How can realistic networks process time-varying signals?

The brain is easily able to process and categorise complex time-varying signals. For example, the two sentences "it is cold in London this time of year" and "it is hot in London this time of year" have different meanings, even though the words "hot" and "cold" appear about 3000 ms before the ends of the two sentences. A network that can perform this kind of processing must, therefore, have a long memory. In other words, the current state of the network must depend on events that happened many seconds ago. This is particularly difficult because neurons are dominated by relatively short time constants---10s to 100s of ms. Recently Jaeger and Haas 2004 (see also Jaeger 2001 ) and Maass et al. 2002, 2004 proposed that randomly connected networks could exhibit the long memories necessary for complex temporal processing. This is an attractive idea, both for its simplicity and because little fine tuning is required. However, a necessary condition for it to work is that the underlying network dynamics must be non-chaotic that is, it must exhibit negative Lyapunov exponents White et al., 2004, Bertschinger and Natschlager, 2004 . Real networks, though, tend to be chaotic Banerjee, 2001a,b , an observation that we have corroborated based on an extension of the analysis used by Bertschinger and Natschlager. Real networks also tend to be very noisy---they exhibit synaptic failures 10-90% of the time in the central nervous system Walmsley et al., 1987, Volgushev et al., 2004 . The question we ask here, then, is: given the chaotic dynamics and high noise intrinsic to biologically realistic networks, can randomly connected networks exhibit memories that are significantly longer than the time constants of their constituent neurons

Kepuasan pelanggan terhadap perkhidmatan kaunter pejabat pertanyaan Balai Polis Pengkalan Hulu, Perak

This study analyses the relationship between customer satisfaction and service quality. This study aimed to test the relationship between customer satisfaction and the factors that led to the creation of customer satisfaction. In addition, this study also aimed to determine the level of customer satisfaction with the quality of the Reception Office Police Station Enquiry in Pengkalan Hulu, Perak. Testing hypotheses conducted using questionnaires, observations and interviews. The findings of the test shows positive and significant relationship between competency- based career service delivery, this study proves the relationship between customer satisfaction and service quality. Overall, those who deal in the customer service counter Police Pengkalan Hulu, Perak very satisfied with the service provided. Dominant factor affecting the quality of service customers are dimensional effective reaction between staff and customers. Overall customer expressed satisfaction and assess the quality of the police station counter Pengkalan Hulu, Perak is in good category

What is the Most Sensitive Measure of Water Maze Probe Test Performance?

The water maze is commonly used to assay spatial cognition, or, more generally, learning and memory in experimental rodent models. In the water maze, mice or rats are trained to navigate to a platform located below the water's surface. Spatial learning is then typically assessed in a probe test, where the platform is removed from the pool and the mouse or rat is allowed to search for it. Performance in the probe test may then be evaluated using either occupancy-based (percent time in a virtual quadrant [Q] or zone [Z] centered on former platform location), error-based (mean proximity to former platform location [P]) or counting-based (platform crossings [X]) measures. While these measures differ in their popularity, whether they differ in their ability to detect group differences is not known. To address this question we compiled five separate databases, containing more than 1600 mouse probe tests. Random selection of individual trials from respective databases then allowed us to simulate experiments with varying sample and effect sizes. Using this Monte Carlo-based method, we found that the P measure consistently outperformed the Q, Z and X measures in its ability to detect group differences. This was the case regardless of sample or effect size, and using both parametric and non-parametric statistical analyses. The relative superiority of P over other commonly used measures suggests that it is the most appropriate measure to employ in both low- and high-throughput water maze screens

Correlated quantum percolation in the lowest Landau level

Our understanding of localization in the integer quantum Hall effect is informed by a combination of semi-classical models and percolation theory. Motivated by the effect of correlations on classical percolation we study numerically electron localization in the lowest Landau level in the presence of a power-law correlated disorder potential. Careful comparisons between classical and quantum dynamics suggest that the extended Harris criterion is applicable in the quantum case. This leads to a prediction of new localization quantum critical points in integer quantum Hall systems with power-law correlated disorder potentials. We demonstrate the stability of these critical points to addition of competing short-range disorder potentials, and discuss possible experimental realizations.Comment: 15 pages, 12 figure

From a mouse: systematic analysis reveals limitations of experiments testing interventions in Alzheimer's disease mouse models

The increasing prevalence of Alzheimer's disease (AD) poses a considerable socio-economic challenge. Decades of experimental research have not led to the development of effective disease modifying interventions. A deeper understanding of in vivo research might provide insights to inform future in vivo research and clinical trial design. We therefore performed a systematic review and meta-analysis of interventions tested in transgenic mouse models of AD. We searched electronically for publications testing interventions in transgenic models of AD. We extracted data for outcome, study characteristics and reported study quality and calculated summary estimates of efficacy using random effects meta-analysis. We identified 427 publications describing 357 interventions in 55 transgenic models, involving 11,118 animals in 838 experiments. Of concern, reported study quality was relatively low; fewer than one in four publications reported the blinded assessment of outcome or random allocation to group and no study reported a sample size calculation. Additionally, there were few data for any individual intervention-only 16 interventions had outcomes described in 5 or more publications. Finally, "trim and fill" analyses suggested one in seven pathological and neurobehavioural experiments remain unpublished. Given these historical weaknesses in the in vivo modelling of AD in transgenic animals and the identified risks of bias, clinical trials that are based on claims of efficacy in animals should only proceed after a detailed critical appraisal of those animal data

Development and Validation of a Sensitive Entropy-Based Measure for the Water Maze

In the water maze, mice are trained to navigate to an escape platform located below the water's surface, and spatial learning is most commonly evaluated in a probe test in which the platform is removed from the pool. While contemporary tracking software provides precise positional information of mice for the duration of the probe test, existing performance measures (e.g., percent quadrant time, platform crossings) fail to exploit fully the richness of this positional data. Using the concept of entropy (H), here we develop a new measure that considers both how focused the search is and the degree to which searching is centered on the former platform location. To evaluate how H performs compared to existing measures of water maze performance we compiled five separate databases, containing more than 1600 mouse probe tests. Random selection of individual trials from respective databases then allowed us to simulate experiments with varying sample and effect sizes. Using this Monte Carlo-based method, we found that H outperformed existing measures in its ability to detect group differences over a range of sample or effect sizes. Additionally, we validated the new measure using three models of experimentally induced hippocampal dysfunction: (1) complete hippocampal lesions, (2) genetic deletion of αCaMKII, a gene implicated in hippocampal behavioral and synaptic plasticity, and (3) a mouse model of Alzheimer's disease. Together, these data indicate that H offers greater sensitivity than existing measures, most likely because it exploits the richness of the precise positional information of the mouse throughout the probe test