Laboratory and telescope use of the NICMOS2 128 x 128 HgCdTe array

The second generation of Hubble Space Telescope (HST) instruments will include a near-infrared instrument. This choice has driven the development of near-infrared arrays to larger sizes and lower read noises. Rockwell International has delivered an array for use in the Near Infrared Camera and Multi-Object Spectrometer (NICMOS) instrument; this array has been dubbed NICMOS2. NICMOS2 is a 128x128 array of HgCdTe diodes In-bonded to a switched MOSFET readout. The readout was specifically designed for astronomical use with the HST requirement of low read noise a prime goal. These arrays use detector material which is similar to that used by Rockwell in previous arrays (e.g., HgCdTe produced on a sapphire substrate), but the NICMOS2 devices differ substantially from other 128x128 arrays produced by Rockwell in having a read noise of only 30 electrons when read out using appropriate correlated sampling. NICMOS2 has now been characterized in the laboratory, and it has been used on groundbased telescopes

The galactic center black hole as a possible retro-lens for the S2 orbiting star

Holz & Wheeler (\cite{hw}) have recently proposed that a Schwarzschild black hole may act as a retro-lens which, if illuminated by a powerful light source, deflects light ray paths to large bending angles and a series of luminous arcs (or rings in the case of aligned objects) centered on the black hole may form. Obviously, the most convenient geometry to get retro-lensing images would be that of a very bright star close to a massive black hole, say the putative $\sim 4\times 10^6$ M$_{\odot}$ black hole at the galactic center. Recent observations of the galactic center region in the $K$-band have revealed the presence of a very bright main sequence star (labelled S2) with mass $\sim 15$ M$_{\odot}$ orbiting at close distance (130-1900 AU) from Sgr A$^*$. The relatively vicinity of S2 to the central massive black hole may offer a unique laboratory to test the formation of retro-lensing images. The next generation of space-based telescopes in the $K$-band (like NGST) may have high enough limiting magnitude necessary to observe such retro-lensing images.Comment: 4 pages, 2 Postscript figures, accepted for pubblications on Astronomy and Astrophysic

Maximum likelihood decoding of neuronal inputs from an interspike interval distribution

An expression for the probability distribution of the interspike interval of a leaky integrate-and-fire (LIF) model neuron is rigorously derived, based on recent theoretical developments in the theory of stochastic processes. This enables us to find for the first time a way of developing maximum likelihood estimates (MLE) of the input information (e.g., afferent rate and variance) for an LIF neuron from a set of recorded spike trains. Dynamic inputs to pools of LIF neurons both with and without interactions are efficiently and reliably decoded by applying the MLE, even within time windows as short as 25 msec

Multiple Thresholds in a Model System of Noisy Ion Channels

Voltage-activated ion channels vary randomly between open and closed states, influenced by the membrane potential and other factors. Signal transduction is enhanced by noise in a simple ion channel model. The enhancement occurs in a finite range of signals; the range can be extended using populations of channels. The range increases more rapidly in multiple-threshold channel populations than in single-threshold populations. The diversity of ion channels may thus be present as a strategy to reduce the metabolic costs of handling a broad class of electrochemical signals.Comment: REVTeX 4, 5 pages, 4 figures; added paragrap

Dynamical Masses in Luminous Infrared Galaxies

We have studied the dynamics and masses of a sample of ten nearby luminous and ultraluminous infrared galaxies (LIRGS and ULIRGs), using 2.3 micron CO absorption line spectroscopy and near-infrared H- and Ks-band imaging. By combining velocity dispersions derived from the spectroscopy, disk scale-lengths obtained from the imaging, and a set of likely model density profiles, we calculate dynamical masses for each LIRG. For the majority of the sample, it is difficult to reconcile our mass estimates with the large amounts of gas derived from millimeter observations and from a standard conversion between CO emission and H_2 mass. Our results imply that LIRGs do not have huge amounts of molecular gas (10^10-10^11 Msolar) at their centers, and support previous indications that the standard conversion of CO to H_2 probably overestimates the gas masses and cannot be used in these environments. This in turn suggests much more modest levels of extinction in the near-infrared for LIRGs than previously predicted (A_V~10-20 versus A_V~100-1000). The lower gas mass estimates indicated by our observations imply that the star formation efficiency in these systems is very high and is triggered by cloud-cloud collisions, shocks, and winds rather than by gravitational instabilities in circumnuclear gas disks.Comment: 14 pages, 2 figures, accepted to Ap

High sensitivity operation of discrete solid state detectors at 4 K

Techniques are described to allow operation of discrete, solid state detectors at 4 K with optimized JFET amplifiers. Three detector types cover the 0.6 to 4 mm spectral range with NEP approximately equal to 10 to the 16th power Hz (-1/2) for two of the types and potential improvement to this performance for the third. Lower NEP's are anticipated at longer infrared wavelengths

Continuous theory of active matter systems with metric-free interactions

We derive a hydrodynamic description of metric-free active matter: starting from self-propelled particles aligning with neighbors defined by "topological" rules, not metric zones, -a situation advocated recently to be relevant for bird flocks, fish schools, and crowds- we use a kinetic approach to obtain well-controlled nonlinear field equations. We show that the density-independent collision rate per particle characteristic of topological interactions suppresses the linear instability of the homogeneous ordered phase and the nonlinear density segregation generically present near threshold in metric models, in agreement with microscopic simulations.Comment: Submitted to Physical Review Letter

The Ising Model for Neural Data: Model Quality and Approximate Methods for Extracting Functional Connectivity

We study pairwise Ising models for describing the statistics of multi-neuron spike trains, using data from a simulated cortical network. We explore efficient ways of finding the optimal couplings in these models and examine their statistical properties. To do this, we extract the optimal couplings for subsets of size up to 200 neurons, essentially exactly, using Boltzmann learning. We then study the quality of several approximate methods for finding the couplings by comparing their results with those found from Boltzmann learning. Two of these methods- inversion of the TAP equations and an approximation proposed by Sessak and Monasson- are remarkably accurate. Using these approximations for larger subsets of neurons, we find that extracting couplings using data from a subset smaller than the full network tends systematically to overestimate their magnitude. This effect is described qualitatively by infinite-range spin glass theory for the normal phase. We also show that a globally-correlated input to the neurons in the network lead to a small increase in the average coupling. However, the pair-to-pair variation of the couplings is much larger than this and reflects intrinsic properties of the network. Finally, we study the quality of these models by comparing their entropies with that of the data. We find that they perform well for small subsets of the neurons in the network, but the fit quality starts to deteriorate as the subset size grows, signalling the need to include higher order correlations to describe the statistics of large networks.Comment: 12 pages, 10 figure

Phase locking below rate threshold in noisy model neurons

The property of a neuron to phase-lock to an oscillatory stimulus before adapting its spike rate to the stimulus frequency plays an important role for the auditory system. We investigate under which conditions neurons exhibit this phase locking below rate threshold. To this end, we simulate neurons employing the widely used leaky integrate-and-fire (LIF) model. Tuning parameters, we can arrange either an irregular spontaneous or a tonic spiking mode. When the neuron is stimulated in both modes, a significant rise of vector strength prior to a noticeable change of the spike rate can be observed. Combining analytic reasoning with numerical simulations, we trace this observation back to a modulation of interspike intervals, which itself requires spikes to be only loosely coupled. We test the limits of this conception by simulating an LIF model with threshold fatigue, which generates pronounced anticorrelations between subsequent interspike intervals. In addition we evaluate the LIF response for harmonic stimuli of various frequencies and discuss the extension to more complex stimuli. It seems that phase locking below rate threshold occurs generically for all zero mean stimuli. Finally, we discuss our findings in the context of stimulus detection

Speed of synchronization in complex networks of neural oscillators Analytic results based on Random Matrix Theory

We analyze the dynamics of networks of spiking neural oscillators. First, we present an exact linear stability theory of the synchronous state for networks of arbitrary connectivity. For general neuron rise functions, stability is determined by multiple operators, for which standard analysis is not suitable. We describe a general non-standard solution to the multi-operator problem. Subsequently, we derive a class of rise functions for which all stability operators become degenerate and standard eigenvalue analysis becomes a suitable tool. Interestingly, this class is found to consist of networks of leaky integrate and fire neurons. For random networks of inhibitory integrate-and-fire neurons, we then develop an analytical approach, based on the theory of random matrices, to precisely determine the eigenvalue distribution. This yields the asymptotic relaxation time for perturbations to the synchronous state which provides the characteristic time scale on which neurons can coordinate their activity in such networks. For networks with finite in-degree, i.e. finite number of presynaptic inputs per neuron, we find a speed limit to coordinating spiking activity: Even with arbitrarily strong interaction strengths neurons cannot synchronize faster than at a certain maximal speed determined by the typical in-degree.Comment: 17 pages, 12 figures, submitted to Chao