InP membrane based broadband regenerator for silicon-based optical interconnect applications

We demonstrate the use of a Membrane-InP-Switch(MIPS) on-silicon as a signal regenerator. A receiver sensitivity enhancement >2.5dB across the entire C-band and a tripling of Extinction Ratio(ER) for low ER signals at 1Gb/sec is demonstrated

Further Results on the Power of Generating APCol Systems

In this paper we continue our investigations in APCol systems (Automatonlike P colonies), variants of P colonies where the environment of the agents is given by a string and the functioning of the system resembles to the functioning of standard nite automaton. We rst deal with the concept of determinism in these systems and compare deterministic APCol systems with deterministic register machines. Then we focus on generating non-deterministic APCol systems with only one agent. We show that these systems are as powerful as 0-type grammars, i.e., generate any recursively enumerable language. If the APCol system is non-erasing, then any context-sensitive language can be generated by a non-deterministic APCol systems with only one agent

Fast recursive filters for simulating nonlinear dynamic systems

A fast and accurate computational scheme for simulating nonlinear dynamic systems is presented. The scheme assumes that the system can be represented by a combination of components of only two different types: first-order low-pass filters and static nonlinearities. The parameters of these filters and nonlinearities may depend on system variables, and the topology of the system may be complex, including feedback. Several examples taken from neuroscience are given: phototransduction, photopigment bleaching, and spike generation according to the Hodgkin-Huxley equations. The scheme uses two slightly different forms of autoregressive filters, with an implicit delay of zero for feedforward control and an implicit delay of half a sample distance for feedback control. On a fairly complex model of the macaque retinal horizontal cell it computes, for a given level of accuracy, 1-2 orders of magnitude faster than 4th-order Runge-Kutta. The computational scheme has minimal memory requirements, and is also suited for computation on a stream processor, such as a GPU (Graphical Processing Unit).Comment: 20 pages, 8 figures, 1 table. A comparison with 4th-order Runge-Kutta integration shows that the new algorithm is 1-2 orders of magnitude faster. The paper is in press now at Neural Computatio

Simulation of networks of spiking neurons: A review of tools and strategies

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of Computational Neuroscience, in press (2007