in schizophrenia patients. We propose that the LP model may have utility for drug discovery efforts, especially to identify compounds that modulate the metabolic and glutamatergic systems.     

Role of miR-21 and its signaling pathways in renal diseases

Abstract

miRNAs are endogenous non-coding RNAs that are ～22 nucleotides in length and can have structural, enzymatic and regulatory functions. miRNAs play important roles in the progression of renal fibrosis. miR-21, through a feed-forward loop and a downstream mediator of transforming growth factor-β (TGF-β), amplifies TGF-β signaling and promotes fibrosis. miR-21 is high on the list of non-coding, small, regulatory RNAs that promote renal fibrosis and emerges as a serum biomarker for kidney diseases, but many questions await answers. This review was performed to sum up the role of miR-21 and its signaling pathways in renal diseases.     

Multiple spike initiation zones in a neuron implicated in learning in the leech: a computational model

Sensitization of the defensive shortening reflex in the leech has been linked to a segmentally repeated tri-synaptic positive feedback loop. Serotonin from the R-cell enhances S-cell excitability, S-cell impulses cross an electrical synapse into the C-interneuron, and the C-interneuron excites the R-cell via a glutamatergic synapse. The C-interneuron has two unusual characteristics. First, impulses take longer to propagate from the S soma to the C soma than in the reverse direction. Second, impulses recorded from the electrically unexcitable C soma vary in amplitude when extracellular divalent cation concentrations are elevated, with smaller impulses failing to induce synaptic potentials in the R-cell. A compartmental, computational model was developed to test the sufficiency of multiple, independent spike initiation zones in the C-interneuron to explain these observations. The model displays asymmetric delays in impulse propagation across the S–C electrical synapse and graded impulse amplitudes in the C-interneuron in simulated high divalent cation concentrations.     

胞浆识别受体NODs及其信号通路在侵袭性肺曲霉病中的作用

【目的】研究天然免疫系统中胞浆识别受体NODs及其信号通路在小鼠侵袭性肺曲霉病(IPA)中的作用。【方法】小鼠随机分为正常对照组、正常+接种烟曲霉菌组(正常感染组)和免疫抑制+接种烟曲霉菌组(IPA组),经鼻吸入烟曲霉孢子后在不同时相点处死小鼠,无菌取肺组织分别进行病理切片,烟曲霉菌落计数,RT-PCR法、Western blot法动态检测小鼠感染烟曲霉菌过程中肺组织NOD1、NOD2、RIP2 mRNA表达,促炎细胞因子TNF-α含量的变化规律。【结果】鼻吸入烟曲霉菌后72 h时,IPA组肺组织出现严重炎症反应,并有大量的菌丝生成,同时各时相点的烟曲霉菌负荷均高于正常感染组;与正常感染组比较,IPA组NOD1、RIP2 mRNA持续低表达,而NOD2 mRNA则在感染最早期(24 h)异常高表达,而在随后的感染过程中一直处于低表达状态;正常小鼠感染烟曲霉菌后,肺组织中促炎细胞因子TNF-α在感染前期皆呈高表达,且最高表达量均出现在48 h或72 h,之后下降并恢复至正常水平。而IPA小鼠促炎症细胞因子TNF-α缓慢且低水平释放。【结论】NOD1、RIP2的表达受到长期抑制,NOD2在感染最早期的过度激活以及随后的抑制表达,引起促炎细胞因子低表达,可能导致了侵袭性肺曲霉的发生发展。     

Inhibition of Toll-like receptors TLR4 and 7 signaling pathways by SIGIRR: A computational approach

Toll-like receptors (TLRs) belong to the Toll-like receptor/interleukin-1 receptor (TLR/IL-1R) superfamily which is defined by a common cytoplasmic Toll/interleukin-1 receptor (TIR) domain. TLRs recognize pathogen-associated molecular patterns and initiate an intracellular kinase cascade to trigger an immediate defensive response. SIGIRR (single immunoglobulin interleukin-1 receptor-related molecule), another member of the TLR/IL-1R superfamily, acts as a negative regulator of MyD88-dependent TLR signaling. It attenuates the recruitment of MyD88 adaptors to the receptors with its intracellular TIR domain. Thus, SIGIRR is a highly important molecule for the therapy of autoimmune diseases caused by TLRs. So far, the structural mechanism of interactions between SIGIRR, TLRs and adaptor molecules is unclear. To develop a working hypothesis for this interaction, we constructed three-dimensional models for the TIR domains of TLR4, TLR7, MyD88 and SIGIRR based on computational modeling. Through protein–protein docking analysis, we developed models of essential complexes involved in the TLR4 and 7 signaling and the SIGIRR inhibiting processes. We suggest that SIGIRR may exert its inhibitory effect through blocking the molecular interface of TLR4, TLR7 and the MyD88 adaptor mainly via its BB-loop region.