Complete synchronization in coupled Type-I neurons

For a system of type-I neurons bidirectionally coupled through a nonlinear feedback mechanism, we discuss the issue of noise-induced complete synchronization (CS). For the inputs to the neurons, we point out that the rate of change of instantaneous frequency with the instantaneous phase of the stochastic inputs to each neuron matches exactly with that for the other in the event of CS of their outputs. Our observation can be exploited in practical situations to produce completely synchronized outputs in artificial devices. For excitatory-excitatory synaptic coupling, a functional dependence for the synchronization error on coupling and noise strengths is obtained. Finally we report an observation of noise-induced CS between non-identical neurons coupled bidirectionally through random non-zero couplings in an all-to- all way in a large neuronal ensemble.Comment: 24 pages, 9 figure

Efficient Dynamic Importance Sampling of Rare Events in One Dimension

Exploiting stochastic path integral theory, we obtain \emph{by simulation} substantial gains in efficiency for the computation of reaction rates in one-dimensional, bistable, overdamped stochastic systems. Using a well-defined measure of efficiency, we compare implementations of ``Dynamic Importance Sampling'' (DIMS) methods to unbiased simulation. The best DIMS algorithms are shown to increase efficiency by factors of approximately 20 for a $5 k_B T$ barrier height and 300 for $9 k_B T$, compared to unbiased simulation. The gains result from close emulation of natural (unbiased), instanton-like crossing events with artificially decreased waiting times between events that are corrected for in rate calculations. The artificial crossing events are generated using the closed-form solution to the most probable crossing event described by the Onsager-Machlup action. While the best biasing methods require the second derivative of the potential (resulting from the ``Jacobian'' term in the action, which is discussed at length), algorithms employing solely the first derivative do nearly as well. We discuss the importance of one-dimensional models to larger systems, and suggest extensions to higher-dimensional systems.Comment: version to be published in Phys. Rev.

A design space for conversational in-vehicle information systems

In this paper we chart a design space for conversational in-vehicle information systems (IVIS). Our work is motivated by the proliferation of speech interfaces in our everyday life, which have already found their way into consumer electronics and will most likely become pervasive in future cars.\u3cbr/\u3e\u3cbr/\u3eOur design space is based on expert interviews as well as a comprehensive literature review. We present five core dimensions - assistant, position, dialog design, system capabilities, and driver state - and show in an initial study how these dimensions affect the design of a prototypical IVIS.\u3cbr/\u3e\u3cbr/\u3eDesign spaces have paved the way for much of the work done in HCI including but not limited to areas such as input and pointing devices, smart phones, displays, and automotive UIs. In a similar way, we expect our design space to aid practitioners in designing future IVIS but also researchers as they explore this young area of research.\u3cbr/\u3