410715
Phased array processing for spike discrimination
We present a novel approach for the detection, discrimination, and identification of superimposed neuronal action potentials from multineuronal, multichannel extracellular nerve recordings with low signal-to-noise ratios. The approach uses phased-array processing techniques to identify the spikes from different neurons on the basis of their unique propagation velocities. We evaluated this new approach using simulated electrophysiological data, under conditions that are known to limit the effectiveness of existing spike discrimination techniques. This approach enabled discrimination of simulated spikes from multiple simultaneously active neurons with a high degree of reliability and robustness within the expected range of experimental recording conditions, even in situations where there was a high degree of spike waveform superposition on the recording channels. Moreover, the technique enables the reliable detection and discrimination of spikes recorded with signal-to-noise ratios less than 1.
35037776
Assessing the Performance of Neural Encoding Models in the Presence of Noise
An analytical method is introduced for evaluating the performance of neural encoding models. The method addresses a critical question that arises during the course of the development and validation of encoding models: is a given model near optimal in terms of its accuracy in predicting the stimulus-elicited responses of a neural system, or can the predictive accuracy be improved significantly by further model development? The evaluation method is based on a derivation of the minimum mean-square error between actual responses and modeled responses. It is formulated as a comparison between the mean-square error of the candidate model and the theoretical minimum mean-square error attainable through an optimal model for the system. However, no a priori information about the nature of the optimal model is required. The theoretically minimum error is determined solely from the coherence function between pairs of system responses to repeated presentations of the same dynamic stimulus. Thus, the performance of the candidate model is judged against the performance of an optimal model rather than against that of an arbitrarily assumed model.Using this method, we evaluated a linear model for neural encoding by mechanosensory cells in the cricket cercal system. At low stimulus intensities, the best-fit linear model of encoding by single cells was found to be nearly optimal, even though the coherence between stimulus-response pairs (a commonly used measure of system linearity) was low. In this low-stimulus-intensity regime, the mean square error of the linear model was on the order of the power of the cell responses. In contrast, at higher stimulus intensities the linear model was not an accurate representation of neural encoding, even though the stimulus-response coherence was substantially higher than in the low-intensity regime.
3221324
How common random numbers affect multinomial selection
We consider the effect of common random numbers (CRNs) among simulated systems in two different multinomial selection procedures: the classical procedure BEM (Bechhofer, Elmaghraby, and Morse) and procedure AVC (all vector comparisons). CVe examine a simple two population scenario and demonstrate analytically how CRNs affect the probability of correct selection (PCS). In addition, various levels of positive correlation are induced to approximate the effect of using CRNs with complex simulated systems. We present analytical and simulation results to show the effect correlation has on the probability of correct selection (PCS) for each solution procedure. This paper responds to Jim Wilson’s question raised at WSC’96 about how the use of CRNs affects multinomial selection procedures.
17749917
Cartesian Genetic Programming
This paper presents a new form of Genetic Programming called Cartesian Genetic Programming in which a program is represented as an indexed graph. The graph is encoded in the form of a linear string of integers. The inputs or terminal set and node outputs are numbered sequentially. The node functions are also separately numbered. The genotype is just a list of node connections and functions. The genotype is then mapped to an indexed graph that can be executed as a program. Evolutionary algorithms are used to evolve the genotype in a symbolic regression problem (sixth order polynomial) and the Santa Fe Ant Trail. The computational effort is calculated for both cases. It is suggested that hit effort is a more reliable measure of computational efficiency. A neutral search strategy that allows the fittest genotype to be replaced by another equally fit genotype (a neutral genotype) is examined and compared with non-neutral search for the Santa Fe ant problem. The neutral search proves to be much more effective.
298900
Optimal Adaptation in Web Processes with Coordination Constraints
We present methods for optimally adapting Web processes to exogenous events while preserving inter-service constraints that necessitate coordination. For example, in a supply chain process, orders placed by a manufacturer may get delayed in arriving. In response to this event, the manufacturer has the choice of either waiting out the delay or changing the supplier. Additionally, there may be compatibility constraints between the different orders, thereby introducing the problem of coordination between them if the manufacturer chooses to change the suppliers. We focus on formulating the decision making models of the managers, who must adapt to external events while satisfying the coordination constraints, using Markov decision processes. Our methods range from being centralized and globally optimal in their adaptation but not scalable, to decentralized that is suboptimal but scalable to multiple managers. We also develop a hybrid approach that improves on the performance of the decentralized approach with a minimal loss of optimality
6824501
The Undecidability of Iterated Modal Relativization
In dynamic epistemic logic and other fields, it is natural to consider relativization as an operator taking sentences to sentences. When using the ideas and methods of dynamic logic, one would like to iterate operators. This leads to iterated relativization. We are also concerned with the transitive closure operation, due to its connection to common knowledge. We show that for three fragments of the logic of iterated relativization and transitive closure, the satisfiability problems are fi1 Σ11–complete. Two of these fragments do not include transitive closure. We also show that the question of whether a sentence in these fragments has a finite (tree) model is fi0 Σ01–complete. These results go via reduction to problems concerning domino systems.
9294328
Performance Modeling of Database Recovery Protocols
The performance modeling described in this paper compares several protocols which ensure that a database can be recovered to a consistent state after a transaction failure or system crash. The contributions of the paper include a collection of simple analytic models, based on Markov processes, for these protocols and some surprising results on the relative performance of the protocols. We consider only two-stage transactions (all reads before writes) and ignore effects of serializing transactions. The most interesting performance result presented is that, for systems obeying the assumptions of this paper, the "pessimistic" policy of holding write locks to commit point is considerably less efficient than the "optimistic" policy which allows reading of uncommitted data, but risks cascading aborts. A multiversion policy introduced in [2] was also studied and found always to be nearly as good as the optimistic policy and sometimes much better.