Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor control

It is widely accepted that the complex dynamics characteristic of recurrent neural circuits contributes in a fundamental manner to brain function. Progress has been slow in understanding and exploiting the computational power of recurrent dynamics for two main reasons: nonlinear recurrent networks often exhibit chaotic behavior and most known learning rules do not work in robust fashion in recurrent networks. Here we address both these problems by demonstrating how random recurrent networks (RRN) that initially exhibit chaotic dynamics can be tuned through a supervised learning rule to generate locally stable neural patterns of activity that are both complex and robust to noise. The outcome is a novel neural network regime that exhibits both transiently stable and chaotic trajectories. We further show that the recurrent learning rule dramatically increases the ability of RRNs to generate complex spatiotemporal motor patterns, and accounts for recent experimental data showing a decrease in neural variability in response to stimulus onset

Predicting direction detection thresholds for arbitrary translational acceleration profiles in the horizontal plane

In previous research, direction detection thresholds have been measured and successfully modeled by exposing participants to sinusoidal acceleration profiles of different durations. In this paper, we present measurements that reveal differences in thresholds depending not only on the duration of the profile, but also on the actual time course of the acceleration. The measurements are further explained by a model based on a transfer function, which is able to predict direction detection thresholds for all types of acceleration profiles. In order to quantify a participant’s ability to detect the direction of motion in the horizontal plane, a four-alternative forced-choice task was implemented. Three types of acceleration profiles (sinusoidal, trapezoidal and triangular) were tested for three different durations (1.5, 2.36 and 5.86 s). To the best of our knowledge, this is the first study which varies both quantities (profile and duration) in a systematic way within a single experiment. The lowest thresholds were found for trapezoidal profiles and the highest for triangular profiles. Simulations for frequencies lower than the ones actually measured predict a change from this behavior: Sinusoidal profiles are predicted to yield the highest thresholds at low frequencies. This qualitative prediction is only possible with a model that is able to predict thresholds for different types of acceleration profiles. Our modeling approach represents an important advancement, because it allows for a more general and accurate description of perceptual thresholds for simple and complex translational motions

Applying Mean-field Approximation to Continuous Time Markov Chains

The mean-field analysis technique is used to perform analysis of a systems with a large number of components to determine the emergent deterministic behaviour and how this behaviour modifies when its parameters are perturbed. The computer science performance modelling and analysis community has found the mean-field method useful for modelling large-scale computer and communication networks. Applying mean-field analysis from the computer science perspective requires the following major steps: (1) describing how the agents populations evolve by means of a system of differential equations, (2) finding the emergent deterministic behaviour of the system by solving such differential equations, and (3) analysing properties of this behaviour either by relying on simulation or by using logics. Depending on the system under analysis, performing these steps may become challenging. Often, modifications of the general idea are needed. In this tutorial we consider illustrating examples to discuss how the mean-field method is used in different application areas. Starting from the application of the classical technique, moving to cases where additional steps have to be used, such as systems with local communication. Finally we illustrate the application of the simulation and uid model checking analysis techniques

The evaluation of failure detection and isolation algorithms for restructurable control

Three failure detection and identification techniques were compared to determine their usefulness in detecting and isolating failures in an aircraft flight control system; excluding sensor and flight control computer failures. The algorithms considered were the detection filter, the Generalized Likelihood Ratio test and the Orthogonal Series Generalized Likelihood Ratio test. A modification to the basic detection filter is also considered which uses secondary filtering of the residuals to produce unidirectional failure signals. The algorithms were evaluated by testing their ability to detect and isolate control surface failures in a nonlinear simulation of a C-130 aircraft. It was found that failures of some aircraft controls are difficult to distinguish because they have a similar effect on the dynamics of the vehicle. Quantitative measures for evaluating the distinguishability of failures are considered. A system monitoring strategy for implementing the failure detection and identification techniques was considered. This strategy identified the mix of direct measurement of failures versus the computation of failure necessary for implementation of the technology in an aircraft system

Hardware simulation of KU-band spacecraft receiver and bit synchronizer, phase 2, volume 1

The acquisition behavior of the PN subsystem of an automatically acquiring spacecraft receiver was studied. A symbol synchronizer subsystem was constructed and integrated into the composite simulation of the receiver. The overall performance of the receiver when subjected to anomalies such as signal fades was evaluated. Potential problems associated with PN/carrier sweep interactions were investigated

Specification and simulation of queuing network models using Domain-Specific Languages

Queuing Network Models (QNMs) provide powerful notations and tools for modeling and analyzing the performance of many different kinds of systems. Although several powerful tools currently exist for solving QNMs, some of these tools define their own model representations, have been developed in platform-specific ways, and are normally difficult to extend for coping with new system properties, probability distributions or system behaviors. This paper shows how Domain Specific Languages (DSLs), when used in conjunction with Model-driven engineering techniques, provide a high-level and very flexible approach for the specification and analysis of QNMs. We build on top of an existing metamodel for QNMs (PMIF) to de ne a DSL and its associated tools (editor and simulation engine), able to provide a high-level notation for the specification of different kinds of QNMs, and easy to extend for dealing with other probability distributions or system properties, such as system reliability.Ministerio de Ciencia e Innovación TIN2011-2379

Non-Stationary Forward Flux Sampling

We present a new method, Non-Stationary Forward Flux Sampling, that allows efficient simulation of rare events in both stationary and non-stationary stochastic systems. The method uses stochastic branching and pruning to achieve uniform sampling of trajectories in phase space and time, leading to accurate estimates for time-dependent switching propensities and time-dependent phase space probability densities. The method is suitable for equilibrium or non-equilibrium systems, in or out of stationary state, including non-Markovian or externally driven systems. We demonstrate the validity of the technique by applying it to a one-dimensional barrier crossing problem that can be solved exactly, and show its usefulness by applying it to the time-dependent switching of a genetic toggle switch.Comment: 18 pages, 10 figure