Room temperature mid-infrared fiber lasing beyond 5 µm in chalcogenide glass small-core step index fiber

We report mid-infrared fiber lasing for the first time above 5 µm in a room temperature, Ce 3+ doped, chalcogenide glass, step index fiber using in-band pumping with a 4.15 µm quantum cascade laser. The lasing fiber is 64 mm long, with a calculated numerical aperture of 0.48 at the lasing wavelengths, the core glass is Ge15As21Ga1Se63 atomic % (at. %), doped with 500 parts-per-million-by-weight Ce, with 9 µm core diameter, the cladding glass is Ge21Sb10Se69 at. % with 190 µm outer diameter. As pump power increases continuous wave lasing corresponding to the 2 F7/2→ 2 F5/2 transition in the Ce 3+ ion occurs at: 5.14 µm, 5.17 µm and 5.28 µm. The MIR (mid-infrared) region (defined as 3-50 µm in BS-ISO-20473:2007) enables direct molecular sensing of high selectivity/specificity. MIR fiber lasers offer excellent beam quality of bright, spatially, and temporally coherent light, routable in MIR fiber-optics for applications like narrow-band sensing, new medical laser wavelengths and pulsed-seeding of MIR-supercontinua for MIR broad-band sensing [1]. The longest wavelength room temperature CW (continuous wave) fiber lasing to date is 3.92 µm in Ho 3+-doped fluoro-indate glass fiber [2], enabled by the lower phonon energy [3] (509 cm-1) fluoro-indate glass host compared to prior fluoro-zirconate glass hosts. However, 509 cm-1 is still too high a phonon energy for laser operation > 4 µm [4]; chalcogenide glass hosts, with phonon energies down to 200 cm-1 , are prime candidates [5] for achieving this goal. Selenide-chalcogenide glasses combine sufficiently low phonon energy with good glass stability. Covalent chalcogenide glasses exhibit large linear refractive indices, so large absorption/emission cross-sections of doped-in lanthanide-ions, promising short, active devices. Chalcogenide glasses are based on sulfur 'S', selenium 'Se' and tellurium 'Te'; adding Groups 14/15 elements increases chemical /mechanical robustness. Chalcogenide fibers are weaker than silica fibers, exhibiting a Young's (elastic) modulus of ~1/5x silica [6]; and a Vickers' Hardness of ~2 GPa [7] (cf. window-glass: 5.5 GPa). Chalcogenide glasses/fiber are exceptionally stable in liquid water/water-vapor at ambient temperature, unlike fluoride glasses [8], and not oxidized in air below the glass transition temperature, beyond a protective oxide nanolayer [9] analogous to ambient silicon-oxidation [10]. Plastic-coated/uncoated chalcogenide fibers of >2 years old, stored under ambient conditions, retained respectable Ultimate Fracture Stress median: ~80 MPa [11]. Coated/uncoated fibers can maintain optical transmission for > 7 years. High optical damage thresholds are reported [12]. MIR-PL (photoluminescence) emission of lanthanide ions in selenide glasses occurs across 3-10 µm [13] wavelengths. Calculated non-radiative transition rates are orders of magnitude lower than fluoride glasses [14]. We reported first step index Pr 3+-doped chalcogenide fiber MIR-PL emission, and long millisecond MIR-PL lifetime equivalent to bulk-glass, showing fiber-processing had not compromised the lanthanide local-environment [15]. With Churbanov and Shiryaev [16] we demonstrated record low optical loss GeAsSe fiber (i.e. host-glass here). Lately, we announced gain in Pr 3+-doped selenide fiber [17]. Recently, Tb 3+ and Pr 3+ doped chalcogenide bulk glass lasers have been reported [18, 19]. Here, we report MIR fiber lasing > 5 µm in a step index selenide-chalcogenide fiber. The step index fiber (9 µm diameter core, 190 µm OD (outside diameter)) comprised core glass: 500-ppmw (parts-per-million-by-weight) Ce-Ge15As21Ga1Se63 at. % and cladding glass: Ge21Sb10Se69 at. %. The Ce 3+ ion dopant was selected due to its simple energy level structure which, in principle, excludes excited state absorption and cooperative up-conversion phenomena, whilst allowing efficient in-band pumping, with a small quantum defect. Thus, this choice mimics Yb 3+ doped silica glass, both reducing heating in the cavity and with potential for becoming the MIR analogue of the Yb 3+-doped silica glass fiber laser. There is a dearth of papers in the available literature on Ce 3+ ion doped glasses for MIR applications [20]. This contribution, apart from reporting MIR fiber lasing beyond 5 µm also displays results on Ce 3+ MIR-PL. To make the step index lasing fiber: arsenic 'As' (7N5, Furakawa Denshi), antimony 'Sb' (5N, Materion), selenium 'Se' (5N, Materion

Natural Changes in Brain Temperature Underlie Variations in Song Tempo during a Mating Behavior

The song of a male zebra finch is a stereotyped motor sequence whose tempo varies with social context – whether or not the song is directed at a female bird – as well as with the time of day. The neural mechanisms underlying these changes in tempo are unknown. Here we show that brain temperature recorded in freely behaving male finches exhibits a global increase in response to the presentation of a female bird. This increase strongly correlates with, and largely explains, the faster tempo of songs directed at a female compared to songs produced in social isolation. Furthermore, we find that the observed diurnal variations in song tempo are also explained by natural variations in brain temperature. Our findings suggest that brain temperature is an important variable that can influence the dynamics of activity in neural circuits, as well as the temporal features of behaviors that some of these circuits generate

Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail

Changes of synaptic connections between neurons are thought to be the physiological basis of learning. These changes can be gated by neuromodulators that encode the presence of reward. We study a family of reward-modulated synaptic learning rules for spiking neurons on a learning task in continuous space inspired by the Morris Water maze. The synaptic update rule modifies the release probability of synaptic transmission and depends on the timing of presynaptic spike arrival, postsynaptic action potentials, as well as the membrane potential of the postsynaptic neuron. The family of learning rules includes an optimal rule derived from policy gradient methods as well as reward modulated Hebbian learning. The synaptic update rule is implemented in a population of spiking neurons using a network architecture that combines feedforward input with lateral connections. Actions are represented by a population of hypothetical action cells with strong mexican-hat connectivity and are read out at theta frequency. We show that in this architecture, a standard policy gradient rule fails to solve the Morris watermaze task, whereas a variant with a Hebbian bias can learn the task within 20 trials, consistent with experiments. This result does not depend on implementation details such as the size of the neuronal populations. Our theoretical approach shows how learning new behaviors can be linked to reward-modulated plasticity at the level of single synapses and makes predictions about the voltage and spike-timing dependence of synaptic plasticity and the influence of neuromodulators such as dopamine. It is an important step towards connecting formal theories of reinforcement learning with neuronal and synaptic properties

Democratic population decisions result in robust policy-gradient learning: A parametric study with GPU simulations

High performance computing on the Graphics Processing Unit (GPU) is an emerging field driven by the promise of high computational power at a low cost. However, GPU programming is a non-trivial task and moreover architectural limitations raise the question of whether investing effort in this direction may be worthwhile. In this work, we use GPU programming to simulate a two-layer network of Integrate-and-Fire neurons with varying degrees of recurrent connectivity and investigate its ability to learn a simplified navigation task using a policy-gradient learning rule stemming from Reinforcement Learning. The purpose of this paper is twofold. First, we want to support the use of GPUs in the field of Computational Neuroscience. Second, using GPU computing power, we investigate the conditions under which the said architecture and learning rule demonstrate best performance. Our work indicates that networks featuring strong Mexican-Hat-shaped recurrent connections in the top layer, where decision making is governed by the formation of a stable activity bump in the neural population (a "non-democratic" mechanism), achieve mediocre learning results at best. In absence of recurrent connections, where all neurons "vote" independently ("democratic") for a decision via population vector readout, the task is generally learned better and more robustly. Our study would have been extremely difficult on a desktop computer without the use of GPU programming. We present the routines developed for this purpose and show that a speed improvement of 5x up to 42x is provided versus optimised Python code. The higher speed is achieved when we exploit the parallelism of the GPU in the search of learning parameters. This suggests that efficient GPU programming can significantly reduce the time needed for simulating networks of spiking neurons, particularly when multiple parameter configurations are investigated. © 2011 Richmond et al

To transduce a zebra finch: interrogating behavioral mechanisms in a model system for speech

The ability to alter neuronal gene expression, either to affect levels of endogenous molecules or to express exogenous ones, is a powerful tool for linking brain and behavior. Scientists continue to finesse genetic manipulation in mice. Yet mice do not exhibit every behavior of interest. For example, Mus musculus do not readily imitate sounds, a trait known as vocal learning and a feature of speech. In contrast, thousands of bird species exhibit this ability. The circuits and underlying molecular mechanisms appear similar between disparate avian orders and are shared with humans. An advantage of studying vocal learning birds is that the neurons dedicated to this trait are nested within the surrounding brain regions, providing anatomical targets for relating brain and behavior. In songbirds, these nuclei are known as the song control system. Molecular function can be interrogated in non-traditional model organisms by exploiting the ability of viruses to insert genetic material into neurons to drive expression of experimenter-defined genes. To date, the use of viruses in the song control system is limited. Here, we review prior successes and test additional viruses for their capacity to transduce basal ganglia song control neurons. These findings provide a roadmap for troubleshooting the use of viruses in animal champions of fascinating behaviors-nowhere better featured than at the 12th International Congress

An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning

An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD) learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards