Timing detectors with SiPM read-out for the MUSE experiment at PSI

The Muon Scattering Experiment at the Paul Scherrer Institute uses a mixed beam of electrons, muons, and pions, necessitating precise timing to identify the beam particles and reactions they cause. We describe the design and performance of three timing detectors using plastic scintillator read out with silicon photomultipliers that have been built for the experiment. The Beam Hodoscope, upstream of the scattering target, counts the beam flux and precisely times beam particles both to identify species and provide a starting time for time-of-flight measurements. The Beam Monitor, downstream of the scattering target, counts the unscattered beam flux, helps identify background in scattering events, and precisely times beam particles for time-of-flight measurements. The Beam Focus Monitor, mounted on the target ladder under the liquid hydrogen target inside the target vacuum chamber, is used in dedicated runs to sample the beam spot at three points near the target center, where the beam should be focused

State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data

Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods to neural spike data simultaneously recorded from the motor cortex of an awake monkey and demonstrate that the higher-order spike correlation organizes dynamically in relation to a behavioral demand